| Type: | Package |
| Title: | "Risk Model Regression and Analysis with Complex Non-Linear Models" |
| Version: | 1.3.0 |
| URL: | https://ericgiunta.github.io/Colossus/, https://github.com/ericgiunta/Colossus |
| BugReports: | https://github.com/ericgiunta/Colossus/issues |
| Description: | Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards https://en.wikipedia.org/wiki/Proportional_hazards_model, Poisson https://en.wikipedia.org/wiki/Poisson_regression, and Fine-Gray competing risks https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109. |
| License: | GPL (≥ 3) |
| Imports: | Rcpp, data.table, parallel, stats, utils, rlang, callr, stringr, processx, dplyr, tibble, lubridate |
| LinkingTo: | Rcpp, RcppEigen, testthat |
| SystemRequirements: | make |
| RoxygenNote: | 7.3.2 |
| Encoding: | UTF-8 |
| Suggests: | knitr, rmarkdown, testthat, xml2, ggplot2, pandoc, spelling, survival, dtplyr |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| Language: | en-US |
| Biarch: | TRUE |
| NeedsCompilation: | yes |
| Packaged: | 2025-06-05 15:33:09 UTC; user |
| Author: | Eric Giunta  [aut,
cre],
Amir Bahadori
[ctb],
Dan Andresen [ctb],
Linda Walsh [ctb],
Benjamin French
[ctb],
Lawrence Dauer [ctb],
John Boice Jr
[ctb],
Kansas State University [cph],
NASA [fnd],
NCRP [fnd],
NRC [fnd] |
| Maintainer: | Eric Giunta <egiunta@ksu.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-05 18:50:02 UTC |
checks for duplicated column names
Description
Check_Dupe_Columns checks for duplicated columns, columns with the same values, and columns with single value. Currently not updated for multi-terms
Usage
Check_Dupe_Columns(df, cols, term_n, verbose = 0, factor_check = FALSE)
Arguments
df |
a data.table containing the columns of interest |
cols |
columns to check |
term_n |
term numbers for each element of the model |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
factor_check |
a boolean used to skip comparing columns of the form ?_? with the same initial string, which is used for factored columns |
Value
returns the usable columns
See Also
Other Data Cleaning Functions:
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 2, 1, 0, 1, 0)
df <- data.table::data.table("a" = a, "b" = b, "c" = c)
cols <- c("a", "b", "c")
term_n <- c(0, 0, 1)
unique_cols <- Check_Dupe_Columns(df, cols, term_n)
Applies time duration truncation limits to create columns for Cox model
Description
Check_Trunc creates columns to use for truncation
Usage
Check_Trunc(df, ce, verbose = 0)
Arguments
df |
a data.table containing the columns of interest |
ce |
columns to check for truncation, (t0, t1, event) |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns the updated data and time period columns
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
ce <- c("%trunc%", "Ending_Age")
val <- Check_Trunc(df, ce)
df <- val$df
ce <- val$ce
General purpose verbosity check
Description
Check_Verbose checks and assigns verbosity values
Usage
Check_Verbose(verbose)
Arguments
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns correct verbose value
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Converts a string equation to regression model inputs
Description
Convert_Model_Eq Converts a string expression of a risk model into the vectors used by different Colossus regression functions
Usage
Convert_Model_Eq(Model_Eq, df)
Arguments
Model_Eq |
String representation of a survival model. Left-hand side details the model (cox, poisson, cox_strata, poisson_strata), time columns, event, and strata when used. The right-hand side details the subterm effects. The 'Unified Equation Representation' vignette provides more details. |
df |
a data.table containing the columns of interest |
Value
returns a list of regression inputs
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 0, 0, 0, 1, 0)
d <- c(1, 2, 3, 4, 5, 6, 7)
e <- c(2, 3, 4, 5, 6, 7, 8)
table <- data.table::data.table(
"a" = a, "b" = b, "c" = c,
"d" = d, "e" = e
)
Model_Eq <- "cox(a,b, c) ~ loglinear(d, factor(e), 0) + multiplicative()"
e <- Convert_Model_Eq(Model_Eq, table)
Corrects the order of terms/formula/etc
Description
Correct_Formula_Order checks the order of formulas given and corrects any ordering issues, orders alphabetically, by term number, etc.
Usage
Correct_Formula_Order(
term_n,
tform,
keep_constant,
a_n,
names,
cons_mat = matrix(c(0)),
cons_vec = c(0),
verbose = FALSE,
model_control = list()
)
Arguments
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
names |
columns for elements of the model, used to identify data columns |
cons_mat |
Matrix containing coefficients for a system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for a system of linear constraints, formatted as vector |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
returns the corrected lists
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
term_n <- c(0, 1, 1, 0, 0)
tform <- c("loglin", "quad_slope", "lin", "lin_int", "lin_slope")
keep_constant <- c(0, 0, 0, 1, 0)
a_n <- c(1, 2, 3, 4, 5)
names <- c("a", "a", "a", "a", "a")
val <- Correct_Formula_Order(term_n, tform, keep_constant,
a_n, names,
cons_mat = matrix(c(0)),
cons_vec = c(0)
)
term_n <- val$term_n
tform <- val$tform
keep_constant <- val$keep_constant
a_n <- val$a_n
names <- val$names
Calculates the likelihood curve for a cox model directly
Description
CoxCurveSolver solves the confidence interval for a cox model, starting at the optimum point and
iteratively optimizing end-points of intervals. Intervals updated using the bisection method.
