| Title: | 1d Goodness of Fit Tests |
| Version: | 3.3.0 |
| Description: | Routines that allow the user to run a large number of goodness-of-fit tests. It allows for data to be continuous or discrete. It includes routines to estimate the power of the tests and display them as a power graph. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| LinkingTo: | Rcpp |
| Imports: | Rcpp, parallel, ggplot2, stats, graphics, microbenchmark, nortest |
| Suggests: | rmarkdown, knitr |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 3.5) |
| LazyData: | true |
| NeedsCompilation: | yes |
| Packaged: | 2025-06-16 18:20:32 UTC; Wolfgang |
| Author: | Wolfgang Rolke 
[aut, cre] |
| Maintainer: | Wolfgang Rolke <wolfgang.rolke@upr.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-16 18:30:02 UTC |
Rgof: 1d Goodness of Fit Tests
Description
Routines that allow the user to run a large number of goodness-of-fit tests. It allows for data to be continuous or discrete. It includes routines to estimate the power of the tests and display them as a power graph. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones.
Author(s)
Maintainer: Wolfgang Rolke wolfgang.rolke@upr.edu (ORCID)
sort vector y by values in vector x
Description
sort vector y by values in vector x
Usage
Cpporder(y, x)
Arguments
y |
numeric vector |
x |
numeric vector |
Value
numeric vector
Find test statistics for continuous data
Description
Find test statistics for continuous data
Usage
TS_cont(x, pnull, param, qnull)
Arguments
x |
A numeric vector. |
pnull |
cdf. |
param |
parameters for pnull in case of parameter estimation. |
qnull |
An R function, the quantile function under the null hypothesis. |
Value
A numeric vector with test statistics
Find test statistics for discrete data
Description
Find test statistics for discrete data
Usage
TS_disc(x, pnull, param, vals)
Arguments
x |
An integer vector. |
pnull |
cdf. |
param |
parameters for pnull in case of parameter estimation. |
vals |
A numeric vector with the values of the discrete rv. |
Value
A vector with test statistics
Find test statistics for continuous data with weights
Description
Find test statistics for continuous data with weights
Usage
TSw_cont(x, pnull, param, w)
Arguments
x |
A numeric vector. |
pnull |
cdf. |
param |
parameters for pnull in case of parameter estimation. |
w |
numeric vector of weights |
Value
A numeric vector with test statistics
Find test statistics for discrete data
Description
Find test statistics for discrete data
Usage
TSw_disc(x, pnull, param, vals, w)
Arguments
x |
An integer vector. |
pnull |
cdf. |
param |
parameters for pnull in case of parameter estimation. |
vals |
A numeric vector with the values of the discrete rv. |
w |
weights |
Value
A vector with test statistics
count events in bins. Useful for power calculations. Replaces hist command from R.
Description
count events in bins. Useful for power calculations. Replaces hist command from R.
Usage
bincounter(x, bins)
Arguments
x |
numeric vector |
bins |
numeric vector |
Value
Integer vector of counts
This function calculates the test statistics for data
Description
This function calculates the test statistics for data
Usage
calcTS(dta, TS, typeTS, TSextra)
Arguments
dta |
data set as a list |
TS |
routine |
typeTS |
format of TS |
TSextra |
list passed to TS function |
Value
A vector of numbers
This function creates the functions needed to run the various case studies.
Description
This function creates the functions needed to run the various case studies.
Usage
case.studies(which, nsample = 500)
Arguments
which |
name of the case study. |
nsample |
=500, sample size. |
Value
a list of functions
This function checks whether the inputs have the correct format
Description
This function checks whether the inputs have the correct format
Usage
check.functions(pnull, rnull, phat = function(x) -99, vals, x)
Arguments
pnull |
cdf under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 |
vals |
vector of discrete values |
x |
data |
This function finds the power of various chi-square tests for continuous data
Description
This function finds the power of various chi-square tests for continuous data
Usage
chi_power_cont(
pnull,
ralt,
param_alt,
qnull = NA,
phat = function(x) -99,
w = function(x) -99,
alpha = 0.05,
Range = c(-99999, 99999),
B = 1000,
nbins = c(50, 10),
rate = 0,
minexpcount = 5,
ChiUsePhat = TRUE
)
Arguments
pnull |
function to find cdf under null hypothesis |
ralt |
function to generate data under alternative hypothesis |
param_alt |
vector of parameter values for distribution under alternative hypothesis |
qnull |
=NA function to find quantiles under null hypothesis, if available |
phat |
=function(x) -99, function to estimate parameters |
w |
=function(x) -99, optional weight function |
alpha |
=0.05, the level of the hypothesis test |
Range |
=c(-99999, 99999) limits of possible observations, if any |
B |
=1000 number of simulation runs to find power |
nbins |
=c(50,10), number of bins for chi square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
=TRUE, if TRUE param is estimated parameters and no minimization is used |
Value
A numeric matrix of power values.
