install.packages("rptR",repos="http://R-Forge.R-project.org")
require(rptR)
?rpt
rpt.aov
, rpt.remlLMM
and rpt.mcmcLMM
are suitable for Gaussian (normally distributed) data. rpt.binomGLMM.add
and rpt.binomGLMM.multi
are suitable for binary and proportion data. rpt.poisGLMM.add
and rpt.poisGLMM.multi
are suitable for Poisson (count) data. rpt(y, groups, datatype="Gaussian", method="ANOVA") |
rpt.aov(y, groups) |
rpt(y, groups, datatype="Gaussian", method="REML") |
rpt.remlLMM(y, groups) |
rpt(y, groups, datatype="Gaussian", method="MCMC") |
rpt.mcmcLMM(y, groups) |
rpt(y, groups, datatype="binomial", method="GLMM.multi") |
rpt.binomGLMM.multi(y, groups) |
rpt(y, groups, datatype="binomial", method="GLMM.add") |
rpt.binomGLMM.add(y, groups) |
rpt(y, groups, datatype="count", method="GLMM.multi") |
rpt.poisGLMM.multi(y, groups) |
rpt(y, groups, datatype="count", method="GLMM.add") |
rpt.poisGLMM.add(y, groups) |
print(rpt.obj)
str(rpt.obj)
rpt.remlLMM.adj
that allows the estimation of adjusted repeatabilites. The function requires
a formula
argument, a grname
argument and a data
argument. This allows much greater flexibility for estimating adjusted
repeatabilites. The formula
argument uses the same syntax as lmer
and allows the specification of fixed and random effects. With only
one random effect, the function will return the same repeatability as the function rpt.remlLMM
. The argument grname
requires
a character string or a vector of character strings that match one or more of the random effects specified in the formula. The option to specify more than one
random effect allows estimating multiple variance components and their standard errors simultaneously. The data
argument requires a
dataframe that contains the columns spefied in the fomula. The function rpt.remlLMM.adj
can also be accessed via the wrapper function rpt.adj
(again using the formula
, grname
, data
specification).rpt.adj
family of functions into rpt
, since this allows greater flexibility for
estiamting standard and adjusted repeatabilites.