EAMM {pamm} | R Documentation |
Given a specific sample size (fixed number of group and replicates per group), the function simulate different variance-covariance structure and assess p-values and power of random intercept and random slope using lmer.
EAMM(numsim, group, repl, fixed = c(0, 1, 0), VI = seq(0.05, 0.95,0.05), VS = seq(0.05, 0.5, 0.05), CoIS = 0, relIS = "cor")
numsim |
number of simulation for each step |
group |
number of group (individuals) |
repl |
number of replicates (observations) per group |
fixed |
vector of lenght 3 with mean, variance and estimate of fixed effect to simulate. default value: c(0,1,0) |
VI |
variance component of intercept (ID). Could be specified as a vector. default value: seq(0.05,0.95,0.05) |
VS |
variance component of slope (ID*fixed effect interaction). Could be specified as a vector. default value :seq(0.05,0.5,0.05)) |
CoIS |
value of correlation or covariance between random intercept and random slope |
relIS |
"cor" or "cov" set the type of relation give in CoIS. By default the relation is set to correlation |
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. Residual variance (e) is calculted as 1-VI.
data frame reporting estimated P-values and power with CI for random intercept and random slope
the simulation is based on a balanced data set with unrelated group
Julien Martin
...
## Not run: # ours=EAMM(numsim=10,group=100,repl=4,fixed=c(0,1,1),VI=seq(0.05,0.3,0.05), # VS=seq(0.05,0.2,0.05)) # plot(ours, "both") ## End(Not run)