power.bmem: Conducting power analysis

In bmemLavaan: Mediation Analysis with Missing Data and Non-Normal Data

Conducting power analysis

Description

A comprehensive power analysis function, it can conduct power analysis based on normal, bootstrap and robust Huber-type confidence intervals.

Usage

power.bmem(model, method="normal", nobs = 100, nrep = 1000, nboot = 1000, 
alpha = 0.95, skewness = NULL, kurtosis = NULL, ovnames = NULL,  
ci='perc', boot.type='default', 
se = "default", estimator = "default", parallel = FALSE, 
ncore = 1, verbose=TRUE, ...)

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. It also specify the indirect or other composite effects using lavaan notation. model<-' math ~ c*ME+start(0)*ME + b*HE+start(.39)*HE HE ~ a*ME+start(.39)*ME ab := a*b abc := a*b + c '
method	Type of confidence intervals based on. Must be "normal", "boot" or "robust", which correspond to the normal, bootstrap or robust Huber-type confidence interval, respectively.
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 bootstrap replicates. It's only required when bootstrap method is used.
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	Whether to use parallel method to calculate.
ncore	Number of cores to be used in parallel.
ci	Type of bootstrap confidence intervals. By default, the percentile one is used. otherwise get the bias-corrected one. It's only required when bootstrap method is used.
boot.type	Type of bootstrap method. By default, the nonparametric one is used. Changing it to "BS" to use the Bollen-Stine method. It's only required when bootstrap method is used.
verbose	Whether to print power information.
...	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

Author(s)

Zhiyong Zhang, Shuigen Ming and Lijuan Wang

References

Zhang, Z. Monte Carlo based statistical power analysis for mediation models: methods and software. Behav Res 46, 1184-1198 (2014). doi: 10.3758/s13428-013-0424-0

Examples

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

N <- 50

system.time(power_normal <- power.bmem(ex1model, method = "normal", nobs = N, 
  nrep=100, parallel=TRUE, skewness=c(-.3, -.7, 1.3), 
  kurtosis=c(1.5, 0, 5), ovnames=c('ME', 'HE', 'math'), ncore=8))
summary(power_normal)

bmemLavaan documentation built on May 28, 2022, 5:06 p.m.