SSF {pamm}R Documentation

SIMULATION FUNCTION TO ASSESS POWER OF MIXED MODELS

Description

Given a specific total number of observations and variance-covariance structure for random effect, the function simulates different association of number of group and replicates (giving the specified sample size) and assess p-values and power of random intercept and random slope

Usage

SSF(numsim, tss, nbstep = 10, randompart, fixed = c(0, 1, 0), exgr = NA, exrepl = NA)

Arguments

numsim number of simulation for each step
tss total sample size (nb group * nb replicates)
nbstep number of group*replicates associations to simulate
randompart vector of lenght 4 or 5 with 1) variance component of intercept (VI); 2) variance component of slope (VS); 3) residual variance (VR); 4) relation between random intercept and random slope; 5) "cor" or "cov" determine id the relation between I ans S is correlation or covariance (set to correlation by default)
fixed vector of lenght 3 with mean, variance and estimate of fixed effect to simulate
exgr a vector specifying minimum and maximum value for number of group. Default;c(2,tss/2)
exrepl a vector specifying minimum and maximum value for number of replicates. Default;c(2,tss/2)

Details

P-values for random effects are estimated using a log-likelihood ratio test between two models with and without the effect. Power represent the percentage of simulations providing a significant p-value for a given random structure

Value

data frame reporting estimated P-values and power with CI for random intercept and random slope

Warning

the simulation is based on a balanced data set with unrelated group

Author(s)

Julien Martin

References

...

See Also

PAMM,EAMM,plot.SSF

Examples

## Not run:
#  ours<- SSF(10,200,10,c(0.4,0.1,0.6,0))
#  plot(ours)
## End(Not run)

[Package pamm version 0.2 Index]