We propose the R package LMest, which is tailored to deal with these types of model. In particular, we consider a general framework for extended LM models by including individual covariates and by formulating a mixed approach to take into account additional dependence structures in the data. ...
for (i in seq_along(val)){ tmp[row_pos[i],col_pos[i]] val[i] } 1. 2. 3. 4. 5. 6. 可以将其封装为一个函数 as_matrix function(mat){ tmp matrix(data=0L, nrow = mat@Dim[1], ncol = mat@Dim[2]) row_pos mat@i+1 col_pos findInterval(seq(mat@x)-1,mat@p[-1])+1...
### load all packages ### loadpackage <- function(x){ for( i in x ){ # require returns TRUE invisibly if it was able to load package if( ! require( i , character.only = TRUE ) ){ # If package was not able to be loaded then re-install install.packages( i , dependencies = ...
To assess the contribution of tissue and species to gene expression variation, we used a linear mixed model (LMM). Gene expression was modeled as a function of tissue and the species (considered as random factors). The LMM was implemented in the R package lme4 (ref). The restricted maximum...
## Original function to interfaceglmer.nb(y ~ f1*f2 + (1|g), data = dd, verbose = FALSE)## The interface puts data as first parameterntbt_glmer.nb(dd, y ~ f1*f2 + (1|g), verbose = FALSE)## so it can be used easily in a pipeline.dd %>% ntbt_glmer.nb(y ~ f1*f2 ...
lme4: linear mixed-effects models using S4 classes.R package version 0.999999-2Bates, DMaechler, MBolker, B
Example data for differences in convergence between lme4 versions SQL4GPL-2.0100UpdatedMay 20, 2014 BIRSPublic Notes from the focused research group meeting at the Banff International Research Center R1200UpdatedAug 20, 2013 People Top languages ...
I am analysing data (included below) usinglme4'sglmerfunction in R. The model I am building consists of a Poisson-distributed response variable (obs), one random factor (area), one continuous offset (duration), five continuous fixed effects (can_perc,can_n,time,temp,clou...
You can use theconvert_old_lme4()function to take a fitted object created withlme4<1.0 and convert it for use withlme4.0. It currently resides on R-forge, and you should be able to install it with (if the binary versions are out of date or unavailable for your system, please contact...
If you’d like to fit orthogonal polynomials you can use the poly() function with raw = FALSE (which is the default). Piecewise growth curve Segmenting the time trend into different pieces has got more to do with simple dummy coding of regression variables, than any specifics of lme or...