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Commit d9f7a50a authored by Kunstler Georges's avatar Kunstler Georges
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rerun deid

parent 3bd7fd04
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R/analysis/lmer.output-fun.R
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R/analysis/lmer.run.R
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......@@ -27,34 +27,10 @@ run.model.for.set.one.trait <- function(model.file, fun.model, trait,
#=====================================================================
path.model <- "R/analysis/model.lmer"
model.files.lmer.Tf.0 <- file.path(path.model,
c("model.lmer.LOGLIN.r.set.species.R",
"model.lmer.LOGLIN.r.set.fixed.biomes.species.R"))
model.files.lmer.Tf.0b <- file.path(path.model,
c("model.lmer.LOGLIN.MAT.MAP.r.set.species.R",
"model.lmer.LOGLIN.MAT.MAP.r.set.fixed.biomes.species.R"))
model.files.lmer.Tf.1 <- file.path(path.model,
c("model.lmer.LOGLIN.ER.AD.Tf.r.set.species.R",
"model.lmer.LOGLIN.ER.AD.Tf.r.set.fixed.biomes.species.R"))
model.files.lmer.Tf.2 <- file.path(path.model,
c("model.lmer.LOGLIN.ER.AD.Tf.r.ecocode.species.R",
"model.lmer.LOGLIN.ER.AD.Tf.r.ecocode.fixed.biomes.species.R"))
model.files.lmer.Tf.3 <- file.path(path.model,
c("model.lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species.R",
"model.lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.fixed.biomes.species.R"))
model.files.lmer.Tf.4 <- file.path(path.model,
c("model.lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.ecocode.species.R",
"model.lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.ecocode.fixed.biomes.species.R"))
model.files.lmer.Tf.intra.0 <- file.path(path.model,
c("model.lmer.LOGLIN.MAT.MAP.intra.r.set.species.R",
"model.lmer.LOGLIN.MAT.MAP.intra.r.set.fixed.biomes.species.R"))
model.files.lmer.Tf.intra.1 <- file.path(path.model,
c("model.lmer.LOGLIN.ER.AD.Tf.intra.r.set.species.R",
"model.lmer.LOGLIN.ER.AD.Tf.intra.r.set.fixed.biomes.species.R"))
model.files.lmer.Tf.intra.2 <- file.path(path.model,
c("model.lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species.R",
"model.lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.fixed.biomes.species.R"))
model.files.lmer.Tf.intra.3 <- file.path(path.model,
model.files.lmer.Tf.intra.2 <- file.path(path.model,
c("model.lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.ecocode.species.R",
"model.lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.ecocode.fixed.biomes.species.R"))
......@@ -71,57 +47,17 @@ call.lmer.and.save <- function(formula, df.lmer, path.out,
print(max(abs(relgrad)) )
if(is.na(select.biome)){
name.file <- paste(var.sample,ecocode.var,
"results.nolog.all.rds", sep ='.')
"results.nolog.all.deid.rds", sep ='.')
}
if(!is.na(select.biome)){
name.file <- paste(var.sample, ecocode.var,
gsub(' ', '_',select.biome),
"results.nolog.all.rds", sep ='.')
"results.nolog.all.deid.rds", sep ='.')
