| Type: | Package |
| Title: | Taxometric Analysis |
| Version: | 3.2.1 |
| Date: | 2023-5-29 |
| Author: | John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> |
| Maintainer: | John Ruscio <ruscio@tcnj.edu> |
| Description: | We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method. |
| License: | MIT + file LICENSE |
| RoxygenNote: | 7.1.0 |
| NeedsCompilation: | no |
| Packaged: | 2023-05-30 02:56:10 UTC; shirleywang |
| Repository: | CRAN |
| Date/Publication: | 2023-05-30 12:00:02 UTC |
Adds variance
Description
This function adds variance if necessary
Usage
AddVariance(x, k, parameters)
Arguments
x |
The supplied data matrix. |
k |
The number of variables. |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Data with necessary variance added
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Assigns variables to input/output for MAMBAC procedure
Description
This function assigns variables to input/output configurations for MAMBAC analysis.
Usage
AssignMAMBAC(parameters)
Arguments
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Input/output variables per curve
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Assigns variables to input/output for MAXEIG procedure
Description
This function assigns variables to input/output configurations for MAXEIG analysis.
Usage
AssignMAXEIG(parameters)
Arguments
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Input/output variables per curve
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates and returns base rate estimates for taxometric analysis
Description
This function calculates and reports base rate estimates for taxometric analysis.
Usage
CalculateBaseRates(x.results, parameters)
Arguments
x.results |
Empirical data results |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
This program returns nothing, and provides text output only.
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates CCFIs
Description
This function calculates CCFIs for a MAMBAC, MAXEIG, or MAXSLOPE curve
Usage
CalculateCCFI(curve, curve.dim, curve.cat)
Arguments
curve |
Empirical data curve |
curve.dim |
Average curve for dimensional comparison data |
curve.cat |
Average curve for categorical comparison data |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
CCFI value
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates CCFIs for profiles
Description
This function calculates CCFI results for CCFI profiles
Usage
CalculateCCFIs(x.results, x.dim.results, x.cat.results, parameters)
Arguments
x.results |
Empirical data results |
x.dim.results |
Dimensional comparison data results |
x.cat.results |
Categorical comparison data results |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
CCFI values
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates CCFIs for profiles
Description
This function calculates CCFI results for CCFI profiles
Usage
CalculateCCFIsProfile(x.results, x.dim.results, x.cat.results, parameters)
Arguments
x.results |
Empirical data results |
x.dim.results |
Dimensional comparison data results |
x.cat.results |
Categorical comparison data results |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
CCFI values
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates fit
Description
This function calculates fit for L-Mode curves
Usage
CalculateFitDensities(shift, data)
Arguments
shift |
Horizontal shift |
data |
Curves for empirical and comparison data |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Fit value
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates kurtosis
Description
This function calculates the sample kurtosis of a distribution
Usage
CalculateKurtosis(x)
Arguments
x |
The data vector |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
The sample kurtosis of x
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates L-Mode CCFI
Description
This function calculates CCFI for an L-Mode curve
Usage
CalculateLModeCCFI(curve.x, curve.y, curve.dim.x, curve.dim.y, curve.cat.x, curve.cat.y)
Arguments
curve.x |
Empirical data curve, x |
curve.y |
Empirical data curve, y |
curve.dim.x |
Average curve for dimensional comparison data, x |
curve.dim.y |
Average curve for dimensional comparison data, y |
curve.cat.x |
Average curve for categorical comparison data, x |
curve.cat.y |
Average curve for categorical comparison data, y |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
CCFI value
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates MAMBAC curve
Description
This function calculates one MAMBAC curve
Usage
CalculateMAMBAC(input, output, parameters)
Arguments
input |
Input indicator |
output |
Output indicator |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
One MAMBAC curve
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates MAXEIG curve
Description
This function calculates one MAXEIG curve
Usage
CalculateMAXEIG(input, outputs, parameters)
Arguments
input |
Input indicator |
outputs |
Output indicators |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
List object with one MAXEIG curve:
curve.x |
x values |
curve.y |
y values |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates MAXSLOPE curve
Description
This function calculates one MAXSLOPE curve
Usage
CalculateMAXSLOPE(x, curve)
Arguments
x |
The data matrix |
curve |
Curve number |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
List object with one MAXSLOPE curve:
curve.x |
x values |
curve.y |
y values |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Provides aggregated CCFIs and base rate estimates for CCFI profile
Description
This function provides aggregated CCFIs and base rate estimates for CCFI profile
Usage
CalculateProfileOutput(CCFIs, parameters)
Arguments
CCFIs |
CCFI values across base rates and procedures |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
This function returns aggregated CCFI values.
