Performs some preparations common to all types of power analyses.
powerPrepare(
type = NULL,
effect = NULL,
effect.measure = NULL,
alpha = NULL,
beta = NULL,
power = NULL,
abratio = NULL,
N = NULL,
df = NULL,
p = NULL,
SigmaHat = NULL,
Sigma = NULL,
muHat = NULL,
mu = NULL,
fittingFunction = "ML",
simulatedPower = FALSE,
modelH0 = NULL,
nReplications = NULL,
minConvergenceRate = NULL,
lavOptions = NULL
)list
type of power analysis
effect size specifying the discrepancy between H0 and H1 (a list for multiple group models; a vector of length 2 for effect-size differences)
type of effect, one of "F0", "RMSEA", "Mc", "GFI", "AGFI"
alpha error
beta error; set either beta or power
power (=1 - beta); set either beta or power
the ratio of alpha to beta
the number of observations (a list for multiple group models)
the model degrees of freedom
the number of observed variables, required for effect.measure = "GFI" and effect.measure = "AGFI"
model implied covariance matrix (a list for multiple group models). Use in conjunction with Sigma to define effect and effect.measure.
observed (or population) covariance matrix (a list for multiple group models). Use in conjunction with SigmaHat to define effect and effect.measure.
model implied mean vector
observed (or population) mean vector
one of 'ML' (default), 'WLS', 'DWLS', 'ULS'. Defines the discrepancy function used to obtain Fmin.
whether to perform a simulated (TRUE) (rather than analytical, FALSE) power analysis.
for simulated power: lavaan model string defining the (incorrect) analysis model.
for simulated power: number of random samples drawn.
for simulated power: the minimum convergence rate required
for simulated power: a list of additional options passed to lavaan, e. g., list(estimator = 'mlm') to request robust ML estimation.