This function finds the power of various chi-square tests for continuous data
Description
This function finds the power of various chi-square tests for continuous data
Usage
chi_power_disc(
pnull,
ralt,
param_alt,
phat = function(x) -99,
alpha = 0.05,
B = 1000,
nbins = c(50, 10),
rate = 0,
minexpcount = 5,
ChiUsePhat = TRUE
)
Arguments
pnull |
function to find cdf under null hypothesis |
ralt |
function to generate data under alternative hypothesis |
param_alt |
vector of parameter values for distribution under alternative hypothesis |
phat |
=function(x) -99, routine to estimate parameters |
alpha |
=0.05, the level of the hypothesis test |
B |
=1000 number of simulation runs to find power |
nbins |
=c(50,10), number of bins for chi square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, should chi square use minimum chi square method? |
Value
A numeric matrix of power values.
This function performs a number of chi-square gof tests for continuous data
Description
This function performs a number of chi-square gof tests for continuous data
Usage
chi_test_cont(
x,
pnull,
w = function(x) -99,
phat = function(x) -99,
qnull = NA,
nbins = c(50, 10),
rate = 0,
Range = c(-99999, 99999),
minexpcount = 5,
ChiUsePhat = TRUE,
allbins
)
Arguments
x |
data set |
pnull |
cdf under the null hypothesis |
w |
function to find weights of observations, returns -99 if data is unweighted |
phat |
=function(x) -99, estimated parameters, or starting values of multi-D minimum chi square minimization, or -99 if no estimation is done |
qnull |
=NA quantile function, if available |
nbins |
=c(50, 10) number of bins for chi-square tests |
rate |
=0, rate of Poisson if sample size is random |
Range |
=c(-99999, 99999) limits of possible observations, if any |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
=TRUE, if TRUE param is estimated parameters and no minimization is used |
allbins |
set of bins to use |
Value
A numeric matrix of test statistics, degrees of freedom and p.values
This function performs a number of chi-square gof tests for continuous data
Description
This function performs a number of chi-square gof tests for continuous data
Usage
chi_test_disc(
x,
pnull,
phat = function(x) -99,
nbins = c(50, 10),
rate = 0,
minexpcount = 5,
ChiUsePhat = TRUE,
allbins
)
Arguments
x |
data set |
pnull |
cdf under the null hypothesis |
phat |
=function(x) -99, function to estimate parameters, or starting values of multi-D minimum chi square minimization, or -99 if no parameters are estimated |
nbins |
=c(50, 10) number of bins for chi-square tests |
rate |
=0, rate of Poisson if sample size is random |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
allbins |
set of bins to use |
Value
A numeric matrix of test statistics, degrees of freedom and p.values
helper functions to do p value adjustment
Description
helper functions to do p value adjustment
Usage
gof_adj_C1(dta, rnull, vals, TS, typeTS, TSextra, B = 1000L)
Arguments
dta |
data set |
rnull |
R function (generate data under null hypothesis) |
vals |
values of discrete random variable |
TS |
function to calculate test statistics |
typeTS |
integer indicating type of test statistic |
TSextra |
list to pass to TS |
B |
=1000 Number of simulation runs |
Value
A matrix of powers
helper functions to do p value adjustment
Description
helper functions to do p value adjustment
Usage
gof_adj_C2(dta, rnull, vals, TS, typeTS, TSextra, A, B = 1000L)
Arguments
dta |
data set |
rnull |
R function (generate data under null hypothesis) |
vals |
values of discrete random variable |
TS |
function to calculate test statistics |
typeTS |
integer indicating type of test statistic |
TSextra |
list to pass to TS |
A |
matrix of test statistic values |
B |
=1000 Number of simulation runs |
Value
A matrix of powers
Power estimation of goodness-of-fit tests.
Description
Find the power of various goodness-of-fit tests.