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates skew
Description
This function calculates the sample skewness of a distribution
Usage
CalculateSkew(x)
Arguments
x |
The data vector |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
The sample skewness of x
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates Validity
Description
This function calculates the standardized mean difference between two groups (Cohen's D)
Usage
CalculateValidity(x.1, x.2)
Arguments
x.1 |
Data for the first group |
x.2 |
Data for the second group |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
The standardized mean difference between groups
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Checks classification
Description
This function checks classification for problems, and terminates the program if necessary
Usage
CheckClassification(group, n)
Arguments
group |
Classification of cases |
n |
Sample size |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Nothing; text output if problem occurs
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Checks supplied data set
Description
This function checks whether the supplied empirical data set is appropriate for taxometric analysis, and provides descriptive statistics about the data set. If data do not meet certain requirements, the program prints warnings in the output, with details about which specific criteria are not met.
Usage
CheckData(x)
Arguments
x |
The supplied data matrix. Cases missing any data will be removed prior to analysis. |
Details
This function should be called directly by users before performing any taxometric procedures.
Value
This program returns nothing, and provides text output only.
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Examples
# create or import data set
# creates a categorical data set
test.cat <- CreateData("cat")
# Checks data
CheckData(test.cat)
# creates a dimensional data set
test.dim <- CreateData("dim")
# Checks data
CheckData(test.dim)
Checks parameters
Description
This function checks the parameter specifications for problems, and adjusts these parameters as needed.
Usage
CheckParameters(x, parameters)
Arguments
x |
The data matrix |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Data parameters, adjusted as needed
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Assigns cases to groups
Description
This function assigns cases to groups using the base-rate classification technique. Cases are sorted according to their total scores on all indicators, and the highest-scoring cases are assigned to the taxon such that the proportion of taxon members equals the specified base rate estimate.
Usage
ClassifyCases(x, p, cols = 0)
Arguments
x |
The supplied data matrix. |
p |
The base rate estimate that will be used to classify cases. |
cols |
The column numbers that contain variables |
Details
Users should call this function directly if they wish to assign cases to groups.
Value
Data matrix with a new classification variable.
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
References
Ruscio, J. (2009). Assigning cases to groups using taxometric results: an empirical comparison of classification techniques. Assessment, 16(1), 55-70.
Creates a data set
Description
This function creates an artificial data set based on either dimensional or categorical latent structure, which can vary according to a number of basic parameters. Such data can be useful for getting to know the taxometric programs and becoming familiar with their output by conducting analyses using data sets whose parameters are known.
Usage
CreateData(str, n = 600, k = 4, p = 0.5, d = 2, r = 0, r.tax = 0, r.comp = 0,
g = 0, h = 0, cuts = 0, uniform = F, seed = 1)
Arguments
str |
The type of data to be generated. Specify either "dim" for dimensional data or "cat" (or anything else) for categorical data. |
n |
Sample size. The default value is 600. |
k |
Number of variables. The default value is 4. |
p |
Taxon base rate. The default value is .5. |
d |
Standardized mean difference between groups. The default value is 2. |
r |
Correlation among variables. The default value is 0. |
r.tax |
Correlation among variables within the taxon. The default value is 0. |
r.comp |
Correlation among variables within the complement. The default value is 0. |
g |
Parameter used to control asymmetry (scalar); sign indicates direction and absolute value indicates magnitude of skew (e.g., +/- .30 yields substantial asymmetry). |
h |
Parameter used to control tail weight (scalar); positive values yield tails that are longer/thinner than a standard normal curve, negative values do the reverse (e.g., +/- .15 is a substantial departure from normality). |
cuts |
Parameter used to create ordered categorieas, if nonzero (scalar); number of categories will be cuts + 1. |
uniform |
Whether to generate random values (the program default) or use uniformly distributed quantiles (T/F). |
seed |
Random number seed; specifying the same seed enables users to generate and analyze identical data sets. The default value is 1. |
Details
Users should call this function directly if they wish to create an artificial data set.