Usage
CoxCurveSolver(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null",
cens_weight = "null",
model_control = list(),
cons_mat = as.matrix(c(0)),
cons_vec = c(0)
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
cens_weight |
column containing the row weights |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for a system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for a system of linear constraints, formatted as vector |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Calculates hazard ratios for a reference vector
Description
RunCoxRegression uses user provided data, vectors specifying the model,
and options to calculate relative risk for every row in the provided data
Usage
Cox_Relative_Risk(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
model_control = list()
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
returns a list of the final results
See Also
Other Plotting Wrapper Functions:
RunCoxPlots()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(1.1, 0.1, 0.2, 0.5) # used to test at a specific point
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5,
"epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow",
"double_step" = 1
)
e <- Cox_Relative_Risk(
df, time1, time2, event, names, term_n, tform,
keep_constant, a_n, modelform, control
)
Automates creating a date difference column
Description
Date_Shift generates a new dataframe with a column containing time difference in a given unit
Usage
Date_Shift(df, dcol0, dcol1, col_name, units = "days")
Arguments
df |
a data.table containing the columns of interest |
dcol0 |
list of starting month, day, and year |
dcol1 |
list of ending month, day, and year |
col_name |
vector of new column names |
units |
time unit to use |
Value
returns the updated dataframe
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
m0 <- c(1, 1, 2, 2)
m1 <- c(2, 2, 3, 3)
d0 <- c(1, 2, 3, 4)
d1 <- c(6, 7, 8, 9)
y0 <- c(1990, 1991, 1997, 1998)
y1 <- c(2001, 2003, 2005, 2006)
df <- data.table::data.table("m0" = m0, "m1" = m1, "d0" = d0, "d1" = d1, "y0" = y0, "y1" = y1)
df <- Date_Shift(df, c("m0", "d0", "y0"), c("m1", "d1", "y1"), "date_since")
Automatically assigns missing control values
Description
Def_Control checks and assigns default values
Usage
Def_Control(control)
Arguments
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
Value
returns a filled list
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"ties" = "breslow", "double_step" = 1
)
control <- Def_Control(control)
Automatically assigns missing guessing control values
Description
Def_Control_Guess checks and assigns default values
Usage
Def_Control_Guess(guesses_control, a_n)
Arguments
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
Value
returns a filled list
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
guesses_control <- list(
"maxiter" = 10, "guesses" = 10,
"loglin_min" = -1, "loglin_max" = 1, "loglin_method" = "uniform"
)
a_n <- c(0.1, 2, 1.3)
guesses_control <- Def_Control_Guess(guesses_control, a_n)
Automatically assigns missing model control values
Description
Def_model_control checks and assigns default values
Usage
Def_model_control(control)
Arguments
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
Value
returns a filled list
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
control <- list("single" = TRUE)
control <- Def_model_control(control)
Automatically assigns geometric-mixture values and checks that a valid modelform is used
Description
Def_modelform_fix checks and assigns default values for modelform options
Usage
Def_modelform_fix(control, model_control, modelform, term_n)
Arguments
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
term_n |
term numbers for each element of the model |
Value
returns a filled list
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"ties" = "breslow", "double_step" = 1
)
control <- Def_Control(control)
model_control <- list("single" = TRUE)
model_control <- Def_model_control(model_control)
term_n <- c(0, 1, 1)
modelform <- "a"
val <- Def_modelform_fix(control, model_control, modelform, term_n)
model_control <- val$model_control
modelform <- val$modelform
uses a table, list of categories, and list of event summaries to generate person-count tables
Description
Event_Count_Gen generates event-count tables
Usage
Event_Count_Gen(table, categ, events, verbose = FALSE)
Arguments
table |
dataframe with every category/event column needed |
categ |
list with category columns and methods, methods can be either strings or lists of boundaries |
events |
list of columns to summarize, supports counts and means and renaming the summary column |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns a grouped table and a list of category boundaries used
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 0, 0, 0, 1, 0)
table <- data.table::data.table(
"a" = a,
"b" = b,
"c" = c
)
categ <- list(
"a" = "0/3/5]7",
"b" = list(
lower = c(-1, 3, 6),
upper = c(3, 6, 10),
name = c("low", "medium", "high")
)
)
event <- list(
"c" = "count AS cases",
"a" = "mean", "b" = "mean"
)
e <- Event_Count_Gen(table, categ, event, T)
uses a table, list of categories, list of summaries, list of events, and person-year information to generate person-time tables
Description
Event_Time_Gen generates event-time tables
Usage
Event_Time_Gen(table, pyr, categ, summaries, events, verbose = FALSE)
Arguments
table |
dataframe with every category/event column needed |
pyr |
list with entry and exit lists, containing day/month/year columns in the table |
categ |
list with category columns and methods, methods can be either strings or lists of boundaries, includes a time category or entry/exit are both required for the pyr list |
summaries |
list of columns to summarize, supports counts, means, and weighted means by person-year and renaming the summary column |
events |
list of events or interests, checks if events are within each time interval |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns a grouped table and a list of category boundaries used
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 0, 0, 0, 1, 0)
d <- c(1, 2, 3, 4, 5, 6, 7)
e <- c(2, 3, 4, 5, 6, 7, 8)
f <- c(
1900, 1900, 1900, 1900,
1900, 1900, 1900
)
g <- c(1, 2, 3, 4, 5, 6, 7)
h <- c(2, 3, 4, 5, 6, 7, 8)
i <- c(
1901, 1902, 1903, 1904,
1905, 1906, 1907
)
table <- data.table::data.table(
"a" = a, "b" = b, "c" = c,
"d" = d, "e" = e, "f" = f,
"g" = g, "h" = h, "i" = i
)
categ <- list(
"a" = "-1/3/5]7",
"b" = list(
lower = c(-1, 3, 6), upper = c(3, 6, 10),
name = c("low", "medium", "high")
),
"time AS time" = list(
"day" = c(1, 1, 1, 1, 1),
"month" = c(1, 1, 1, 1, 1),
"year" = c(1899, 1903, 1910)
)
)
summary <- list(
"c" = "count AS cases",
"a" = "mean",
"b" = "weighted_mean"
)
events <- list("c")
pyr <- list(
entry = list(year = "f", month = "e", day = "d"),
exit = list(year = "i", month = "h", day = "g"),
unit = "years"
)
e <- Event_Time_Gen(table, pyr, categ, summary, events, T)
Performs checks to gather a list of guesses and iterations
Description
Gather_Guesses_CPP called from within R, uses a list of options and the model definition to generate a list of parameters and iterations that do not produce errors
Usage
Gather_Guesses_CPP(
df,
dfc,
names,
term_n,
tform,
keep_constant,
a_n,
x_all,
a_n_default,
modelform,
control,