Usage
gof_power(
pnull,
vals = NA,
rnull,
ralt,
param_alt,
w = function(x) -99,
phat = function(x) -99,
TS,
TSextra,
With.p.value = FALSE,
alpha = 0.05,
Range = c(-Inf, Inf),
B = 1000,
nbins = c(50, 10),
rate = 0,
maxProcessor,
minexpcount = 5,
ChiUsePhat = TRUE
)
Arguments
pnull |
function to find cdf under null hypothesis |
vals |
=NA values of discrete random variable, or NA |
rnull |
function to generate data under null hypothesis |
ralt |
function to generate data under alternative hypothesis |
param_alt |
vector of parameter values for distribution under alternative hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99 function to estimate parameters from the data, or -99 |
TS |
user supplied function to find test statistics |
TSextra |
list provided to TS (optional) |
With.p.value |
=FALSE does user supplied routine return p values? |
alpha |
=0.05, the level of the hypothesis test |
Range |
=c(-Inf, Inf) limits of possible observations, if any |
B |
=1000 number of simulation runs |
nbins |
=c(50,10), number of bins for chi square tests. |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
maxProcessor |
maximum of number of processors to use, 1 if no parallel processing is needed or number of cores-1 if missing |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
Details
For details on the usage of this routine consult the vignette with vignette("Rgof","Rgof")
Value
A numeric matrix of power values.
Examples
# Power of tests when null hypothesis specifies the standard normal distribution but
# true data comes from a normal distribution with mean different from 0.
pnull = function(x) pnorm(x)
rnull = function() rnorm(50)
ralt = function(mu) rnorm(50, mu)
TSextra = list(qnull=function(x) qnorm(x))
gof_power(pnull, NA, rnull, ralt, c(0.25, 0.5), TSextra=TSextra, B=200)
# Power of tests when null hypothesis specifies normal distribution and
# mean and standard deviation are estimated from the data.
# true data comes from a normal distribution with mean different from 0.
pnull = function(x, p=c(0, 1)) pnorm(x, p[1], ifelse(p[2]>0.001, p[2], 0.001))
rnull = function(p=c(0, 1)) rnorm(50, p[1], ifelse(p[2]>0.001, p[2], 0.001))
ralt = function(mu) rnorm(50, mu)
phat = function(x) c(mean(x), sd(x))
TSextra = list(qnull = function(x, p=c(0, 1)) qnorm(x, p[1],
ifelse(p[2]>0.001, p[2], 0.001)))
pwr=gof_power(pnull, NA, rnull, ralt, c(0, 1), phat=phat, TSextra=TSextra, B=200)
pwr
#' Compare power of a new test based on variants of the Cramer-vonMises
#' criterion to the methods included in the package:
newTS = function(x, pnull, param) {
Fx=sort(pnull(x, param))
n=length(x)
out = c(sum(abs( (2*1:n-1)/2/n-Fx )), sum(sqrt(abs( (2*1:n-1)/2/n-Fx ))))
names(out) = c("CvM alt 1","CvM alt 2")
out
}
#' Compare power to Lilliefors KS test, which finds its own p value:
LLtest=function(x, pnull, param) {
out=nortest::lillie.test(x)$p.value
names(out)="KS - Lilliefors"
out
}
cbind(gof_power(pnull, NA, rnull, ralt, c(0, 1), TS=LLtest, phat=phat,
With.p.value=TRUE, TSextra=TSextra, B=200), pwr)
# Power of tests when null hypothesis specifies Poisson rv with rate 100 and
# true rate is 100.5
vals = 0:250
pnull = function() ppois(0:250, 100)
rnull =function () table(c(0:250, rpois(1000, 100)))-1
ralt =function (p) table(c(0:250, rpois(1000, p)))-1
gof_power(pnull, vals, rnull, ralt, param_alt=100.5, B=200)
# Power of tests when null hypothesis specifies a Binomial n=10 distribution
# with the success probability estimated
vals = 0:10
pnull=function(p) pbinom(0:10, 10, ifelse(0<p&p<1, p, 0.001))
rnull=function(p) table(c(0:10, rbinom(1000, 10, ifelse(0<p&p<1, p, 0.001))))-1
ralt=function(p) table(c(0:10, rbinom(1000, 10, p)))-1
phat=function(x) mean(rep(0:10,x))/10
gof_power(pnull, vals, rnull, ralt, c(0.5, 0.6), phat=phat, B=200)
find power of gof tests for continuous data
Description
find power of gof tests for continuous data
Usage
gof_power_C(rnull, vals, ralt, param_alt, TS, typeTS, TSextra, B = 1000L)
Arguments
rnull |
R function (generate data under null hypothesis) |
vals |
values of discrete random variable |
ralt |
R function to generate data under alternative |
param_alt |
parameters of ralt |
TS |
function to calculate test statistics |
typeTS |
integer indicating type of test statistic |
TSextra |
list to pass to TS |
B |
=1000 Number of simulation runs |
Value
A matrix of powers
Tests for the univariate goodness-of-fit problem
Description
This function runs a number of goodness-of-fit tests using Rcpp and parallel computing.
