power.boot {bmem} R Documentation

## Conducting power analysis based on bootstrap

### Description

Different from power.basic, this function conduct power analysis based on the bootstrap method.

### Usage

power.boot(model, indirect = NULL, nobs, nrep = 1000, nboot = 1000,
alpha = 0.95, skewness = NULL, kurtosis = NULL, ovnames = NULL,
ci='default', boot.type='default',
se = "default", estimator = "default", parallel = "no",
ncore = 1,  ...)


### Arguments

 model A model specified using lavaan notation and above. See model.syntax for basic model specification. For the power analysis, the population parameter values should be provided in the following way. For example, the coefficient between math and HE is .39. Then it is specified as start(.39). If the parameter will be referred in the mediation effect, a label should be given as a modifier as b*HE+start(.39)*HE. model<-' math ~ c*ME+start(0)*ME + b*HE+start(.39)*HE HE ~ a*ME+start(.39)*ME ' indirect The indirect or other composite effects are specified in the following way indirect<-' ab: = a*b abc := a*b + c ' nobs Number of observations for power analysis. If it is a vector, multiple group analysis will be conducted. nrep Number of replications for Monte Carlo simulation. At least 1,000 is recommended. nboot Number of bootstraps to conduct. alpha The alpha level is used to obtain the confidence interval for model parameters. skewness A vector to give the skewness for the observed variables. kurtosis A vector to give the kurtosis for the observed variables. ovnames A vector to give the variable names for the observed variables. This is only needed when the skewness and kurtosis are provided. The skewness, kurtosis and variable names should be in the same order. se How to calculate the standard error, for example, robust standard error can be specified using se="robust". estimator Estimation methods to be used here. parallel Parallel methods, snow or multicore, can be used here. ncore Number of cores to be used in parallel. By defautl, the maximum number of cores are used. ci Type of bootstrap confidence intervals. By default, the percentile one is used. To get the bias-corrected one, use ci='BC' boot.type Type of bootstrap method. By default, the nonparametric one is used. Changing it to "BS" to use the Bollen-Stine method. ... Other named arguments for lavaan can be passed here.

### Value

 power power for all parameters and required ones in the model coverage coverage probability pop.value Population parameter values results A list to give all intermediate results data The last data set generated for checking purpose

### Examples


ex1model<-'
math ~ c*ME+start(0)*ME + b*HE+start(0.39)*HE
HE ~ a*ME+start(0.39)*ME
'

indirect<-'ab:=a*b'

N<-50

## change nrep and nboot to at least 1000 in real analysis
system.time(boot.non.normal<-power.boot(ex1model, indirect, N,
nrep=100, nboot=100, skewness=c(-.3, -.7, 1.3),
kurtosis=c(1.5, 0, 5), ovnames=c('ME', 'HE', 'math'),  ci='percent', boot.type='simple'))
summary(boot.non.normal)



[Package bmem version 2.1 Index]