guesses_control,
model_control = list()
)
Arguments
df |
a data.table containing the columns of interest |
dfc |
vector matching subterm number to matrix column |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
x_all |
covariate matrix |
a_n_default |
center of parameter distribution guessing scope |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
returns a list of the final results
Examples
library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 0, 0, 0, 1, 0)
d <- c(3, 4, 5, 6, 7, 8, 9)
df <- data.table::data.table("a" = a, "b" = b, "c" = c, "d" = d)
time1 <- "a"
time2 <- "b"
event <- "c"
names <- c("d")
term_n <- c(0)
tform <- c("loglin")
keep_constant <- c(0)
a_n <- c(-0.1)
a_n_default <- a_n
modelform <- "M"
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = -1,
"halfmax" = 5, "epsilon" = 1e-9,
"deriv_epsilon" = 1e-9, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow",
"double_step" = 1
)
guesses_control <- list()
model_control <- list()
all_names <- unique(names(df))
dfc <- match(names, all_names)
term_tot <- max(term_n) + 1
x_all <- as.matrix(df[, all_names, with = FALSE])
control <- Def_Control(control)
guesses_control <- Def_Control_Guess(guesses_control, a_n)
model_control <- Def_model_control(model_control)
options(warn = -1)
Gather_Guesses_CPP(
df, dfc, names, term_n, tform, keep_constant,
a_n, x_all, a_n_default,
modelform, control, guesses_control
)
Calculates and returns data for time by hazard and survival to estimate censoring rate
Description
GetCensWeight uses user provided data, time/event columns, vectors specifying the model, and options generate an estimate of the censoring rate, plots, and returns the data
Usage
GetCensWeight(
df,
time1,
time2,
event0,
names,
term_n,
tform,
keep_constant,
a_n,
modelform,
control,
plot_options,
model_control = list(),
strat_col = "e"
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
plot_options |
list of parameters controlling the plot options, see RunCoxPlots() for different options |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
strat_col |
column to stratify by if needed |
Value
saves the plots in the current directory and returns a data.table of time and corresponding hazard, cumulative hazard, and survival
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 0)
df$censor <- (df$Cancer_Status == 0)
event <- "censor"
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5,
"epsilon" = 1e-6, "deriv_epsilon" = 1e-6,
"abs_max" = 1.0, "dose_abs_max" = 100.0, "verbose" = FALSE,
"ties" = "breslow", "double_step" = 1
)
plot_options <- list(
"name" = paste(tempfile(), "run_06", sep = ""), "verbose" = FALSE,
"studyID" = "studyID", "age_unit" = "years"
)
dft <- GetCensWeight(
df, time1, time2, event, names, term_n, tform,
keep_constant, a_n, modelform, control, plot_options
)
t_ref <- dft$t
surv_ref <- dft$surv
t_c <- df$t1
cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$y
Prints a regression output clearly
Description
Interpret_Output uses the list output from a regression, prints off a table of results and summarizes the score and convergence.
Usage
Interpret_Output(out_list, digits = 2)
Arguments
out_list |
list output from a regression, used to build results table and pull out convergence values |
digits |
digits used for printing results |
Value
return nothing, prints the results to console
See Also
Other Output and Information Functions:
System_Version()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4))
# used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiters" = c(5, 5, 5),
"halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE,
"ties" = "breslow", "double_step" = 1, "guesses" = 2
)
e <- RunCoxRegression_Omnibus(df, time1, time2, event,
names, term_n, tform, keep_constant,
a_n, modelform, control,
model_control = list(
"single" = FALSE,
"basic" = FALSE, "cr" = FALSE, "null" = FALSE
)
)
Interpret_Output(e)
Automates creating data for a joint competing risks analysis
Description
Joint_Multiple_Events generates input for a regression with multiple non-independent events and models
Usage
Joint_Multiple_Events(
df,
events,
name_list,
term_n_list = list(),
tform_list = list(),
keep_constant_list = list(),
a_n_list = list()
)
Arguments
df |
a data.table containing the columns of interest |
events |
vector of event column names |
name_list |
list of vectors for columns for event specific or shared model elements, required |
term_n_list |
list of vectors for term numbers for event specific or shared model elements, defaults to term 0 |
tform_list |
list of vectors for subterm types for event specific or shared model elements, defaults to loglinear |
keep_constant_list |
list of vectors for constant elements for event specific or shared model elements, defaults to free (0) |
a_n_list |
list of vectors for parameter values for event specific or shared model elements, defaults to term 0 |
Value
returns the updated dataframe and model inputs
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
a <- c(0, 0, 0, 1, 1, 1)
b <- c(1, 1, 1, 2, 2, 2)
c <- c(0, 1, 2, 2, 1, 0)
d <- c(1, 1, 0, 0, 1, 1)
e <- c(0, 1, 1, 1, 0, 0)
df <- data.table("t0" = a, "t1" = b, "e0" = c, "e1" = d, "fac" = e)
time1 <- "t0"
time2 <- "t1"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
events <- c("e0", "e1")
names_e0 <- c("fac")
names_e1 <- c("fac")
names_shared <- c("t0", "t0")
term_n_e0 <- c(0)
term_n_e1 <- c(0)
term_n_shared <- c(0, 0)
tform_e0 <- c("loglin")
tform_e1 <- c("loglin")
tform_shared <- c("quad_slope", "loglin_top")
keep_constant_e0 <- c(0)
keep_constant_e1 <- c(0)
keep_constant_shared <- c(0, 0)
a_n_e0 <- c(-0.1)
a_n_e1 <- c(0.1)
a_n_shared <- c(0.001, -0.02)
name_list <- list("shared" = names_shared, "e0" = names_e0, "e1" = names_e1)
term_n_list <- list("shared" = term_n_shared, "e0" = term_n_e0, "e1" = term_n_e1)
tform_list <- list("shared" = tform_shared, "e0" = tform_e0, "e1" = tform_e1)
keep_constant_list <- list(
"shared" = keep_constant_shared,
"e0" = keep_constant_e0, "e1" = keep_constant_e1
)
a_n_list <- list("shared" = a_n_shared, "e0" = a_n_e0, "e1" = a_n_e1)
val <- Joint_Multiple_Events(
df, events, name_list, term_n_list,
tform_list, keep_constant_list, a_n_list
)
Defines the likelihood ratio test
Description
Likelihood_Ratio_Test uses two models and calculates the ratio
Usage
Likelihood_Ratio_Test(alternative_model, null_model)
Arguments
alternative_model |
the new model of interest in list form, output from a Poisson regression |
null_model |
a model to compare against, in list form |
Value
returns the score statistic
Examples
library(data.table)
# In an actual example, one would run two seperate RunCoxRegression regressions,
# assigning the results to e0 and e1
e0 <- list("name" = "First Model", "LogLik" = -120)
e1 <- list("name" = "New Model", "LogLik" = -100)
score <- Likelihood_Ratio_Test(e1, e0)
Calculates Full Parameter list for Special Dose Formula
Description
Linked_Dose_Formula Calculates all parameters for linear-quadratic and linear-exponential linked formulas
Usage
Linked_Dose_Formula(tforms, paras, verbose = 0)
Arguments
tforms |
list of formula types |
paras |
list of formula parameters |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns list of full parameters
Examples
library(data.table)
tforms <- list("cov_0" = "quad", "cov_1" = "exp")
