SSF | R Documentation |
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
SSF(
numsim,
tss,
nbstep = 10,
randompart,
fixed = c(0, 1, 0),
n.X = NA,
autocorr.X = 0,
X.dist = "gaussian",
intercept = 0,
exgr = NA,
exrepl = NA,
heteroscedasticity = c("null")
)
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 ans S is
correlation or covariance, set to |
fixed |
vector of lenght 3 with mean, variance and estimate of fixed effect to simulate |
n.X |
number of different values to simulate for the fixed effect (covariate).
If |
autocorr.X |
correlation between two successive covariate value for a group. Default: |
X.dist |
specify the distribution of the fixed effect. Only "gaussian" (normal distribution) and
"unif" (uniform distribution) are accepted actually. Default: |
intercept |
a numeric value giving the expected intercept value. Default:0 |
exgr |
a vector specifying minimum and maximum value for number of group.
Default: |
exrepl |
a vector specifying minimum and maximum value for number
of replicates. Default: |
heteroscedasticity |
a vector specifying heterogeneity in residual variance
across X. If |
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
PAMM(), EAMM() for other simulation functions plot.SSF() for plotting
## Not run:
oursSSF <- SSF(10, 100, 10, c(0.4, 0.1, 0.6, 0))
plot(oursSSF)
## End(Not run)
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