simsem (version 0.5-16)

inspect: Extract information from a simulation result

Description

Extract information from a simulation result

Arguments

object

The target '>SimResult object

what

The target component to be extracted. Please see details below.

improper

Specify whether to include the information from the replications with improper solutions

nonconverged

Specify whether to include the information from the nonconvergent replications

Value

The target information depending on the what argument

Details

Here are the list of information that can be specified in the what argument. The items starting with * are the information that the improper and nonconverged arguments are not applicable.

• *"modeltype": The type of the simulation result

• *"nrep": The number of overall replications, including converged and nonconverged replications

• "param": Parameter values (equivalent to the getPopulation function)

• "stdparam": Standardized parameter values (equivalent to the getPopulation function with std = TRUE)

• "coef": Parameter estimates (equivalent to the coef method)

• "se": Standard errors

• "fit": Fit indices

• "misspec": Misspecified parameter values

• "popfit": Population misfit

• "fmi1": Fraction missings type 1

• "fmi2": Fraction missings type 2

• "std": Standardized Parameter Estimates

• "stdse": Standard Errors of Standardized Values

• "cilower": Lower bounds of confidence intervals

• "ciupper": Upper bounds of confidence intervals

• "ciwidth": Widths of confidence intervals

• *"seed": Seed number (equivalent to the summarySeed function)

• "ngroup": Sample size of each group

• "ntotal": Total sample size

• "mcar": Percent missing completely at random

• "mar": Percent missing at random

• "extra": Extra output from the outfun argument from the sim function)

• *"time": Time elapsed in running the simulation (equivalent to the summaryTime function)

• *"converged": Convergence of each replication

See Also

'>SimResult for the object input

Examples

# NOT RUN {
loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
LY <- bind(loading, 0.7)

latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPS <- binds(latent.cor, 0.5)

RTE <- binds(diag(6))

VY <- bind(rep(NA,6),2)

CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType = "CFA")

# In reality, more than 5 replications are needed.
Output <- sim(5, CFA.Model, n=200)
inspect(Output, "coef")
inspect(Output, "param")
inspect(Output, "se", improper = TRUE, nonconverged = TRUE)
# }