paras <- list("cov_0" = c(1, 3.45), "cov_1" = c(1.2, 4.5, 0.1))
full_paras <- Linked_Dose_Formula(tforms, paras)
Calculates The Additional Parameter For a linear-exponential formula with known maximum
Description
Linked_Lin_Exp_Para Calculates what the additional parameter would be for a desired maximum
Usage
Linked_Lin_Exp_Para(y, a0, a1_goal, verbose = 0)
Arguments
y |
point formula switch |
a0 |
linear slope |
a1_goal |
exponential maximum desired |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns parameter used by Colossus
Examples
library(data.table)
y <- 7.6
a0 <- 1.2
a1_goal <- 15
full_paras <- Linked_Lin_Exp_Para(y, a0, a1_goal)
Checks the OMP flag
Description
OMP_Check Called directly from R, checks the omp flag and returns if omp is enabled
Usage
OMP_Check()
Value
boolean: True for OMP allowed
Calculates the likelihood curve for a poisson model directly
Description
PoissonCurveSolver solves the confidence interval for a poisson model, starting at the optimum point and
iteratively optimizing each point to using the bisection method
Usage
PoissonCurveSolver(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null",
model_control = list(),
cons_mat = as.matrix(c(0)),
cons_vec = c(0)
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for a system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for a system of linear constraints, formatted as vector |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Checks how R was compiled
Description
Rcomp_version Checks how R was compiled, part of configuration script
Usage
Rcomp_version()
Value
returns a string representation of gcc, clang, or R CMD config CC output
Checks default R c++ compiler
Description
Rcpp_version checks ~/.R/Makevars script for default compilers set, part of configuration script
Usage
Rcpp_version()
Value
returns a string representation of gcc, clang, or head ~/.R/Makevars
Automatically assigns missing values in listed columns
Description
Replace_Missing checks each column and fills in NA values
Usage
Replace_Missing(df, name_list, msv, verbose = FALSE)
Arguments
df |
a data.table containing the columns of interest |
name_list |
vector of string column names to check |
msv |
value to replace na with, same used for every column used |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns a filled datatable
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, NA, 47, 36, NA, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0)
)
df <- Replace_Missing(df, c("Starting_Age", "Ending_Age"), 70)
Performs Matched Case-Control Conditional Logistic Regression
Description
RunCaseControlRegression_Omnibus uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence
and starting positions. Has additional options for starting with several
initial guesses, using stratification and/or matching by time at risk,
and calculation without derivatives
Usage
RunCaseControlRegression_Omnibus(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null",
cens_weight = "null",
model_control = list(),
cons_mat = as.matrix(c(0)),
cons_vec = c(0)
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
cens_weight |
column containing the row weights |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for a system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for a system of linear constraints, formatted as vector |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4))
# used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiters" = c(5, 5, 5),
"halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE,
"ties" = "breslow", "double_step" = 1, "guesses" = 2
)
e <- RunCaseControlRegression_Omnibus(df, time1, time2, event,
names, term_n, tform, keep_constant,
a_n, modelform, control,
model_control = list(
"stata" = FALSE,
"time_risk" = FALSE
)
)
Performs basic Cox Proportional Hazards regression with the null model
Description
RunCoxRegression uses user provided data and time/event columns
to calculate the log-likelihood with constant hazard ratio
Usage
RunCoxNull(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
control = list()
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
control <- list(
"ncores" = 2, "verbose" = FALSE, "ties" = "breslow",
"double_step" = 1
)
e <- RunCoxNull(df, time1, time2, event, control)
Performs Cox Proportional Hazard model plots
Description
RunCoxPlots uses user provided data, time/event columns,
vectors specifying the model, and options to choose and save plots
Usage
RunCoxPlots(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
plot_options = list(),
model_control = list()
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
plot_options |
list of parameters controlling the plot options, see RunCoxPlots() for different options |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
saves the plots in the current directory and returns the data used for plots
See Also
Other Plotting Wrapper Functions:
Cox_Relative_Risk()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(-0.1, 0.5, 1.1, -0.3)
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = -1, "halfmax" = 5,
"epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE, "ties" = "breslow", "double_step" = 1
)
# setting maxiter below 0 forces the function to calculate the score
# and return
plot_options <- list(
"type" = c("surv", paste(tempfile(),
"run",
sep = ""
)), "studyid" = "UserID",
"verbose" = FALSE
)
RunCoxPlots(
df, time1, time2, event, names, term_n, tform, keep_constant,
a_n, modelform, control, plot_options
)
Performs basic Cox Proportional Hazards regression without special options
Description
RunCoxRegression uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence
and starting position
Usage
RunCoxRegression(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list()
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5,
"epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE, "ties" = "breslow", "double_step" = 1
)
e <- RunCoxRegression(
df, time1, time2, event, names, term_n, tform,
keep_constant, a_n, modelform, control
)
Performs basic Cox Proportional Hazards regression with a multiplicative log-linear model
Description
RunCoxRegression_Basic uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions
Usage
RunCoxRegression_Basic(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
keep_constant = c(0),
a_n = c(0),
control = list()
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5,
"epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE,
"ties" = "breslow", "double_step" = 1
)
e <- RunCoxRegression_Basic(
df, time1, time2, event, names, keep_constant,
a_n, control
)
Performs basic Cox Proportional Hazards regression with competing risks
Description
RunCoxRegression_CR uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence, starting positions, and censoring adjustment
Usage
RunCoxRegression_CR(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
cens_weight = "null"
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
cens_weight |
column containing the row weights |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 2, 1, 2, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE,
"ties" = "breslow", "double_step" = 1
)
# weights the probability that a row would continue to extend without censoring,
# for risk group calculation
df$cens_weight <- c(0.83, 0.37, 0.26, 0.34, 0.55, 0.23, 0.27)
