Power for model parameters
Apply Satorra & Saris (1985) method for chi-squared power analysis.
SSpower(powerModel, n, nparam, popModel, mu, Sigma, fun = "cfa", alpha = 0.05, ...)
powerModel |
lavaan |
n |
|
nparam |
|
popModel |
lavaan |
mu |
numeric or list. For a single-group model, a vector of population
means. For a multigroup model, a list of vectors (one per group). If
|
Sigma |
matrix or list. For a single-group model, a population covariance matrix. For a multigroup model, a list of matrices (one per group). If missing, popModel will be used to generate a model-implied Sigma. |
fun |
character. Name of lavaan function used to fit |
alpha |
Type I error rate used to set a criterion for rejecting H0. |
... |
additional arguments to pass to |
Specify all non-zero parameters in a population model, either by using
lavaan syntax (popModel) or by submitting a population covariance
matrix (Sigma) and optional mean vector (mu) implied by the
population model. Then specify an analysis model that constrains at least
one nonzero parameter to an incorrect value. Note the number in the
nparam argument.
Alexander M. Schoemann (East Carolina University; schoemanna@ecu.edu)
Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50, 83–90. doi: 10.1007/BF02294150
## Specify population values. Note every paramter has a fixed value.
modelP <- '
f1 =~ .7*V1 + .7*V2 + .7*V3 + .7*V4
f2 =~ .7*V5 + .7*V6 + .7*V7 + .7*V8
f1 ~~ .3*f2
f1 ~~ 1*f1
f2 ~~ 1*f2
V1 ~~ .51*V1
V2 ~~ .51*V2
V3 ~~ .51*V3
V4 ~~ .51*V4
V5 ~~ .51*V5
V6 ~~ .51*V6
V7 ~~ .51*V7
V8 ~~ .51*V8
'
## Specify analysis model. Note parameter of interest f1~~f2 is fixed to 0.
modelA <- '
f1 =~ V1 + V2 + V3 + V4
f2 =~ V5 + V6 + V7 + V8
f1 ~~ 0*f2
'
## Calculate power
SSpower(powerModel = modelA, popModel = modelP, n = 150, nparam = 1,
std.lv = TRUE)
## Get power for a range of sample sizes
Ns <- seq(100, 500, 40)
Power <- rep(NA, length(Ns))
for(i in 1:length(Ns)) {
Power[i] <- SSpower(powerModel = modelA, popModel = modelP,
n = Ns[i], nparam = 1, std.lv = TRUE)
}
plot(x = Ns, y = Power, type = "l", xlab = "Sample Size")
## Specify second population to calculate power for multigroup model
popMoments1 <- fitted(cfa(modelP))
modelP2 <- '
f1 =~ .7*V1 + .7*V2 + .7*V3 + .7*V4
f2 =~ .7*V5 + .7*V6 + .7*V7 + .7*V8
f1 ~~ .5*f2 ## higher correlation in Group 2
f1 ~~ 1*f1
f2 ~~ 1*f2
V1 ~~ .51*V1
V2 ~~ .51*V2
V3 ~~ .51*V3
V4 ~~ .51*V4
V5 ~~ .51*V5
V6 ~~ .51*V6
V7 ~~ .51*V7
V8 ~~ .51*V8
'
popMoments2 <- fitted(cfa(modelP2))
modelA2 <- '
f1 =~ V1 + V2 + V3 + V4
f2 =~ V5 + V6 + V7 + V8
f1 ~~ c(0, 0)*f2
'
mu <- list(popMoments1$mean, popMoments2$mean) # ignored if NULL
Sigma <- list(popMoments1$cov, popMoments2$cov)
SSpower(powerModel = modelA2, mu = mu, Sigma = Sigma,
n = c(60, 65), nparam = 2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.