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pamm (version 0.5)

SSF: 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), intercept = 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
fixed
vector of lenght 3 with mean, variance and estimate of fixed effect to simulate
intercept
a numeric value giving the expected intercept value. Default:0
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)

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

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

References

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See Also

PAMM, EAMM, plot.SSF

Examples

Run this code
ours<- SSF(100,200,10,c(0.4,0.1,0.6,0))
  plot(ours)

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