# censoring weight is generated by the survival library finegray function, or by hand.
# The ratio of weight at event end point to weight at row endpoint is used.
e <- RunCoxRegression_CR(
df, time1, time2, event, names, term_n, tform,
keep_constant, a_n, modelform, control, "cens_weight"
)
Performs basic Cox Proportional Hazards regression, Generates multiple starting guesses on c++ side
Description
RunCoxRegression_Guesses_CPP uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions. Has additional options to starting with several initial guesses
Usage
RunCoxRegression_Guesses_CPP(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
guesses_control = list(),
strat_col = "null",
model_control = list(),
cens_weight = "null"
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cens_weight |
column containing the row weights |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow",
"double_step" = 1
)
guesses_control <- list(
"maxiter" = 10, "guesses" = 10,
"lin_min" = 0.001, "lin_max" = 1,
"loglin_min" = -1, "loglin_max" = 1,
"lin_method" = "uniform",
"loglin_method" = "uniform", strata = FALSE
)
strat_col <- "e"
options(warn = -1)
e <- RunCoxRegression_Guesses_CPP(
df, time1, time2, event, names, term_n,
tform, keep_constant, a_n, modelform,
control, guesses_control, strat_col
)
Performs Cox Proportional Hazards regression using the omnibus function
Description
RunCoxRegression_Omnibus uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence
and starting positions. Has additional options for starting with several
initial guesses, using stratification, multiplicative loglinear 1-term,
competing risks, and calculation without derivatives
Usage
RunCoxRegression_Omnibus(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null",
cens_weight = "null",
model_control = list(),
cons_mat = as.matrix(c(0)),
cons_vec = c(0)
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
cens_weight |
column containing the row weights |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for a system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for a system of linear constraints, formatted as vector |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4))
# used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiters" = c(5, 5, 5),
"halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE,
"ties" = "breslow", "double_step" = 1, "guesses" = 2
)
e <- RunCoxRegression_Omnibus(df, time1, time2, event,
names, term_n, tform, keep_constant,
a_n, modelform, control,
model_control = list(
"single" = FALSE,
"basic" = FALSE, "cr" = FALSE, "null" = FALSE
)
)
Performs Cox Proportional Hazards regression using the omnibus function with multiple column realizations
Description
RunCoxRegression_Omnibus_Multidose uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence
and starting positions. Used for 2DMC column uncertainty methods.
Returns optimized parameters, log-likelihood, and standard deviation for each realization.
Has additional options for using stratification,
multiplicative loglinear 1-term,
competing risks, and calculation without derivatives
Usage
RunCoxRegression_Omnibus_Multidose(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
realization_columns = matrix(c("temp00", "temp01", "temp10", "temp11"), nrow = 2),
realization_index = c("temp0", "temp1"),
control = list(),
strat_col = "null",
cens_weight = "null",
model_control = list(),
cons_mat = as.matrix(c(0)),
cons_vec = c(0)
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
realization_columns |
used for multi-realization regressions. Matrix of column names with rows for each column with realizations, columns for each realization |
realization_index |
used for multi-realization regressions. Vector of column names, one for each column with realizations. Each name should be used in the "names" variable in the equation definition |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
cens_weight |
column containing the row weights |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for a system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for a system of linear constraints, formatted as vector |
Value
returns a list of the final results for each realization
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Single(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"t0" = c(18, 20, 18, 19, 21, 20, 18),
"t1" = c(30, 45, 57, 47, 36, 60, 55),
"lung" = c(0, 0, 1, 0, 1, 0, 0),
"dose" = c(0, 1, 1, 0, 1, 0, 1)
)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
df$rand0 <- floor(runif(nrow(df), min = 0, max = 5))
df$rand1 <- floor(runif(nrow(df), min = 0, max = 5))
df$rand2 <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
names <- c("dose", "rand")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
realization_columns <- matrix(c("rand0", "rand1", "rand2"), nrow = 1)
realization_index <- c("rand")
keep_constant <- c(1, 0)
a_n <- c(0, 0)
modelform <- "M"
cens_weight <- c(0)
event <- "lung"
a_n <- c(-0.1, -0.1)
keep_constant <- c(0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 1,
"halfmax" = 2, "epsilon" = 1e-6,
"deriv_epsilon" = 1e-6, "abs_max" = 1.0,
"dose_abs_max" = 100.0,
"verbose" = 0, "ties" = "breslow", "double_step" = 1
)
e <- RunCoxRegression_Omnibus_Multidose(df, time1, time2, event,
names,
term_n = term_n, tform = tform,
keep_constant = keep_constant, a_n = a_n,
modelform = modelform,
realization_columns = realization_columns,
realization_index = realization_index,
control = control, strat_col = "fac",
model_control = list(), cens_weight = "null"
)
Performs basic Cox Proportional Hazards calculation with no derivative
Description
RunCoxRegression_Single uses user provided data, time/event columns, vectors specifying the model, and options and returns the log-likelihood
Usage
RunCoxRegression_Single(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list()
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Strata(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "verbose" = FALSE,
"ties" = "breslow", "double_step" = 1
)
e <- RunCoxRegression_Single(
