regsem (version 0.1.2)

cv_regsem: The main function that ties together and runs the models.

Description

The main function that ties together and runs the models.

Usage

cv_regsem(model, n.lambda = 100, mult.start = TRUE, multi.iter = 100,
  jump = 0.002, type = "none", fit.ret = c("rmsea", "BIC"),
  fit.ret2 = c("train", "test"), data = NULL, optMethod = "nlminb",
  gradFun = "ram", hessFun = "none", parallel = "no", Start = "default",
  subOpt = "nlminb", longMod = F, optNL = "NLOPT_LN_NEWUOA_BOUND",
  fac.type = "cfa", matrices = "extractMatrices", pars_pen = NULL,
  diff_par = NULL, LB = -Inf, UB = Inf, calc = "normal", tol = 1e-10,
  max.iter = 50000, missing = "listwise", ...)

Arguments

model

lavaan output object.

n.lambda

number of penalization values to test.

mult.start

Logical. Whether to use multi_optim() (TRUE) or regsem() (FALSE).

multi.iter

number of random starts for multi_optim

jump

Amount to increase penalization each iteration.

type

penalty type.

fit.ret

Fit indices to return.

fit.ret2

Return fits from just train sample?

data

Optional dataframe. Only required for missing="fiml".

optMethod

solver to use.

gradFun

gradient function to use.

hessFun

hessian function to use.

parallel

whether to parallelize the processes?

Start

type of starting values to use.

subOpt

type of optimization to use in the optimx package.

longMod

longitudinal model?

optNL

type of optimization to use in the NLopt package.

fac.type

using cfa or efa type of model.

matrices

function to use for extracting RAM matrices.

pars_pen

parameter indicators to penalize.

diff_par

parameter values to deviate from.

LB

lower bound vector.

UB

upper bound vector

calc

type of calc function to use with means or not.

tol

absolute tolerance for convergence.

max.iter

max iterations for optimization.

missing

How to handle missing data. Current options are "listwise" and "fiml".

...

Any additional arguments to pass to regsem() or multi_optim().

Examples

library(lavaan)
HS <- data.frame(scale(HolzingerSwineford1939[,7:15]))
mod <- '
f =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
'
outt = cfa(mod,HS)

cv.out = cv_regsem(outt,type="ridge",gradFun="none",n.lambda=100)