PAMM {pamm}R Documentation

SIMULATION FUNCTION TO ASSESS POWER OF MIXED MODELS FOR RANDOM EFFECTS

Description

Given a specific varaince-covariance structure for random effect, the function simulate different group size and assess p-values and power of random intercept and random slope

Usage

PAMM(numsim, group, repl, randompart, fixed = c(0, 1, 0))

Arguments

numsim number of simulation for each step
group number of group (individuals). Could be specified as a vector
repl number of replicates (observations) per group . Could be specified as a vector
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 if the relation 4) between I ans S is a correlation or a covariance (set to correlation by default)
fixed vector with mean, variance and estimate of fixed effect to simulate. c(0,1,0) by default

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

EAMM,SSF,plot.PAMM

Examples

## Not run:
#  ours=PAMM(numsim=10,group=c(seq(10,50,10),100),repl=c(2,4,6),
#           randompart=c(0.4,0.1,0.5,0.1),fixed=c(0,1,0.7))       
#  plot(ours,"both")
## End(Not run)

[Package pamm version 0.2 Index]