PAMM {pamm} | R Documentation |
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
PAMM(numsim, group, repl, randompart, fixed = c(0, 1, 0))
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 |
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,
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
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## 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)