Value
Data matrix; k columns contain data, final column contains classification.
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Examples
# creates a categorical data set
test.cat <- CreateData("cat")
# creates a dimensional data set
test.dim <- CreateData("dim")
Creates sample of data
Description
Generates sample of correlated data with univariate g-and-h distributions.
Usage
CreateSample(n, k, r, g, h, uniform)
Arguments
n |
Sample size |
k |
Number of variables |
r |
Correlation among variables |
g |
Parameter used to control asymmetry (scalar); sign indicates direction and absolute value indicates magnitude of skew (e.g., +/- .30 yields substantial asymmetry). |
h |
Parameter used to control tail weight (scalar); positive values yield tails that are longer/thinner than a standard normal curve, negative values do the reverse (e.g., +/- .15 is a substantial departure from normality). |
uniform |
Whether to generate random values (the program default) or use uniformly distributed quantiles (T/F). |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Sample of data
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Creates a variable
Description
Generates variable with g-and-h distribution.
Usage
CreateVariable(n, g, h, uniform)
Arguments
n |
Size of sample to create |
g |
Parameter used to control asymmetry (scalar); sign indicates direction and absolute value indicates magnitude of skew (e.g., +/- .30 yields substantial asymmetry). |
h |
Parameter used to control tail weight (scalar); positive values yield tails that are longer/thinner than a standard normal curve, negative values do the reverse (e.g., +/- .15 is a substantial departure from normality). |
uniform |
Whether to generate random values (the program default) or use uniformly distributed quantiles (T/F). |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Single variable
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Displays panels of graphs
Description
This function provides panels of graphs for taxometric analysis
Usage
DisplayPanels(x.results, x.dim.results, x.cat.results, parameters)
Arguments
x.results |
Empirical data results |
x.dim.results |
Dimensional comparison data results |
x.cat.results |
Categorical comparison data results |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
This function returns nothing, and provides graphical output only
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Plots CCFI Profiles
Description
This function plots CCFI profiles
Usage
DisplayProfiles(CCFIs, parameters)
Arguments
CCFIs |
CCFI values across base rates and procedures |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
This function returns nothing, and provides graphical output only
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Estimates L-Mode base rate
Description
This function estimates the taxon base rate for an L-Mode curve
Usage
EstimateLMode(curve.x, curve.y, parameters)
Arguments
curve.x |
X values of density |
curve.y |
Y values of density |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
List of base rate estimates:
p.r |
Based on location of left mode |
p.l |
Based on location of right mode |
p.estimate |
mean |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Estimates MAMBAC base rate
Description
This function estimates the taxon base rate for a MAMBAC curve
Usage
EstimateMAMBAC(curve)
Arguments
curve |
MAMBAC curve |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Base rate estimate
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Estimates MAXEIG base rate
Description
This function estimates the taxon base rate for a MAXEIG curve
Usage
EstimateMAXEIG(curve)
Arguments
curve |
MAXEIG curve |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Base rate estiamte
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Estimates MAXSLOPE base rate
Description
This function estimates the taxon base rate for a MAXSLOPE curve
Usage
EstimateMAXSLOPE(curve.x, curve.y)
Arguments
curve.x |
X values of curve |
curve.y |
Y values of curve |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Base rate estimate
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Generates comparison data
Description
This function generates a population of comparison data
Usage
GenerateData(x, n, n.factors = 0, max.trials = 5, initial.multiplier = 1)
Arguments
x |
The data matrix |
n |
Size of population to create |
n.factors |
The number of factors used to reproduce correlations. The default value is 0. |
max.trials |
Maximum number of trials. The default value is 5. |
initial.multiplier |
Size of multiplier to adjust target correlations. The default value is 1. |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Population of comparison data
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Provides analytic specifications
Description
This function provides analytic specifications
Usage
GetSpecifications(parameters)
Arguments
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
This function returns nothing, and provides text output only
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Plots a panel of curves
Description
This function plots a two-panel graph with results for empirical and comparison data
Usage
PlotPanel(x.results, x.dim.results, x.cat.results, parameters, procedure)
Arguments
x.results |
Empirical data results |
x.dim.results |
Dimensional comparison data results |
x.cat.results |
Categorical comparison data results |
parameters |
The data and program parameters |
procedure |
Name of taxometric procedure |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
This function returns nothing, and provides graphical output only
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Calculates CCFIs and base rates for CCFI profile
Description
This function calculates the aggregated CCFI and base rate estimate for one CCFI profile
Usage
ProcessProfile(CCFIs, parameters)
Arguments
CCFIs |
CCFI values across base rates for a single procedure |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
List of aggregated CCFI and base rate estimate
CCFI |
Aggregated CCFI |
p.est |
Base rate estimate |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Removes missing data
Description
This function performs listwise deletion of missing data
Usage
RemoveMissingData(x)
Arguments
x |
The data matrix |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Data after listwise deletion of missing data
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Performs taxometric analyses to generate a CCFI profile
Description
This function performs a series of taxometric analysis using categorical comparison data sets that vary in taxon base rates, and plots a profile of CCFI values across this range of base rates. Results can be assigned to an object to store results; otherwise results will be displayed on-screen.
