CODEWITHSUNDEEP
rlapplylme4lsmeans
rsp
rsp

Reputation: 35

lapply: Fitting thousands of mixed models and being able to extract lsmeans

I have a list of formulas (> 10,000) for linear mixed models (lme4) that I fitted to a data set. Successfully, I have used lapply() and a custom function that incorporated tryCatch() to fit these models. Now I would like to extract the P-values and lsmeans for all of these models. I have successfully extracted the P-values, but the lsmeans function is encountering errors.

library(lme4)
library(lmerTest)
library(pbkrtest)
library(lsmeans)

formulaS <- list() #Not going to detail generation of list, generically: 'Yvar~X1*X2+(1|subject)'
dataSET <- X #dataframe with first 3 columns containing fixed and random factors, 
             # as well as >10,000 columns of variables of interest

modelSeq <- function (x, dat) {
  return(tryCatch(lmer(x, data = dat), error=function(e) NULL))
}

modelsOutput <- lapply(formulaS, function(x) modelSeq(x, dat = dataSET))

lsmeans(modelsOutput[[1]], pairwise ~ X1:X2) #recieves error

Error in solve.default(L %% V0 %% t(L), L) : Lapack routine dgesv: system is exactly singular: U[1,1] = 0

The reason I don't think it's a model problem is that if I fit the models individually I can extract the lsmeans just fine. Is there any commentary on 1) why I cannot extract lsmeans, 2) how to efficiently extract means, or 3) an alternative, efficient method.

Thanks!

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UPDATE & EDIT: This is RNAseq data with repeated samples of subjects over time I am playing with, so the >10,000 models have the same fixed and random effects that describe the experimental design. The response (a gene) is the only variable that varies. I have tried to make that more explicit in the code below. Recognizing that a mixed model with an identity link might not be ideal for the data, I have the new wrapper below. I'm still having issues extracting means. Also, any commentary on more appropriate, time-efficient methods for computing P-values is appreciated.

library(lme4)
library(blmeco)
library(ggeffects)

formulaS <- list() #Not going to detail generation of list, generically: 'GeneI~TRT*TIME+(1|subject)'
dataSET <- X #dataframe with first 3 columns containing fixed and random factors, 
             # as well as >10,000 columns of variables of interest (gene TPM)

wrap.glmer.nb <- function (modelForm, dat) {
  m <- tryCatch(glmer.nb(formula = modelForm, data = dat), error = function(e) NULL)
  if (!is.null(m)) {
    m.disp <- tryCatch(dispersion_glmer(m), error = function(e) NULL)
    m.wald <- tryCatch(anova(m), error = function(e) NULL)
    m.means.c <- tryCatch(ggemmeans(model = m, terms = c('TRT')), error = function(e) NULL)
    m.means.e <- tryCatch(ggemmeans(model = m, terms = c('TIME')), error = function(e) NULL)
    m.means.cxe <- tryCatch(ggemmeans(model = m, terms = c('TRT', 'TIME')), error = function(e) NULL)
    x <- list(m.disp, m.wald, m.means.c, m.means.e, m.means.cxe)
    print(paste0('Done with a model at ', Sys.time()))
    return(x)
  } else{
    x <- m
    return(x)
  }
}

startTime <- Sys.time()
modelOUTPUTS <- lapply(formulaS, function(modelForm) wrap.glmer.nb(modelForm, dat = dataSET))
endTime <- Sys.time()
print(paste('Victory! The analysis took:', endTime - startTime))

Upvotes: 3

Views: 458

Answers (1)

Russ Lenth
Russ Lenth

Reputation: 6780

Your original setup would work if you add one line to modelSeq():

modelSeq <- function (x, dat) {
  environment(x) <- environment()
  return(tryCatch(lmer(x, data = dat), error=function(e) NULL))
}

This sets the environment of the formula to that of the function body, making it possible to find the dataset named dat.

A similar example:

fitm <- function(formula, data, ...) {
    environment(formula) <- environment()
    lm(formula, data = data, ...)
}

fl <- list(breaks ~ tension, breaks ~ wool + tension, breaks ~ wool*tension)

md <- lapply(fl, fitm, data = warpbreaks[c(1,2,3,5,8,13,21,34,54), ])

lapply(md, function(m) emmeans(m, "tension"))

Which produces:

NOTE: Results may be misleading due to involvement in interactions

[[1]]
 tension emmean    SE df lower.CL upper.CL
 L         41.2  6.64  6    24.91     57.4
 M         17.0 16.27  6   -22.82     56.8
 H         26.0 11.51  6    -2.16     54.2

Confidence level used: 0.95 

[[2]]
 tension emmean    SE df lower.CL upper.CL
 L         41.6  8.91  5    18.73     64.5
 M         17.7 19.41  5   -32.21     67.6
 H         26.0 12.59  5    -6.38     58.4

Results are averaged over the levels of: wool 
Confidence level used: 0.95 

[[3]]
 tension emmean   SE df lower.CL upper.CL
 L         41.1 10.9  4     10.9     71.3
 M       nonEst   NA NA       NA       NA
 H         26.0 14.1  4    -13.0     65.0

Results are averaged over the levels of: wool 
Confidence level used: 0.95

BTW, you don't need the lsmeans package; it is just a front-end for emmeans. In fact, the lsmeans function itself is in emmeans; it just runs emmeans and re-labels the results.

Upvotes: 1

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