df, time1, time2, event, names, term_n, tform,
keep_constant, a_n, modelform, control
)
Performs basic Cox Proportional Hazards regression with strata effect
Description
RunCoxRegression_Strata uses user provided data,
time/event columns, vectors specifying the model, and options to control
the convergence and starting positions
Usage
RunCoxRegression_Strata(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null"
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 0, 0, 1, 0, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5,
"epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE, "ties" = "breslow", "double_step" = 1
)
strat_col <- "e"
e <- RunCoxRegression_Strata(
df, time1, time2, event, names, term_n,
tform, keep_constant, a_n, modelform,
control, strat_col
)
Performs basic cox regression, with multiple guesses, starts with solving for a single term
Description
RunCoxRegression_Tier_Guesses uses user provided data, time/event
columns, vectors specifying the model, and options to control the
convergence and starting positions, with additional guesses
Usage
RunCoxRegression_Tier_Guesses(
df,
time1 = "%trunc%",
time2 = "%trunc%",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
guesses_control = list(),
strat_col = "null",
model_control = list(),
cens_weight = "null"
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cens_weight |
column containing the row weights |
Value
returns a list of the final results
See Also
Other Cox Wrapper Functions:
CoxCurveSolver(),
RunCaseControlRegression_Omnibus(),
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Strata()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 0, 0, 1, 0, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5,
"epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE, "ties" = "breslow", "double_step" = 1
)
guesses_control <- list(
"iterations" = 10, "guesses" = 10, "lin_min" = 0.001,
"lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform",
"loglin_method" = "uniform", strata = TRUE, term_initial = c(0, 1)
)
strat_col <- "e"
options(warn = -1)
e <- RunCoxRegression_Tier_Guesses(
df, time1, time2, event, names,
term_n, tform, keep_constant,
a_n, modelform,
control, guesses_control,
strat_col
)
Predicts how many events are due to baseline vs excess
Description
RunPoissonEventAssignment uses user provided data, person-year/event columns, vectors specifying the model, and options to calculate background and excess events
Usage
RunPoissonEventAssignment(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null",
model_control = list()
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
df$pyr <- df$Ending_Age - df$Starting_Age
pyr <- "pyr"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1
)
e <- RunPoissonEventAssignment(
df, pyr, event, names, term_n,
tform, keep_constant,
a_n, modelform, control
)
Predicts how many events are due to baseline vs excess at the confidence bounds of a single parameter
Description
RunPoissonEventAssignment_bound uses user provided data, the results of a poisson regression, and options to calculate background and excess events
Usage
RunPoissonEventAssignment_bound(
df,
pyr0 = "pyr",
event0 = "event",
alternative_model = list(),
keep_constant = c(0),
modelform = "M",
check_num = 1,
z = 2,
control = list(),
strat_col = "null",
model_control = list()
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
alternative_model |
the new model of interest in list form, output from a Poisson regression |
keep_constant |
binary values to denote which parameters to change |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
check_num |
the parameter number to check at the bounds of, indexed from 1 using the order returned by Colossus |
z |
Z score to use for confidence interval |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
pyr <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow",
"double_step" = 1
)
guesses_control <- list(
"maxiter" = 10, "guesses" = 10, "lin_min" = 0.001,
"lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform",
"loglin_method" = "uniform", strata = FALSE
)
strat_col <- "e"
e0 <- RunPoissonRegression_Omnibus(
df, pyr, event, names, term_n, tform,
keep_constant,
a_n, modelform,
control, strat_col
)
e <- RunPoissonEventAssignment_bound(
df, pyr, event, e0, keep_constant,
modelform, 4, 2, control
)
Performs basic poisson regression
Description
RunPoissonRegression uses user provided data, person-year/event columns, vectors specifying the model, and options to control the convergence and starting positions with no special options
Usage
RunPoissonRegression(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list()
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
df$pyr <- df$Ending_Age - df$Starting_Age
pyr <- "pyr"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1
)
e <- RunPoissonRegression(
df, pyr, event, names, term_n, tform,
keep_constant,
a_n, modelform, control
)
Performs basic Poisson regression, generates multiple starting guesses on c++ side
Description
RunPoissonRegression_Guesses_CPP uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions. Has additional options to starting with several initial guesses
Usage
RunPoissonRegression_Guesses_CPP(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
guesses_control = list(),
strat_col = "null",
model_control = list()
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
pyr <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow",
"double_step" = 1
)
guesses_control <- list(
"maxiter" = 10, "guesses" = 10,
"lin_min" = 0.001, "lin_max" = 1,
"loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform",
"loglin_method" = "uniform", strata = FALSE
)
strat_col <- "e"
options(warn = -1)
e <- RunPoissonRegression_Guesses_CPP(
df, pyr, event, names, term_n,
tform, keep_constant, a_n, modelform,
control, guesses_control, strat_col
)
Performs joint Poisson regression using the omnibus function
Description
RunPoissonRegression_Joint_Omnibus uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence and starting positions.