Usage
gof_test(
x,
vals = NA,
pnull,
rnull,
w = function(x) -99,
phat = function(x) -99,
TS,
TSextra = NA,
nbins = c(50, 10),
rate = 0,
Range = c(-Inf, Inf),
B = 5000,
minexpcount = 5,
ChiUsePhat = TRUE,
maxProcessor,
doMethods = "all"
)
Arguments
x |
data set |
vals |
=NA, values of discrete RV, or NA if data is continuous |
pnull |
cdf under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters are estimated |
TS |
user supplied function to find test statistics, if any |
TSextra |
=NA, list passed to TS, if desired, or NA |
nbins |
=c(100, 10) number of bins for chi-square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
Range |
=c(-Inf, Inf) limits of possible observations, if any, for chi-square tests |
B |
=5000 number of simulation runs |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
maxProcessor |
=1, number of processors to use in parallel processing. |
doMethods |
="all", a vector of codes for the methods to include or all of them. |
Details
For details on the usage of this routine consult the vignette with vignette("Rgof","Rgof")
Value
A list with vectors of test statistics and p.values
Examples
# Tests to see whether data comes from a standard normal distribution.
pnull = function(x) pnorm(x)
rnull = function() rnorm(100)
x = rnorm(100)
gof_test(x, NA, pnull, rnull, B=500)
# Tests to see whether data comes from a normal distribution with standard deviation 1
# and the mean estimated.
pnull=function(x, m) pnorm(x, m)
rnull=function(m) rnorm(100, m)
TSextra = list(qnull=function(x, m=0) qnorm(x, m),
pnull=function(x, m=0) pnorm(x, m), phat=function(x) mean(x))
phat=function(x) mean(x)
x = rnorm(100, 1, 2)
gof_test(x, NA, pnull, rnull, phat=phat, TSextra=TSextra, B=500)
# Tests to see whether data comes from a binomial (10, 0.5) distribution.
vals=0:10
pnull = function() pbinom(0:10, 10, 0.5)
rnull = function() table(c(0:10, rbinom(1000, 10, 0.5)))-1
x = rnull()
gof_test(x, vals, pnull, rnull, doMethods="all", B=500)
# Tests to see whether data comes from a binomial distribution with
# the success probability estimated from the data.
pnull = function(p=0.5) pbinom(0:10, 10, ifelse(p>0&&p<1, p, 0.001))
rnull = function(p=0.5) table(c(0:10, rbinom(1000, 10,
ifelse(p>0&&p<1, p, 0.001))))-1
phat=function(x) mean(rep(0:10,x))/10
gof_test(x, vals, pnull, rnull, phat=phat, B=500)
run gof tests for continuous data
Description
run gof tests for continuous data
Usage
gof_test_C(dta, rnull, TS, typeTS, TSextra, B = 5000L)
Arguments
dta |
A numeric vector of data |
rnull |
R function (generate data under null hypothesis) |
TS |
function that calculates test statistics |
typeTS |
integer indicating type of test statistic |
TSextra |
list to pass to TS |
B |
(=5000) Number of simulation runs |
Value
A matrix of numbers
Adjusted p values for simultaneous testing in the goodness-of-fit problem.
Description
This function performs a number of goodness-of-fit tests and finds the adjusted p value for the combined test.