Usage
RunCCFIProfile(x, seed = 0, min.p = 0.025, max.p = 0.975, num.p = 39,
n.pop = 1e+05, n.samples = 100, reps = 1, MAMBAC = TRUE, assign.MAMBAC = 1,
n.cuts = 50, n.end = 25, MAXEIG = TRUE, assign.MAXEIG = 1, windows = 50,
overlap = 0.9, LMode = TRUE, mode.l = -0.001, mode.r = 0.001, MAXSLOPE = FALSE,
graph = 1, text.file = FALSE, profile = TRUE)
Arguments
x |
Supplied data matrix. Cases missing any data will be removed prior to analysis. |
seed |
Random number seed provided prior to analysis of empirical data as well as prior to generating each population of comparison data. The default value is 0. |
min.p |
Minimum base rate for CCFI profile. The default value is .025. |
max.p |
Maximum base rate for CCFI profile. The default value is .975. |
num.p |
Number of base rates for CCFI profile. The default value is 39. |
n.pop |
Size of the finite populations of categorical and dimensional comparison data. The default value is 100,000. |
n.samples |
Number of comparison data sets of each structure to generate and analyze. The default value is 100. |
reps |
Number of times to resort cases along the input indicator at random and redo the calculations (if tied scores are found), averaging to obtain final results.The default value is 1 if no tied scores are found, and 10 if tied scores are found. |
MAMBAC |
Whether the MAMBAC procedure is performed. The default value is TRUE. |
assign.MAMBAC |
How variables are assigned as input and output variables in the MAMBAC procedure. Variables may be used in all possible input-output pairings (assing.MAMBAC = 1), or variables may be summed to form the input variable (assign.MAMBAC = 2). The default value is 1. |
n.cuts |
The total number of cuts to make along the input variable when performing the MAMBAC procedure. The default value is 25. |
n.end |
The number of cases to set aside at each extreme along the input variable before making the first and last cuts when performing the MAMBAC procedure. The default value is 25. |
MAXEIG |
Whether the MAXEIG procedure is performed. The default value is TRUE if k is >= 3, and FALSE if k < 3. |
assign.MAXEIG |
How variables are assigned as input and output variables in the MAXEIG procedure. Variables may be used in all input-output triplets (assign.MAXEIG = 1), each variable may serve as input once (assign.MAXEIG = 2), or variables may be summed to form the input (assign.MAXEIG = 3). The default value is 1. |
windows |
The nubmer of overlapping windows to use when performing the MAXEIG procedure. The default value is 50. |
overlap |
The amount of overlap between windows when performing the MAXEIG procedure. The default value is .90. |
LMode |
Whether the L-Mode procedure is performed. The default value is TRUE if k is >= 3, and FALSE if k < 3. |
mode.l |
Position beyond which to serach for the left mode when performing the L-Mode procedure. The default value is -.001. |
mode.r |
Position beyond which to serach for the right mode when performing the L-Mode procedure. The default value is .001. |
MAXSLOPE |
Whether the MAXSLOPE procedure is performed. The default value is FALSE if k >= 3, and TRUE if k < 3. |
graph |
Whether to display graphs on screen (1), save as a compressed .jpeg file (2), or save as a high-resolution .tiff file (3). The default value is 1. |
text.file |
Whether to divert text output to a .txt file (T/F). The default value is FALSE. |
profile |
Whether a CCFI profile is generated. The default value is TRUE. |
Details
This function should be called directly by users who wish to perform taxometric analyses to generate a CCFI profile.
