stat.glmm: Extracts statistical measures of interest in Generalized...

In SebastianSosa/ANTs: Animal Network Toolkit Software

Extracts statistical measures of interest in Generalized Linear Mixed Models

Description

Performs Generalized Linear Mixed Models tests

Usage

stat.glmm(ant, formula, family, oda = NULL, progress = TRUE, ...)

Arguments

ant	an output of ANT function perm.net.nl with random factor stated, or output of ANT 'met' categories functions in which output of ANT functions perm.ds.focal, perm.ds.grp or perm.net.lk where multiple matrices have been used.
formula	two-sided linear formula object describing both the fixed-effects and random-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. Random-effects terms are distinguished by vertical bars (|) separating expressions for design matrices from grouping factors. Two vertical bars (||) can be used to specify multiple uncorrelated random effects for the same grouping variable. (Because of the way it is implemented, the ||-syntax works only for design matrices containing numeric (continuous) predictors; to fit models with independent categorical effects, see dummy or the lmer_alt function from the afex package.).
family	a GLM family, see glm and family.
oda	the original data frame of associations when argument ant is obtained with perm.ds.grp or perm.ds.focal ANT functions.
progress	a boolean indicating the visualization of the permutation process.
...	Extra arguments for lmer or glmer function only.

Details

GLMM with permutation data.

Value

Returns a list of 3 elements :

• An object of class merMod (more specifically, an object of subclass lmerMod or glmerMod), for which many methods are available (e.g. methods(class="merMod")).

• A data frame if the estimates of the permuted models.

• A vector of integers indicating the permutations that returned model errors or warnings (e.g. model convergence issues) and for which new permutations were done.

Author(s)

Sebastian Sosa, Ivan Puga-Gonzalez.

See Also

lmer or glmer

Examples

# Creating temporal data--------------------------
m2=matrix(sample(sim.m),20,20)
diag(m2)=0
colnames(m2)=colnames(sim.m)
row.names(m2)=row.names(sim.m)
df2=sim.df
df2$age=df2$age+1
df1=sim.df
df1$period=rep(1,nrow(df1))
df2$period=rep(2,nrow(df2))
# Data structure for multiple matrices analytical protocol------------------
sim.lm=list(sim.m,m2)
sim.ldf=list(df1,df2)
# Computing network metric---------------------------------------------------
t=met.strength(sim.lm,sim.ldf,1)
# Node label permutations---------------------------------------------------
t=perm.net.nl(t,labels='age',rf="period",nperm=10,progress=FALSE) 
# Permuted GLMM-------------------------------------------------------------
r.glmm=stat.glmm(ant = t,formula = strength ~ age + (1|id),family = gaussian(), progress=TRUE)

SebastianSosa/ANTs documentation built on Sept. 25, 2023, 11:06 p.m.