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, n.X, autocorr.X,
X.dist, intercept, heteroscedasticity = c("null"),
ftype="lmer", mer.sim=FALSE)
data frame reporting estimated P-values and power with CI for random intercept and random slope
number of simulation for each step
number of group. Could be specified as a vector
number of replicates per group . Could be specified as a vector
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. Default: "cor"
vector with mean, variance and estimate of fixed effect to simulate. Default: c(0, 1, 0)
number of different values to simulate for the fixed effect (covariate).
If NA
, all values of X are independent between groups. If the value specified
is equivalent to the number of replicates per group, repl
, then all groups
are observed for the same values of the covariate. Default: NA
correlation between two successive covariate value for a group. Default: 0
specify the distribution of the fixed effect. Only "gaussian" (normal distribution) and
"unif" (uniform distribution) are accepted actually. Default: "gaussian"
a numeric value giving the expected intercept value. Default:0
a vector specifying heterogeneity in residual variance
across X. If c("null")
residual variance is homogeneous across X. If
c("power",t1,t2)
models heterogeneity with a constant plus power variance function.
Letting \(v\) denote the variance covariate and \(\sigma^2(v)\)
denote the variance function evaluated at \(v\), the constant plus power
variance function is defined as \(\sigma^2(v) = (\theta_1 + |v|^{\theta_2})^2\),
where \(\theta_1,\theta_2\) are the variance function coefficients.
If c("exp",t)
,models heterogeneity with an
exponential variance function. Letting \(v\) denote the variance covariate and \(\sigma^2(v)\)
denote the variance function evaluated at \(v\), the exponential
variance function is defined as \(\sigma^2(v) = e^{2 * \theta * v}\), where \(\theta\) is the variance
function coefficient.
character value "lmer", "lme" or "MCMCglmm" specifying the function to use to fit the model. Actually "lmer" only is accepted
simulate the data using simulate.merMod from lme4. Faster for large sample size but not as flexible.
Julien Martin
the simulation is based on a balanced data set with unrelated group
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
Martin, Nussey, Wilson and Reale Submitted Measuring between-individual variation in reaction norms in field and experimental studies: a power analysis of random regression models. Methods in Ecology and Evolution.
EAMM
, SSF
, plot.PAMM
if (FALSE) {
ours <- PAMM(numsim = 10, group = c(seq(10, 50, 10), 100),
repl = c(3, 4, 6),
randompart = c(0.4, 0.1, 0.5, 0.1), fixed = c(0, 1, 0.7))
plot(ours,"both")
}
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