Usage
gof_test_adjusted_pvalue(
x,
vals = NA,
pnull,
rnull,
w = function(x) -99,
phat = function(x) -99,
TS,
TSextra = NA,
nbins = c(50, 10),
rate = 0,
Range = c(-Inf, Inf),
B = c(5000, 1000),
minexpcount = 5,
ChiUsePhat = TRUE,
maxProcessor,
doMethods
)
Arguments
x |
data set |
vals |
=NA, values of discrete RV, or NA if data is continuous |
pnull |
cdf under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters are estimated |
TS |
user supplied function to find test statistics, if any |
TSextra |
=NA, list passed to TS, if desired, or NA |
nbins |
=c(100, 10) number of bins for chi-square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
Range |
=c(-Inf, Inf) limits of possible observations, if any, for chi-square tests |
B |
=c(5000,1000) number of simulation runs for individual and for adjusted p values |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
maxProcessor |
number of cores to use |
doMethods |
a vector of codes for the methods to include. If missing, a default selection of methods are used. |
Details
For details on the usage of this routine consult the vignette with vignette("Rgof","Rgof")
Value
None
Examples
# Tests to see whether data comes from a standard normal distribution.
pnull = function(x) pnorm(x)
rnull = function() rnorm(100)
x = rnorm(100)
gof_test_adjusted_pvalue(x, NA, pnull, rnull, B=c(500, 200),
maxProcessor=1)
# Tests to see whether data comes from a normal distribution with standard deviation 1
# and the mean estimated.
pnull=function(x, m) pnorm(x, m)
rnull=function(m) rnorm(100, m)
TSextra = list(qnull=function(x, m=0) qnorm(x, m))
phat=function(x) mean(x)
x = rnorm(100, 1, 2)
gof_test_adjusted_pvalue(x, NA, pnull, rnull, phat=phat,
TSextra=TSextra, B=c(500, 200), maxProcessor=1)
# Tests to see whether data comes from a binomial (10, 0.5) distribution.
vals=0:10
pnull = function() pbinom(0:10, 10, 0.5)
rnull = function() table(c(0:10, rbinom(1000, 10, 0.5)))-1
x = rnull()
gof_test_adjusted_pvalue(x, vals, pnull, rnull,
B=c(500, 200), maxProcessor=1)
# Tests to see whether data comes from a binomial distribution with
# the success probability estimated from the data.
pnull = function(p=0.5) pbinom(0:10, 10, p)
rnull = function(p=0.5) table(c(0:10, rbinom(1000, 10, p)))-1
phat=function(x) mean(rep(0:10,x))/10
gof_test_adjusted_pvalue(x, vals, pnull, rnull, phat=phat,
B=c(500, 200), maxProcessor=1)
This function creates several type of bins for continuous data
Description
This function creates several type of bins for continuous data
Usage
make_bins_cont(
x,
pnull,
qnull = NA,
phat = function(x) -99,
DataBased = FALSE,
nbins = c(50, 10),
minexpcount = 5,
Range = c(-99999, 99999)
)
Arguments
x |
data set |
pnull |
cdf under the null hypothesis |
qnull |
=NA quantile function, if available |
phat |
=function(x) -99 parameters for pnull |
DataBased |
=FALSE bins based on data, not expected counts |
nbins |
=c(50, 10) number of bins |
minexpcount |
=5 smallest expected count per bin |
Range |
=c(-99999, 99999) limits of possible observations, if any |
Value
A list of bins and bin probabilities
This function creates several types of bins for discrete data
Description
This function creates several types of bins for discrete data
Usage
make_bins_disc(
x,
pnull,
phat = function(x) -99,
nbins = c(50, 10),
minexpcount = 5
)
Arguments
x |
counts |
pnull |
cumulative distribution function |
phat |
=function(x) -99, function to estimated parameters, or -99 |
nbins |
=c(50, 10) number of bins |
minexpcount |
=5 smallest expected count per bin |
Value
A list of indices
a local function needed for the vignette
Description
a local function needed for the vignette
Usage
newTSdisc(x, pnull, param, vals)
Arguments
x |
An integer vector. |
pnull |
cdf. |
param |
parameters for pnull in case of parameter estimation. |
vals |
A numeric vector with the values of the discrete rv. |
Value
A vector with test statistics
This function draws the power graph, with curves sorted by the mean power and smoothed for easier reading.
Description
This function draws the power graph, with curves sorted by the mean power and smoothed for easier reading.
Usage
plot_power(pwr, xname = " ", title, Smooth = TRUE, span = 0.25)
Arguments
pwr |
a matrix of power values, usually from the twosample_power command |
xname |
Name of variable on x axis |
title |
(Optional) title of graph |
Smooth |
=TRUE lines are smoothed for easier reading |
span |
=0.25bandwidth of smoothing method |
Value
plt, an object of class ggplot.