Value
This program returns CCFI values, and provides text and graphical output. Note that any CCFI values of 0 represent missing values, as analyses will never yield a CCFI of 0.
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Performs factor analysis
Description
This function performs factor analysis
Usage
RunFactorAnalysis(x, cor.matrix = FALSE, n.factors = 0, max.iter = 50, criterion = 0.01)
Arguments
x |
The data or correlation matrix |
cor.matrix |
Whether x is a correlation matrix. The default is FALSE. |
n.factors |
The number of factors to use. The default value is 0. |
max.iter |
The maximum number of iterations. The default value is 50. |
criterion |
Acceptably small change in h2 between interations. The default value is .01. |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
List of factor loadings and number of factors
loadings |
The factor loadings |
factors |
The number of factors |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Performs L-Mode
Description
This function performs the L-Mode analysis
Usage
RunLMode(x)
Arguments
x |
The data matrix |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
L-Mode curve:
curve.x |
X values of curve |
curve.y |
Y values of curve |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
References
Waller, N.G., & Meehl, P.E. (1998). Multivariate taxometric procedures: Distinguishing types from continua. Thousand Oaks, CA, US: Sage Publications, Inc.
Performs MAMBAC
Description
This function performs the MAMBAC analysis
Usage
RunMAMBAC(x, parameters)
Arguments
x |
The data matrix |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Panel of MAMBAC curves
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
References
Meehl, P.E., & Yonce, L.J. (1994). Taxometric analysis: I. Detecting taxonomy with two quantitative indicators using means above and below a sliding cut (MAMBAC procedure). Psychological Reports, 74(3, Pt 2), 1059-1274.
Performs MAXEIG
Description
This function performs the MAXEIG analysis
Usage
RunMAXEIG(x, parameters)
Arguments
x |
The data matrix |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Panel of MAXEIG curves:
curve.x |
X values of curve |
curve.y |
Y values of curve |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
References
Waller, N.G., & Meehl, P.E. (1998). Multivariate taxometric procedures: Distinguishing types from continua. Thousand Oaks, CA, US: Sage Publications, Inc.
Performs MAXSLOPE
Description
This function performs the MAXSLOPE analysis
Usage
RunMAXSLOPE(x)
Arguments
x |
The data matrix |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
Panel of MAXSLOPE curves
curve.x |
X values of curve |
curve.y |
Y values of curve |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
References
Grove, W.M., & Meehl, P.E. (1993). Simple regression-based procedures for taxometric investigations. Psychological Reports, 73, 707-737.
Runs taxometric procedures for empirical data
Description
This function runs the MAMBAC, MAXEIG, L-Mode, and MAXSLOPE analyses for empirical data
Usage
RunProcedures(x, parameters)
Arguments
x |
The data matrix |
parameters |
The data and program parameters |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
A list of curve-level data for each procedure performed:
MAMBAC |
MAMBAC curve |
MAXEIG.x |
X values of MAXEIG curve |
MAXEIG.y |
Y values of MAXEIG curve |
LMode.x |
X values of LMode curve |
LMode.y |
Y values of LMode curve |
MAXSLOPE.x |
X values of MAXSLOPE curve |
MAXSLOPE.y |
Y values of MAXSLOPE curve |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Runs taxometric procedures for comparison data
Description
This function runs the MAMBAC, MAXEIG, L-Mode, and MAXSLOPE analyses for comparison data
Usage
RunProceduresComp(x, parameters)
Arguments
x |
The data matrix |
parameters |
The data and program parameters. |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
A list of averaged curves for each procedure performed:
MAMBAC |
MAMBAC curve |
MAXEIG.x |
X values of MAXEIG curve |
MAXEIG.y |
Y values of MAXEIG curve |
LMode.x |
X values of LMode curve |
LMode.y |
Y values of LMode curve |
MAXSLOPE.x |
X values of MAXSLOPE curve |
MAXSLOPE.y |
Y values of MAXSLOPE curve |
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Taxometric analysis for a sample of data
Description
Performs taxometric analysis for a sample of data and provides text (analytic specifications, CCFI values, base rate estimates) and graphical (panels of empirical data curves superimposed above comparison data curves) output. By default, the function will run MAMBAC, MAXEIG, and L-Mode, unless only 2 variables are provided, in which case the program will run MAMBAC and MAXSLOPE. Results can be assigned to an object to store results; otherwise results will be displayed on-screen.
