regsem (version 0.1.2)

multi_optim: Multiple starts for Regularized Structural Equation Modeling

Description

Multiple starts for Regularized Structural Equation Modeling

Usage

multi_optim(model, max.try = 10, lambda, LB = -Inf, UB = Inf, type,
  optMethod = "nlminb", gradFun = "ram", pars_pen = NULL,
  diff_par = NULL, hessFun = "none", verbose = TRUE, warm.start = FALSE,
  Start2 = NULL, tol = 1e-06, max.iter = 50000)

Arguments

model

Lavaan output object. This is a model that was previously run with any of the lavaan main functions: cfa(), lavaan(), sem(), or growth(). It also can be from the efaUnrotate() function from the semTools package. Currently, the parts of the model which can

max.try

number of starts to try before convergence.

lambda

Penalty value. Note: higher values will result in additional convergence issues.

LB

lower bound vector. Note: This is very important to specify when using regularization. It greatly increases the chances of converging.

UB

Upper bound vector

type

Penalty type. Options include "none", "lasso", "ridge", and "diff_lasso". diff_lasso penalizes the discrepency between parameter estimates and some pre-specified values. The values to take the deviation from are specified in diff_par.

optMethod

Solver to use. Recommended options include "nlminb" and "optimx". Note: for "optimx", the default method is to use nlminb. This can be changed in subOpt.

gradFun

Gradient function to use. Recommended to use "ram", which refers to the method specified in von Oertzen & Brick (2014). The "norm" procedure uses the forward difference method for calculating the hessian. This is slower and less accurate.

pars_pen

Parameter indicators to penalize. If left NULL, by default, all parameters in the A matrix outside of the intercepts are penalized when lambda > 0 and type != "none".

diff_par

Parameter values to deviate from. Only used when type="diff_lasso".

hessFun

Hessian function to use. Recommended to use "ram", which refers to the method specified in von Oertzen & Brick (2014). The "norm" procedure uses the forward difference method for calculating the hessian. This is slower and less accurate.

verbose

Whether to print iteration number.

warm.start

Whether start values are based on previous iteration. This is not recommended.

Start2

Provided starting values. Not required

tol

absolute tolerance for convergence.

max.iter

max iterations for optimization.

Examples

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

fit1 <- multi_optim(outt,max.try=40,
                   lambda=0.1,type="lasso",
                   gradFun="ram")


# growth model
model <- ' i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
          s =~ 0*t1 + s1*t2 + s2*t3 + 3*t4 '
fit <- growth(model, data=Demo.growth)
summary(fit)
fitmeasures(fit)
fit3 <- multi_optim(fit,lambda=0.2,type="ridge",gradFun="none")
summary(fit3)