This function estimates the power of test routines that calculate p value(s)
Description
This function estimates the power of test routines that calculate p value(s)
Usage
power_newtest(
TS,
vals = NA,
pnull,
ralt,
param_alt,
phat,
TSextra,
alpha = 0.05,
B = 1000
)
Arguments
TS |
routine to calculate test statistics. |
vals |
=NA if data is discrete, a vector of possible values |
pnull |
routine to calculate the cdf under the null hypothesis |
ralt |
generate data under alternative hypothesis |
param_alt |
values of parameter under the alternative hypothesis. |
phat |
function to estimate parameters, function(x) -99 if no parameter estimation |
TSextra |
list (possibly) passed to TS |
alpha |
=0.05 type I error. |
B |
= 1000 number of simulation runs to estimate the power. |
Value
A matrix of power values
power_studies_results
Description
the results of the included power studies
Usage
power_studies_results
Format
'power_studies_results'
A list of matrices with powers
pvaluecdf
Description
the info needed to draw a graph
Usage
pvaluecdf
Format
'pvaluecdf'
A matrix
Power Comparisons
Description
This function runs the case studies included in the package and compares the power of a new test to those included.
Usage
run.studies(
TS,
study,
TSextra = list(aaa = 1),
With.p.value = FALSE,
BasicComparison = TRUE,
nsample = 500,
alpha = 0.05,
param_alt,
maxProcessor,
B = 1000
)
Arguments
TS |
routine to calculate test statistic(s) or p value(s). |
study |
either the name of the study, or its number. If missing all the studies are run. |
TSextra |
=list(aaa=1), list passed to TS. |
With.p.value |
=FALSE does user supplied routine return p values? |
BasicComparison |
=TRUE if true compares tests on one default value of parameter of the alternative distribution. |
nsample |
= 500, desired sample size. |
alpha |
=0.05 type I error |
param_alt |
(list of) values of parameter under the alternative hypothesis. If missing included values are used. |
maxProcessor |
number of cores to use for parallel programming |
B |
= 1000 number of simulation runs |
Details
For details on the usage of this routine consult the vignette with vignette("Rgof","Rgof")
Value
A (list of ) matrices of power values
Examples
# New test is a simple chi-square test:
chitest=function(x, pnull, param, TSextra) {
nbins=TSextra$nbins
bins=quantile(x, (0:nbins)/nbins)
O=hist(x, bins, plot=FALSE)$counts
if(param[1]!=-99) { #with parameter estimation
E=length(x)*diff(pnull(bins, param))
chi=sum((O-E)^2/E)
pval=1-pchisq(chi, nbins-1-length(param))
}
else {
E=length(x)*diff(pnull(bins))
chi=sum((O-E)^2/E)
pval=1-pchisq(chi,nbins-1)
}
out=ifelse(TSextra$statistic, chi, pval)
names(out)="ChiSquare"
out
}
TSextra=list(nbins=10, statistic=FALSE) # Use 10 bins, test routine returns p-value
run.studies(chitest, TSextra=TSextra, With.p.value=TRUE, maxProcessor=1, B=200)
This function does some rounding to nice numbers
Description
This function does some rounding to nice numbers
Usage
## S3 method for class 'digits'
signif(x, d = 3)
Arguments
x |
a list of two vectors |
d |
=4 number of digits to round to |
Value
A list with rounded vectors
This function checks whether the correct methods have been requested
Description
This function checks whether the correct methods have been requested
Usage
test_methods(doMethods, Continuous, WithWeights)
Arguments
doMethods |
="all" Which methods should be included? |
Continuous |
is data continuous |
WithWeights |
with weights? |
Value
TRUE or FALSE
estimate run time function
Description
estimate run time function
Usage
timecheck(dta, TS, typeTS, TSextra)
Arguments
dta |
data set |
TS |
test statistic |
typeTS |
format of TS |
TSextra |
additional info TS |
Value
Mean computation time
Find counts or sum of weights in bins. Useful for power calculations. Replaces hist command from R.
Description
Find counts or sum of weights in bins. Useful for power calculations. Replaces hist command from R.
Usage
wbincounter(x, bins, w)
Arguments
x |
numeric vector |
bins |
numeric vector |
w |
numeric vector of weights |
Value
sum of weights in bins