simPower | R Documentation |
Power simulation of a likelihood ratio test between two models given simulated data from a true model in R/OpenMx Assume you have a H1 model that test whether a pretest and a posttest score
simPower(h0Model, h1Model, populationModel, N = c(20, 100),
repetitions = 100, keepModels = FALSE, simfunc = simulateData,
simargs = NULL)
h0Model |
Model representing the null hypothesis |
h1Model |
Model representing the alternative hypothesis |
N |
Sample size. For RAM models, this is either (1) a scalar representing a fixed sample size, (2) a list with two items representing a minimum and a maximum for randomly drawn sample sizes (when sample size is the dependent variable), or (3) for multiple group models, this is a named list of sample sizes for the sub models where the names must match the submodels' names. |
repetitions |
Number of Monte Carlo trials |
keepModels |
Boolean. Keep models from all trials for later inspection? |
simfunc |
Function to simulate data. Default is based on multivariate normal distribution. |
simargs |
Extra arguments to be passed to the method for simulating data. |
populationModel |
True |
population model used to generate data from.
psim |
SimPower result object |
prng |
Range for plotting p values |
target.power |
If not null, draw a line to indicate sample size for a specified target power in a power curve plot. |
lw |
line width for power curve |
lty |
line type for power curve |
: Andreas Brandmaier
simPowerZeroRestriction
simPower(h0Model,h1Model, populationModel, N=c(20,100),repetitions=100, keepModels=F)
plot(psim, prng=seq(0.4,0.95,0.05), target.power=NULL, lw=2, lty=1,
xlab="sample size", ylab="statistical power",
main="Monte Carlo Power Simulation", add=F,...)
print(psim, ...)
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