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6 - dataprep - formula.R
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6 - dataprep - formula.R
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# Set up environment -----------------------------------------------------------
## Libraries
"lme4" %>=>% libInstall %=>% library(.., char = T)
"pbapply" %>=>% libInstall %=>% library(.., char = T)
# Import data ------------------------------------------------------------------
## Load saved R objects
"/Data/data.rda" %=>% paste0(getwd(), ..) %=>% load(.., envir = globalenv())
# Model selection from range of formulae ---------------------------------------
## TSS function
tss = function(targ, pred) {
pos = targ == 1
neg = targ == 0
pair = sum(pos) * sum(neg)
A = sum(pred >= 0.5 & pos)
C = sum(pos) - A
D = sum(pred < 0.5 & neg)
B = sum(neg) - D
(A * D - B * C) / pair
}
## Function for calculating final TSS
tssFinal = function(fit) {
if (any(class(fit) %in% "glmerMod")) targ = fit@resp$y
if (any(class(fit) %in% "glm")) targ = unname(fit$y)
tss(targ, fitted(fit))
}
## Function for calculating external TSS
tssTe = function(fit, tedata = tedataBinom) {
.data = tedata[, colnames(tedata) %in% colnames(model.frame(fit))]
.data = na.omit(.data)
pred = predict(fit, .data, type = "resp", allow.new.levels = T)
tss(.data$presence, pred)
}
# Generate formula and extract summaries (GLM) ---------------------------------
## Create list of formula
formulae = unlist(
lapply(3:5, x ->> {
cluster[[x]] %=>%
expand.grid %|>%
paste(unlist(..), collapse = " + ") %=>%
paste("week", "dis", "gene", "dir", .., sep = " + ") %=>%
paste(.., "week:dis", "gene:week", "gene:dis", sep = " + ") %=>%
paste("presence ~", ..)
})
)
## Models
pblapply(formulae, y ->> {
glm(y, binomial, merge(field, env[[4]])) %=>%
data.frame(
paste(deparse(formula(..)[[3]], width.cutoff = 500)),
AIC(..), BIC(..), logLik(..), tssFinal(..), tssTe(..)
)
}) %=>%
do.call(rbind, ..) %=>%
setNames(.., c("Formula", "AIC", "BIC", "logLik", "TSS", "eTSS")) %=>%
# arrange(.., BIC) %->%
filter(.., TSS >= 0.70) %=>%
arrange(.., -eTSS) %->%
modelsBinom
## Selecting best formula
modelsBinom %=>%
..$Formula[which.min(..$BIC), ] %->%
bFormGLM %=>%
cat(paste0("Best formula by GLM is ", .., "."))
# Generate formula and extract summaries (GLMM - execute in cluster) -----------
## Create list of formula
# formulaeMM <- unlist(
# lapply(3:5, x ->> {
# cluster[[x]] %=>%
# expand.grid %|>%
# paste(unlist(..), collapse = " + ") %=>%
# paste("week", "dis", "gene", "dir", .., sep = " + ") %=>%
# paste(.., "week:dis", "gene:week", "gene:dis", sep = " + ") %=>%
# paste(.., "+ (1 | year / rep / id) + (1 | year : dir)") %=>%
# paste("presence ~", ..)
# })
# )
## Models
# pblapply(formulaeMM, y ->> {
# glmer(y, dataBinom, binomial(link = "logit")) %=>%
# data.frame(
# paste(deparse(formula(..)[[3]], width.cutoff = 500)),
# AIC(..), BIC(..), logLik(..), tss(..), tssTe(..)
# )
# }, cl = makeForkCluster()) %=>%
# do.call(rbind, ..) %=>%
# setNames(.., c("Formula", "AIC", "BIC", "logLik", "TSS", "eTSS")) %=>%
# # arrange(.., BIC) %->%
# filter(.., TSS >= 0.80) %=>%
# arrange(.., -eTSS) %->%
# modelsBinomMM
## Selecting best formula
# modelsBinomMM %=>%
# ..$Formula[which.min(..$BIC), ] %->%
# bFormGLMM %=>%
# cat(paste0("Best formula by GLM is ", .., "."))
# Merge best lag period --------------------------------------------------------
## Select preferred approach
if (bFormGLM == bFormGLMM) {
bForm = bFormGLMM
} else {
cat("Best formula by GLM and GLMM are different. Select one to proceed.")
userChoice = select.list(c("GLM", "GLMM"))
bForm = ifelse(userChoice == "GLM", bFormGLM, bFormGLMM)
}
# Cleaning up ------------------------------------------------------------------
## Save dataframes as R object
c("bForm") %=>%
save(list = .., file = paste0(getwd(), "/Data/formula.rda"))
## Remove old dataframes
rm(
"dataBinom", "teDataBinom", "formulae", "modelsBinom",
"formulaeMM", "modelsBinomMM"
)
## Load saved .rda
"/Data/foumula.rda" %=>% paste0(getwd(), ..) %=>% load(.., envir = globalenv())