Has additional options to starting with several initial guesses, uses joint competing risks equation
Usage
RunPoissonRegression_Joint_Omnibus(
df,
pyr0,
events,
name_list,
term_n_list = list(),
tform_list = list(),
keep_constant_list = list(),
a_n_list = list(),
modelform = "M",
control = list(),
strat_col = "null",
model_control = list(),
cons_mat = as.matrix(c(0)),
cons_vec = c(0)
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
events |
vector of event column names |
name_list |
list of vectors for columns for event specific or shared model elements, required |
term_n_list |
list of vectors for term numbers for event specific or shared model elements, defaults to term 0 |
tform_list |
list of vectors for subterm types for event specific or shared model elements, defaults to loglinear |
keep_constant_list |
list of vectors for constant elements for event specific or shared model elements, defaults to free (0) |
a_n_list |
list of vectors for parameter values for event specific or shared model elements, defaults to term 0 |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for a system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for a system of linear constraints, formatted as vector |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
a <- c(0, 0, 0, 1, 1, 1)
b <- c(1, 1, 1, 2, 2, 2)
c <- c(0, 1, 2, 2, 1, 0)
d <- c(1, 1, 0, 0, 1, 1)
e <- c(0, 1, 1, 1, 0, 0)
f <- c(0, 1, 0, 0, 1, 1)
df <- data.table("t0" = a, "t1" = b, "e0" = c, "e1" = d, "fac" = e)
time1 <- "t0"
time2 <- "t1"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
events <- c("e0", "e1")
names_e0 <- c("fac")
names_e1 <- c("fac")
names_shared <- c("t0", "t0")
term_n_e0 <- c(0)
term_n_e1 <- c(0)
term_n_shared <- c(0, 0)
tform_e0 <- c("loglin")
tform_e1 <- c("loglin")
tform_shared <- c("quad_slope", "loglin_top")
keep_constant_e0 <- c(0)
keep_constant_e1 <- c(0)
keep_constant_shared <- c(0, 0)
a_n_e0 <- c(-0.1)
a_n_e1 <- c(0.1)
a_n_shared <- c(0.001, -0.02)
name_list <- list("shared" = names_shared, "e0" = names_e0, "e1" = names_e1)
term_n_list <- list("shared" = term_n_shared, "e0" = term_n_e0, "e1" = term_n_e1)
tform_list <- list("shared" = tform_shared, "e0" = tform_e0, "e1" = tform_e1)
keep_constant_list <- list(
"shared" = keep_constant_shared,
"e0" = keep_constant_e0, "e1" = keep_constant_e1
)
a_n_list <- list("shared" = a_n_shared, "e0" = a_n_e0, "e1" = a_n_e1)
modelform <- "M"
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE,
"ties" = "breslow", "double_step" = 1
)
guesses_control <- list(
"maxiter" = 10, "guesses" = 10,
"lin_min" = 0.001, "lin_max" = 1,
"loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform",
"loglin_method" = "uniform", strata = FALSE
)
strat_col <- "f"
e <- RunPoissonRegression_Joint_Omnibus(
df, pyr, events, name_list,
term_n_list,
tform_list, keep_constant_list,
a_n_list,
modelform,
control, strat_col
)
Performs basic Poisson regression using the omnibus function
Description
RunPoissonRegression_Omnibus uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence and starting positions.
Has additional options to starting with several initial guesses
Usage
RunPoissonRegression_Omnibus(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null",
model_control = list(),
cons_mat = as.matrix(c(0)),
cons_vec = c(0)
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for a system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for a system of linear constraints, formatted as vector |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
pyr <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow",
"double_step" = 1
)
guesses_control <- list(
"maxiter" = 10, "guesses" = 10, "lin_min" = 0.001,
"lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform",
"loglin_method" = "uniform", strata = FALSE
)
strat_col <- "e"
e <- RunPoissonRegression_Omnibus(
df, pyr, event, names, term_n,
tform, keep_constant,
a_n, modelform,
control, strat_col
)
Calculates poisson residuals
Description
RunPoissonRegression_Residual uses user provided data, time/event columns,
vectors specifying the model, and options. Calculates residuals or sum of residuals
Usage
RunPoissonRegression_Residual(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null",
model_control = list()
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
pyr <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow",
"double_step" = 1
)
guesses_control <- list(
"maxiter" = 10, "guesses" = 10,
"lin_min" = 0.001, "lin_max" = 1,
"loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform",
"loglin_method" = "uniform", strata = FALSE
)
strat_col <- "e"
e <- RunPoissonRegression_Residual(
df, pyr, event, names, term_n,
tform, keep_constant,
a_n, modelform,
control, strat_col
)
Performs poisson regression with no derivative calculations
Description
RunPoissonRegression_Single uses user provided data, person-year/event columns, vectors specifying the model, and returns the results
Usage
RunPoissonRegression_Single(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
a_n = c(0),
modelform = "M",
control = list(),
keep_constant = rep(0, length(names))
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
keep_constant |
binary values to denote which parameters to change |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Strata(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
df$pyr <- df$Ending_Age - df$Starting_Age
pyr <- "pyr"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5,
"epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE, "double_step" = 1
)
e <- RunPoissonRegression_Single(
df, pyr, event, names,
term_n, tform, a_n, modelform,
control
)
Performs poisson regression with strata effect
Description
RunPoissonRegression_Strata uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions
Usage
RunPoissonRegression_Strata(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
strat_col = "null"
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 0, 0, 1, 0, 1)
)
# For the interval case
df$pyr <- df$Ending_Age - df$Starting_Age
pyr <- "pyr"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5,
"epsilon" = 1e-3, "deriv_epsilon" = 1e-3,
"abs_max" = 1.0, "dose_abs_max" = 100.0,
"verbose" = FALSE, "double_step" = 1
)
strat_col <- c("e")
e <- RunPoissonRegression_Strata(
df, pyr, event, names,
term_n, tform, keep_constant,
a_n, modelform, control, strat_col
)
Performs basic poisson regression, with multiple guesses, starts with a single term
Description
RunPoissonRegression_Tier_Guesses uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions, with additional guesses
Usage
RunPoissonRegression_Tier_Guesses(
df,
pyr0 = "pyr",
event0 = "event",
names = c("CONST"),
term_n = c(0),
tform = "loglin",
keep_constant = c(0),
a_n = c(0),
modelform = "M",
control = list(),
guesses_control = list(),
strat_col = "null",
model_control = list()
)
Arguments
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
Value
returns a list of the final results
See Also
Other Poisson Wrapper Functions:
PoissonCurveSolver(),
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Strata()
Examples
library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1),
"e" = c(0, 0, 0, 0, 1, 0, 1)
)
# For the interval case
df$pyr <- df$Ending_Age - df$Starting_Age
pyr <- "pyr"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
"ncores" = 2, "lr" = 0.75, "maxiter" = 5,
"halfmax" = 5, "epsilon" = 1e-3,
"deriv_epsilon" = 1e-3, "abs_max" = 1.0,
"dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1
)
guesses_control <- list(
"iterations" = 10, "guesses" = 10,
"lin_min" = 0.001, "lin_max" = 1,
"loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform",
"loglin_method" = "uniform", strata = TRUE, term_initial = c(0, 1)
)
strat_col <- c("e")
options(warn = -1)
e <- RunPoissonRegression_Tier_Guesses(
df, pyr, event, names,
term_n, tform, keep_constant, a_n, modelform,
control, guesses_control, strat_col
)
Checks OS, compilers, and OMP
Description
System_Version checks OS, default R c++ compiler, and if OMP is enabled
Usage
System_Version()
Value
returns a list of results
See Also
Other Output and Information Functions:
Interpret_Output()
Automates creating a date since a reference column
Description
Time_Since generates a new dataframe with a column containing time since a reference in a given unit
Usage
Time_Since(df, dcol0, tref, col_name, units = "days")
Arguments
df |
a data.table containing the columns of interest |
dcol0 |
list of ending month, day, and year |
tref |
reference time in date format |
col_name |
vector of new column names |
units |
time unit to use |
Value
returns the updated dataframe
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
m0 <- c(1, 1, 2, 2)
m1 <- c(2, 2, 3, 3)
d0 <- c(1, 2, 3, 4)
d1 <- c(6, 7, 8, 9)
y0 <- c(1990, 1991, 1997, 1998)
y1 <- c(2001, 2003, 2005, 2006)
df <- data.table::data.table(
"m0" = m0, "m1" = m1,
"d0" = d0, "d1" = d1,
"y0" = y0, "y1" = y1
)
tref <- strptime("3-22-1997", format = "%m-%d-%Y", tz = "UTC")
df <- Time_Since(df, c("m1", "d1", "y1"), tref, "date_since")
Splits a parameter into factors
Description
factorize uses user provided list of columns to define new parameter for each unique value and update the data.table.