}
saveRDS(list(output = lmer.output, relgrad = relgrad, list.sd = list.sd),
file = file.path(path.out, name.file))
}
save.deid.data <- function(model.file, trait,
min.obs = 10, sample.size = NA,
ecocode.var = 'koppen',
data.type ='simple',
var.sample = NA,
select.biome = NA,
merge.biomes.TF = FALSE,
sample.vec.TF = FALSE){
require(lme4)
source(model.file, local = TRUE)
model <- load.model()
#= Path for output
path.out <- output.dir('lmer', model$name, trait, data.type)
print(path.out)
dir.create(path.out, recursive = TRUE, showWarnings = FALSE)
cat("run lmer for model", model.file, " for trait",
trait, "\n")
list.df.lmer <- load.data.for.lmer(trait,
data.type = data.type,
sample.size. = sample.size,
ecocode.var. = ecocode.var,
var.sample. = var.sample,
select.biome. = select.biome,
merge.biomes.TF = merge.biomes.TF,
sample.vec.TF. = sample.vec.TF)
# return a list of DF
cat("Ok data with Nobs", nrow(list.df.lmer[[1]]),
"\n")
print(names(list.df.lmer[[1]]))
print(dim(list.df.lmer[[1]]))
write.csv(list.df.lmer[[1]],
file = file.path("output/processed",paste('data.deid', trait, data.type, 'csv',
sep = '.')),
row.names = FALSE)
}
run.lmer <- function (model.file, trait,
......@@ -142,28 +78,20 @@ run.lmer <- function (model.file, trait,
cat("run lmer for model", model.file, " for trait",
trait, "\n")
list.df.lmer <- load.data.for.lmer(trait,
data.type = data.type,
sample.size. = sample.size,
ecocode.var. = ecocode.var,
var.sample. = var.sample,
select.biome. = select.biome,
merge.biomes.TF = merge.biomes.TF,
sample.vec.TF. = sample.vec.TF)
# return a list of DF
cat("Ok data with Nobs", nrow(list.df.lmer[[1]]),
df.lmer <- read.csv(file = file.path("output/processed",
paste('data.deid', trait, data.type, 'csv',
sep = '.')))
cat("Ok data with Nobs", nrow(df.lmer),
"\n")
# if(merge.biomes.TF) df <- merge.biomes(df) TO MOVE AFTER
if(merge.biomes.TF) df.lmer <- merge.biomes(df.lmer)
print(names(list.df.lmer[[1]]))
print(dim(list.df.lmer[[1]]))
#= Run model
call.lmer.and.save(formula = model$lmer.formula.tree.id,
list.df.lmer[[1]], path.out = path.out,
df.lmer, path.out = path.out,
var.sample = var.sample,
ecocode.var = ecocode.var,
select.biome = select.biome,
list.sd = list.df.lmer[[2]])
list.sd = list(NA))
gc()
}
......@@ -179,343 +107,16 @@ output.dir <- function (type , model, trait, set) {
# Function to prepare data for lmer
#============================================================
## add sample equivalent per ecocode
add.sampling.prob.by.var.sample <- function(df, var.sample){
vec.prob <- 1/table(df[[var.sample]])
df1 <- data.frame(ecocode = names(vec.prob),
prob.sample = as.numeric(vec.prob))
names(df1) <- c(var.sample, 'prob.sample')
df2 <- left_join(df, df1, by = var.sample)
return(df2)
}
resample.plot.by.var <- function(df, var.sample, sample.size){
df2 <- add.sampling.prob.by.var.sample(df, var.sample)
df1 <- df2 %>% group_by(plot) %>% summarise(prob.sample = mean(prob.sample))
num.sample.plot <- sample(1:nrow(df1), size = sample.size,
prob = df1$prob.sample)
vec.sample.plot <- df1[['plot']][num.sample.plot]
return(vec.sample.plot)
}
load.data.for.lmer <- function(trait, data.type,
base.dir = "output/processed",
sample.size. ,
ecocode.var.,
var.sample. = 'koppen',
select.biome. = NA,
select.set. = NA,
sample.vec.TF. = FALSE,
merge.biomes.TF = FALSE){
require(dplyr)
if (! data.type %in% c('simple', 'intra', 'all.census'))
stop('not good data.type')
df <- readRDS( file.path(base.dir,paste('data', trait, data.type, 'rds',
sep = '.')))
df <- dplyr::mutate(df,sp.name = ifelse(is.na(sp.name), 'missing.sp',
sp.name))
# if(merge.biomes.TF) df <- merge.biomes(df) TO MOVE AFTER
if(!is.na(select.biome.)) {
df <- dplyr::filter(df, biomes == select.biome.)
}
if(!is.na(select.set.)) {
df <- dplyr::filter(df, set == select.set.)