Usage
RunTaxometrics(x, seed = 0, n.pop = 1e+05, n.samples = 100, reps = 1,
MAMBAC = TRUE, assign.MAMBAC = 1, n.cuts = 50, n.end = 25, MAXEIG =
TRUE, assign.MAXEIG = 1, windows = 50, overlap = 0.9, LMode = TRUE, mode.l =
-0.001, mode.r = 0.001, MAXSLOPE = FALSE, graph = 1)
Arguments
x |
The supplied data matrix. Cases missing any data will be removed prior to analysis. |
seed |
Random number seed provided prior to analysis of empirical data as well as prior to generating each population of comparison data. The default value is 0. |
n.pop |
Size of the finite populations of categorical and dimensional comparison data. The default value is 100,000. |
n.samples |
Number of comparison data sets of each structure to generate and analyze. The default value is 100. |
reps |
Number of times to resort cases along the input indicator at random and redo the calculations (if tied scores are found), averaging to obtain final results.The default value is 1 if no tied scores are found, and 10 if tied scores are found. |
MAMBAC |
Whether the MAMBAC procedure is performed. The default value is TRUE. |
assign.MAMBAC |
How variables are assigned as input and output variables in the MAMBAC procedure. Variables may be used in all possible input-output pairings (assing.MAMBAC = 1), or variables may be summed to form the input variable (assign.MAMBAC = 2). The default value is 1. |
n.cuts |
The total number of cuts to make along the input variable when performing the MAMBAC procedure. The default value is 25. |
n.end |
The number of cases to set aside at each extreme along the input variable before making the first and last cuts when performing the MAMBAC procedure. The default value is 25. |
MAXEIG |
Whether the MAXEIG procedure is performed. The default value is TRUE if k is >= 3, and FALSE if k < 3. |
assign.MAXEIG |
How variables are assigned as input and output variables in the MAXEIG procedure. Variables may be used in all input-output triplets (assign.MAXEIG = 1), each variable may serve as input once (assign.MAXEIG = 2), or variables may be summed to form the input (assign.MAXEIG = 3). The default value is 1. |
windows |
The nubmer of overlapping windows to use when performing the MAXEIG procedure. The default value is 50. |
overlap |
The amount of overlap between windows when performing the MAXEIG procedure. The default value is .90. |
LMode |
Whether the L-Mode procedure is performed. The default value is TRUE if k is >= 3, and FALSE if k < 3. |
mode.l |
Position beyond which to serach for the left mode when performing the L-Mode procedure. The default value is -.001. |
mode.r |
Position beyond which to serach for the right mode when performing the L-Mode procedure. The default value is .001. |
MAXSLOPE |
Whether the MAXSLOPE procedure is performed. The default value is FALSE if k >= 3, and TRUE if k < 3. |
graph |
Whether to display graphs on screen (1), save as a compressed .jpeg file (2), or save as a high-resolution .tiff file (3). The default value is 1. |
Details
This function should be called directly by users who wish to perform taxometric analyses for a sample of data.
Value
This program returns CCFI values, and provides text and graphical output. Note that any CCFI values of 0 represent missing values, as analyses will never yield a CCFI of 0.
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>
Summarizes distribution
Description
This function calculates the sample mean, standard deviation, skewness, and kurtosis
Usage
SummarizeDist(x)
Arguments
x |
The data vector |
Details
Called by higher-order functions; users do not need to call this function directly.
Value
The sample mean, standard deviation, skewness, and kurtosis of x.
Author(s)
John Ruscio <ruscio@tcnj.edu> and Shirley Wang <shirleywang@g.harvard.edu> Maintainer: John Ruscio <ruscio@tcnj.edu>