CDM-utilities: Utility Functions in 'CDM'
In alexanderrobitzsch/CDM: Cognitive Diagnosis Modeling

CDM-utilities

R Documentation

Utility Functions in CDM

Description

Utility functions in CDM.

Usage

## requireNamespace with package message for needed installation
CDM_require_namespace(pkg)
## attach internal function in a package
cdm_attach_internal_function(pack, fun)

## print function in summary
cdm_print_summary_data_frame(obji, from=NULL, to=NULL, digits=3, rownames_null=FALSE)
## print summary call
cdm_print_summary_call(object, call_name="call")
## print computation time
cdm_print_summary_computation_time(object, time_name="time", time_start="s1",
         time_end="s2")

## string vector of matrix entries
cdm_matrixstring( matr, string )

## mvtnorm::rmvnorm with vector conversion for n=1
CDM_rmvnorm(n, mean=NULL, sigma, ...)
## fit univariate and multivariate normal distribution
cdm_fit_normal(x, w)

## fit unidimensional factor analysis by unweighted least squares
cdm_fa1(Sigma, method=1, maxit=50, conv=1E-5)

## another rbind.fill implementation
CDM_rbind_fill( x, y )
## fills a vector row-wise into a matrix
cdm_matrix2( x, nrow )
## fills a vector column-wise into a matrix
cdm_matrix1( x, ncol )

## SCAD thresholding operator
cdm_penalty_threshold_scad(beta, lambda, a=3.7)
## lasso thresholding operator
cdm_penalty_threshold_lasso(val, eta )
## ridge thresholding operator
cdm_penalty_threshold_ridge(beta, lambda)
## elastic net threshold operator
cdm_penalty_threshold_elnet( beta, lambda, alpha )
## SCAD-L2 thresholding operator
cdm_penalty_threshold_scadL2(beta, lambda, alpha, a=3.7)
## truncated L1 penalty thresholding operator
cdm_penalty_threshold_tlp( beta, tau, lambda )
## MCP thresholding operator
cdm_penalty_threshold_mcp(beta, lambda, a=3.7)

## general thresholding operator for regularization
cdm_parameter_regularization(x, regular_type, regular_lam, regular_alpha=NULL,
         regular_tau=NULL )
## values of penalty function
cdm_penalty_values(x, regular_type, regular_lam, regular_tau=NULL,
       regular_alpha=NULL)
## thresholding operators regularization
cdm_parameter_regularization(x, regular_type, regular_lam, regular_alpha=NULL,
       regular_tau=NULL)

## utility functions for P-EM acceleration
cdm_pem_inits(parmlist)
cdm_pem_inits_assign_parmlist(pem_pars, envir)
cdm_pem_acceleration( iter, pem_parameter_index, pem_parameter_sequence, pem_pars,
      PEM_itermax, parmlist, ll_fct, ll_args, deviance.history=NULL )
cdm_pem_acceleration_assign_output_parameters(res_ll_fct, vars, envir, update)

## approximation of absolute value function and its derivative
abs_approx(x, eps=1e-05)
abs_approx_D1(x, eps=1e-05)

## information criteria
cdm_calc_information_criteria(ic)
cdm_print_summary_information_criteria(object, digits_crit=0, digits_penalty=2)

## string pasting
cat_paste(...)

Arguments

`pkg`	An R package
`pack`	An R package
`fun`	An R function
`obji`	Object
`from`	Integer
`to`	Integer
`digits`	Number of digits used for printing
`rownames_null`	Logical
`call_name`	Character
`time_name`	Character
`time_start`	Character
`time_end`	Character
`matr`	Matrix
`string`	String
`object`	Object
`n`	Integer
`mean`	Mean vector or matrix if separate means for cases are provided. In this case, `n` can be missing.
`sigma`	Covariance matrix
`...`	More arguments to be passed (or a list of arguments)
`x`	Matrix or vector
`y`	Matrix or vector
`w`	Vector of sampling weights
`nrow`	Integer
`ncol`	Integer
`Sigma`	Covariance matrix
`method`	Method `1` indicates estimation of different item loadings, method `2` estimation of same item loadings.
`maxit`	Maximum number of iterations
`conv`	Convergence criterion
`beta`	Numeric
`lambda`	Regularization parameter
`alpha`	Regularization parameter
`a`	Parameter
`tau`	Regularization parameter
`val`	Numeric
`eta`	Regularization parameter
`regular_type`	Type of regularization
`regular_lam`	Regularization parameter λ
`regular_tau`	Regularization parameter τ
`regular_alpha`	Regularization parameter α
`parmlist`	List containing parameters
`pem_pars`	Vector containing parameter names
`envir`	Environment
`update`	Logical
`iter`	Iteration number
`pem_parameter_index`	List with parameter indices
`pem_parameter_sequence`	List with updated parameter sequence
`PEM_itermax`	Maximum number of iterations for PEM
`ll_fct`	Name of log-likelihood function
`ll_args`	Arguments of log-likelihood function
`deviance.history`	Deviance history, a data frame.
`res_ll_fct`	Result of maximized log-likelihood function
`vars`	Vector containing parameter names
`eps`	Numeric
`ic`	List
`digits_crit`	Integer
`digits_penalty`	Integer