}
if(!is.na(sample.size.)){
}
list.sd <- get.sd.lmer(df, trait, min.obs = 10)
print('sd ok')
res <- format.data.for.lmer(df, trait,
data.type = data.type,
ecocode.var = ecocode.var.)
return( list(res, list.sd))
}
merge.biomes <- function(data){
data$biomes <- factor(data$biomes)
levels.name <- levels(data$biomes)
data$biomes <- factor(data$biomes.id)
levels.name <- levels(data$biomes.id)
levels.name[levels.name == 'Tundra'] <- 'Boreal forest'
levels.name[levels.name == 'Subtropical desert'] <- 'Temperate grassland desert'
levels.name[levels.name == "Tropical rain forest"] <- "Tropical forest savanna"
levels(data$biomes) <- levels.name
levels(data$biomes.id) <- levels.name
print('biomes merged')
return(data)
}
select.one.census.per.plot <- function(df){
require(dplyr)
cat(dim(df))
df <- df %>% mutate(plot.c = paste(plot, census))
df.select <- df %>%
sample_n(., nrow(df)) %>% group_by( plot) %>%
summarise( plot.cs = first(plot.c)) %>%
dplyr::select(plot.cs)
plots.select <- as.vector(df.select$plot.cs)
df2 <- filter(df, plot.c %in% plots.select)
print(dim(df2))
if(!(nrow(df2) < nrow(df))) stop('selection census error')
return(df2)
}
select.data.for.analysis <- function(data.tree, abs.CWM.tntf, perc.neigh.sp,
perc.neigh.gs, BATOT, min.obs,
min.perc.neigh.sp = 0.90,
min.perc.neigh.gs = 0.95,
min.BA.G = -40,
max.BA.G = 180){
select.temp <-(1:nrow(data.tree))[!is.na(data.tree[["BA.G"]]) &
!is.na(data.tree[["D"]]) &
!is.na(data.tree[["biomes"]]) &
!is.na(data.tree[["MAT"]]) &
!is.na(data.tree[["MAP"]]) &
data.tree[["BA.G"]]>min.BA.G &
data.tree[["BA.G"]]<max.BA.G &
data.tree[["D"]]>9.9 &
!is.na(data.tree[[abs.CWM.tntf]]) &
!is.na(data.tree[[BATOT]]) &
data.tree[[perc.neigh.sp]] >
min.perc.neigh.sp &
!is.na(data.tree[[perc.neigh.sp]]) &
data.tree[[perc.neigh.gs]] >
min.perc.neigh.gs &
!is.na(data.tree[[perc.neigh.gs]])]
## remove tree with NA
data.tree <- data.tree[select.temp ,]
# select species with minimum obs
select.temp <- (1:nrow(data.tree))[data.tree[["sp"]] %in%
names(table(factor(data.tree[["sp"]])))[
table(factor(data.tree[["sp"]]))>min.obs]]
data.tree <- data.tree[select.temp, ]
DT <- drop(data.tree)
rm(data.tree)
gc()
return(DT)
}
#============================================================
# Function to prepare data for lmer with new CWM data
# that will be used in a simple linear model
#============================================================
select.data.trait <- function(data.tree, trait, min.obs = 10) {
if(! trait %in% c("SLA", "Leaf.N", "Seed.mass",
"Wood.density", "Max.height"))
stop("trait need to be in SLA, Leaf.N, Seed.mass,
Wood.density, or Max.height ")
# get vars names
CWM.tn <- paste(trait, "CWM", 'fill', sep = ".")
abs.CWM.tntf <- paste(trait, "abs.CWM", 'fill', sep = ".")
tf <- paste(trait, "focal", sep = ".")
BATOT <- "BATOT"
perc.neigh.sp <- paste(trait, "perc", 'species', sep = ".")
perc.neigh.gs <- paste(trait, "perc", 'genus', sep = ".")
data.tree <- select.data.for.analysis(data.tree,
abs.CWM.tntf,
perc.neigh.sp,
perc.neigh.gs,
BATOT,
min.obs)
return(data.tree)
}
load.and.save.data.for.lmer <- function(trait,
min.obs= 10,
data.type = 'simple',
base.dir = "output/processed"){
fname <- 'data.all.no.log.all.census.rds'
data.tree.tot <- readRDS(file.path(base.dir, fname))
df <- select.data.trait(data.tree.tot, trait)
if(data.type != 'all.census') {
df <- select.one.census.per.plot(df)
}
saveRDS(df,file = file.path(base.dir,paste('data', trait, data.type, 'rds',
sep = '.')))
