Utility Functions in CDM — CDM-utilities • CDM

Utility functions in CDM.

## 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 \(\lambda\)
regular_tau: Regularization parameter \(\tau\)
regular_alpha: Regularization parameter \(\alpha\)
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