Usage
ss.power.sem(F.ML = NULL, df = NULL, RMSEA.null = NULL, RMSEA.true = NULL,
F.full = NULL, F.res = NULL, RMSEA.full = NULL, RMSEA.res = NULL,
df.full = NULL, df.res = NULL, alpha = 0.05, power = 0.8)
Arguments
F.ML
The true maximum likelihood fit function value in the population for the model of interest. Leave this argument NULL
if you are doing nested model significance tests.
df
The degrees of freedom of the model of interest. Leave this argument NULL
if you are doing nested model significance tests.
RMSEA.null
The model's population RMSEA under the null hypothesis. Leave this argument NULL
if you are doing nested model significance tests.
RMSEA.true
The model's population RMSEA under the alternative hypothesis. This should be the model's
true population RMSEA value. Leave this argument NULL
if you are doing nested model significance tests.
F.full
The maximum likelihood fit function value for the full model.
F.res
The maximum likelihood fit function value for the restricted model.
RMSEA.full
The population RMSEA value for the full model.
RMSEA.res
The population RMSEA value for the restricted model.
df.full
The degrees of freedom for the full model.
df.res
The degrees of freedom for the restricted model.
alpha
The Type I error rate.