}
###################
## FORMAT DATA
### get variables for analysis three type simple cat multi
scale.d <- function(x, ...){
return(as.vector(scale(x, ...)))
}
scale.nc <- function(x, center = FALSE){
return(as.vector(scale(x,
center = center,
scale = sd(x, na.rm = TRUE))))
}
get.variables <- function(data.tree, BATOT, CWM.tn,
abs.CWM.tntf, tf, ecocode.var = 'koppen',
min.BA.G = 40, min.MAT = 10){
logG <- scale.d(log(data.tree[["BA.G"]] + min.BA.G+1))
logD <- scale.d(log(data.tree[["D"]]))
MAT <- scale.d(log(data.tree[["MAT"]]+min.MAT))
MAP <- scale.d(log(data.tree[["MAP"]]))
species.id <- as.character(factor(data.tree[["sp.name"]]))
plot.id <- as.character(factor(data.tree[["plot"]]))
tree.id <- as.character(factor(data.tree[["tree.id"]]))
set.id <- as.character(factor(data.tree[["set"]]))
ecocode.id <- as.character(factor(paste(data.tree[[ecocode.var]], data.tree[["set"]])))
biomes.id <- factor(data.tree[['biomes']])
#= multiply CWMs by BATOT
sumTnTfBn.abs <- data.tree[[abs.CWM.tntf]]*
data.tree[[BATOT]]
sumTnBn <- data.tree[[CWM.tn]]*data.tree[[BATOT]]
sumTfBn <- data.tree[[tf]]*data.tree[[BATOT]]
sumBn <- data.tree[[BATOT]]
return(data.frame(logG = logG,
logD = logD,
MAT = MAT,
MAP = MAP,
species.id = species.id,
set.id = set.id,
ecocode.id = ecocode.id,
biomes.id = biomes.id,
plot.id = plot.id,
tree.id = tree.id,
sumTnTfBn.abs = scale.nc(sumTnTfBn.abs, center = FALSE),
Tf = scale.d(data.tree[[tf]]),
sumTnBn = scale.nc(sumTnBn, center = FALSE),
sumTfBn = scale.nc(sumTfBn, center = FALSE),
sumBn = scale.nc(sumBn, center = FALSE),
stringsAsFactors = FALSE))
}
get.variables.intra <- function(data.tree, BATOT, CWM.tn,
abs.CWM.tntf, tf, ecocode.var = 'koppen',
min.BA.G = 40, min.MAT = 10){
logG <- scale.d(log(data.tree[["BA.G"]] + min.BA.G+1))
logD <- scale.d(log(data.tree[["D"]]))
MAT <- scale.d(log(data.tree[["MAT"]]+min.MAT))
MAP <- scale.d(log(data.tree[["MAP"]]))
species.id <- as.character(unclass(factor(data.tree[["sp.name"]])))
plot.id <- as.character(unclass(factor(data.tree[["plot"]])))
tree.id <- as.character(unclass(factor(data.tree[["tree.id"]])))
set.id <- as.character(unclass(factor(data.tree[["set"]])))
ecocode.id <- as.character(factor(paste(data.tree[[ecocode.var]], data.tree[["set"]])))
biomes.id <- factor(data.tree[['biomes']])
#= multiply CWMs by BATOT
sumTnTfBn.abs <- data.tree[[abs.CWM.tntf]]*
(data.tree[[BATOT]] - data.tree[['BAintra']])
sumTnBn <- data.tree[[CWM.tn]]*data.tree[[BATOT]]
sumTfBn <- data.tree[[tf]]*data.tree[[BATOT]]
sumBn.inter <- (data.tree[[BATOT]] - data.tree[['BAintra']])
sumBn <- data.tree[[BATOT]]
return(data.frame(logG = logG,
logD = logD,
MAT = MAT,
MAP = MAP,
species.id = species.id,
set.id = set.id,
ecocode.id = ecocode.id,
biomes.id = biomes.id,
plot.id = plot.id,
tree.id = tree.id,
sumTnTfBn.abs = scale.nc(sumTnTfBn.abs, center = FALSE),
Tf = scale.d(data.tree[[tf]]),
sumTnBn = scale.nc(sumTnBn, center = FALSE),
sumTfBn = scale.nc(sumTfBn, center = FALSE),
sumBn.inter = scale.nc(sumBn.inter, center = FALSE),
sumBn.intra = scale.nc(data.tree[['BAintra']],
center = FALSE),
sumBn = scale.nc(sumBn, center = FALSE),
stringsAsFactors = FALSE))
}
#============================================================
# Function to prepare data for lmer with CWM data
#============================================================
