CDM-utilities: Utility Functions in 'CDM'

In CDM: Cognitive Diagnosis Modeling

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

CDM documentation built on Aug. 25, 2022, 5:08 p.m.