Not for interaction terms
Usage
factorize(df, col_list, verbose = 0)
Arguments
df |
a data.table containing the columns of interest |
col_list |
an array of column names that should have factor terms defined |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns a list with two named fields. df for the updated dataframe, and cols for the new column names
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize_par(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 2, 1, 0, 1, 0)
df <- data.table::data.table("a" = a, "b" = b, "c" = c)
col_list <- c("c")
val <- factorize(df, col_list)
df <- val$df
new_col <- val$cols
Splits a parameter into factors in parallel
Description
factorize_par uses user provided list of columns to define new parameter for each unique value and update the data.table.
Not for interaction terms
Usage
factorize_par(df, col_list, verbose = 0, nthreads = as.numeric(detectCores()))
Arguments
df |
a data.table containing the columns of interest |
col_list |
an array of column names that should have factor terms defined |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
nthreads |
number of threads to use, do not use more threads than available on your machine |
Value
returns a list with two named fields. df for the updated dataframe, and cols for the new column names
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
gen_time_dep(),
interact_them()
Examples
library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 2, 1, 0, 1, 0)
df <- data.table::data.table("a" = a, "b" = b, "c" = c)
col_list <- c("c")
val <- factorize_par(df, col_list, FALSE, 2)
df <- val$df
new_col <- val$cols
Checks default c++ compiler
Description
gcc_version Checks default c++ compiler, part of configuration script
Usage
gcc_version()
Value
returns a string representation of gcc, clang, or c++ output
Applies time dependence to parameters
Description
gen_time_dep generates a new dataframe with time dependent covariates by applying a grid in time
Usage
gen_time_dep(
df,
time1,
time2,
event0,
iscox,
dt,
new_names,
dep_cols,
func_form,
fname,
tform,
nthreads = as.numeric(detectCores())
)
Arguments
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
iscox |
boolean if rows not at event times should not be kept, rows are removed if true. a Cox proportional hazards model does not use rows with intervals not containing event times |
dt |
spacing in time for new rows |
new_names |
list of new names to use instead of default, default used if entry is ”" |
dep_cols |
columns that are not needed in the new dataframe |
func_form |
vector of functions to apply to each time-dependent covariate. Of the form func(df, time) returning a vector of the new column value |
fname |
filename used for new dataframe |
tform |
list of string function identifiers, used for linear/step |
nthreads |
number of threads to use, do not use more threads than available on your machine |
Value
returns the updated dataframe
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
interact_them()
Examples
library(data.table)
# Adapted from the tests
a <- c(20, 20, 5, 10, 15)
b <- c(1, 2, 1, 1, 2)
c <- c(0, 0, 1, 1, 1)
df <- data.table::data.table("a" = a, "b" = b, "c" = c)
time1 <- "%trunc%"
time2 <- "a"
event <- "c"
control <- list(
"lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-9,
"deriv_epsilon" = 1e-9, "abs_max" = 1.0,
"dose_abs_max" = 100.0,
"verbose" = FALSE, "ties" = "breslow", "double_step" = 1
)
grt_f <- function(df, time_col) {
return((df[, "b"] * df[, get(time_col)])[[1]])
}
func_form <- c("lin")
df_new <- gen_time_dep(
df, time1, time2, event, TRUE, 0.01, c("grt"), c(),
c(grt_f), paste("test", "_new.csv", sep = ""), func_form, 2
)
file.remove("test_new.csv")
Checks system OS
Description
get_os checks the system OS, part of configuration script
Usage
get_os()
Value
returns a string representation of OS
Defines Interactions
Description
interact_them uses user provided interactions define interaction terms and update the data.table. assumes interaction is "+" or "*" and applies basic anti-aliasing to avoid duplicates
Usage
interact_them(df, interactions, new_names, verbose = 0)
Arguments
df |
a data.table containing the columns of interest |
interactions |
array of strings, each one is of form term1?*?term2" for term1 interaction of type * or + with term2, "?" dlimits |
new_names |
list of new names to use instead of default, default used if entry is ”" |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
Value
returns a list with two named fields. df for the updated dataframe, and cols for the new column names
See Also
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Convert_Model_Eq(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Event_Count_Gen(),
Event_Time_Gen(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep()
Examples
library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 2, 1, 0, 1, 0)
df <- data.table::data.table("a" = a, "b" = b, "c" = c)
interactions <- c("a?+?b", "a?*?c")
new_names <- c("ab", "ac")
vals <- interact_them(df, interactions, new_names)
df <- vals$df
new_col <- vals$cols