format.data.for.lmer <- function(data.tree, trait, min.obs = 10,
data.type = 'simple', ecocode.var = 'koppen') {
if(! trait %in% c("SLA", "Leaf.N", "Seed.mass",
"SLA", "Wood.density", "Max.height"))
stop("need trait to be in SLA Leaf.N Seed.mass
SLA Wood.density or Max.height")
# get vars names
CWM.tn <- paste(trait, "CWM", 'fill', sep = ".")
abs.CWM.tntf <- paste(trait, "abs.CWM", 'fill', sep = ".")
tf <- paste(trait, "focal", sep = ".")
BATOT <- "BATOT"
#= DATA LIST FOR LMER
if(data.type == 'simple' | data.type == 'all.census') {
print('simple')
lmer.data <- get.variables(data.tree,
BATOT,
CWM.tn,
abs.CWM.tntf,
tf,
ecocode.var)
}
if(data.type =='intra') {
print('intra')
lmer.data <- get.variables.intra(data.tree,
BATOT,
CWM.tn,
abs.CWM.tntf,
tf,
ecocode.var)
}
return(lmer.data)
}
## SD for plot
get.sd.lmer <- function(data.tree, trait, min.obs = 10) {
if(! trait %in% c("SLA", "Leaf.N", "Seed.mass",
"SLA", "Wood.density", "Max.height"))
stop("need trait to be in SLA Leaf.N Seed.mass
SLA Wood.density or Max.height")
# get vars names
CWM.tn <- paste(trait, "CWM", 'fill', sep = ".")
abs.CWM.tntf <- paste(trait, "abs.CWM", 'fill', sep = ".")
tf <- paste(trait, "focal", sep = ".")
BATOT <- "BATOT"
#= DATA SD LIST FOR LMER
list.sd <- compute.sd.mean.var.lmer(data.tree, tf, CWM.tn,
abs.CWM.tntf, min.BA.G = 40)
return(list.sd)
}
compute.sd.mean.var.lmer <- function(data.tree, tf, CWM.tn,
abs.CWM.tntf, min.BA.G = 40){
list.sd <- list(
'mean.logG' = mean(log(data.tree[["BA.G"]] + min.BA.G+1), na.rm = TRUE),
'sd.logG' = sd(log(data.tree[["BA.G"]] + min.BA.G+1), na.rm = TRUE),
'mean.logD' = mean(log(data.tree[["D"]] ), na.rm = TRUE),
'sd.logD' = sd(log(data.tree[["D"]] ), na.rm = TRUE),
'mean.tf' = mean(data.tree[[tf]] , na.rm = TRUE),
'sd.tf' = sd(data.tree[[tf]] , na.rm = TRUE),
'sd.sumBn' = sd( data.tree[['BATOT']], na.rm = TRUE),
'sd.sumBn.inter' = sd( data.tree[['BATOT']] - data.tree[['BAintra']],
na.rm = TRUE),
'sd.sumBn.intra' = sd( data.tree[['BAintra']], na.rm = TRUE),
'sd.sumTfBn' = sd(data.tree[[tf]]* data.tree[['BATOT']], na.rm = TRUE),
'sd.sumTnTfBn.abs' = sd(data.tree[[abs.CWM.tntf]]*
data.tree[['BATOT']], na.rm = TRUE),
'sd.sumTnBn' = sd(data.tree[[CWM.tn]]*data.tree[['BATOT']], na.rm = TRUE)
)
return(list.sd)
}
launch.cluster/launch_all_lmer.bash
View file @ d9f7a50a
......@@ -23,29 +23,22 @@ samplesize=$1
for trait in "'SLA'" "'Wood.density'" "'Max.height'" ; do
# # INTRA 0
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.0[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRA0${trait}.sh
qsub Rscript_temp/allINTRA0${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRA0${trait}" -q opt32G -j oe
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.0[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRA02${trait}.sh
qsub Rscript_temp/allINTRA02${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRA02${trait}" -q opt32G -j oe
# # INTRA 2
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.2[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAB${trait}.sh
# # INTRA 1
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.1[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAB${trait}.sh
qsub Rscript_temp/allINTRAB${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAB${trait}" -q opt32G -j oe
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.2[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAB2${trait}.sh
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.1[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAB2${trait}.sh
qsub Rscript_temp/allINTRAB2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAB2${trait}" -q opt32G -j oe
# # INTRA 3
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.3[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAE${trait}.sh
# # INTRA 2
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.2[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAE${trait}.sh
qsub Rscript_temp/allINTRAE${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAE${trait}" -q opt32G -j oe
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.3[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAE2${trait}.sh
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.2[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAE2${trait}.sh
qsub Rscript_temp/allINTRAE2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAE2${trait}" -q opt32G -j oe
......
scripts/analysis/lmer.all.no.log.output.R
View file @ d9f7a50a
......@@ -6,27 +6,11 @@ source("R/analysis/lmer.run.R")
## ## save list of all output for NA
## format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
## list.file.name = 'list.lmer.out.all.NA.simple.ecocode.koppen.rds',
## models = c(model.files.lmer.Tf.4, model.files.lmer.Tf.2),
## traits = c("SLA", "Wood.density", "Max.height"))
## format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
## list.file.name = 'list.lmer.out.all.NA.simple.set.null.rds',
## models = c(model.files.lmer.Tf.0b),
## traits = c("SLA", "Wood.density", "Max.height"))
## format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
## list.file.name = 'list.lmer.out.all.NA.simple.set.rds',
## models = c(model.files.lmer.Tf.0, model.files.lmer.Tf.0b,
## model.files.lmer.Tf.1, model.files.lmer.Tf.3),
## traits = c("SLA", "Wood.density", "Max.height"))
format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
list.file.name = 'list.lmer.out.all.NA.intra.set.rds',
models = c(model.files.lmer.Tf.intra.0,
model.files.lmer.Tf.intra.2,
model.files.lmer.Tf.intra.3),
format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.deid.rds",
list.file.name = 'list.lmer.out.all.NA.intra.set.deid.rds',
models = c(model.files.lmer.Tf.intra.1,
model.files.lmer.Tf.intra.2),
traits = c("SLA", "Wood.density", "Max.height"),
data.type = 'intra')
......
scripts/analysis/results.fig.R
View file @ d9f7a50a
......@@ -12,78 +12,18 @@ names.biomes <- c('Desert', 'Desert', 'Woodland/shrubland', 'Temperate forest',
## load results
list.all.results.set <-
readRDS('output/list.lmer.out.all.NA.simple.set.rds')
list.all.results.ecocode <-
readRDS('output/list.lmer.out.all.NA.simple.ecocode.koppen.rds')
list.all.results.intra <-
readRDS('output/list.lmer.out.all.NA.intra.set.rds')
list.all.results <-
readRDS('output/list.lmer.out.all.NA.intra.set.deid.rds')
# check convergence
fun.get.conv(list.all.results.set)
vec.rel.grad.set <- sapply(list.all.results.set,
function(list.t) { max(abs(list.t[['relgrad']]))})
vec.rel.grad.set < 0.001
fun.get.conv(list.all.results.ecocode)
vec.rel.grad.ecocode <- sapply(list.all.results.ecocode,
function(list.t) { max(abs(list.t[['relgrad']]))})
vec.rel.grad.ecocode < 0.001
fun.get.conv(list.all.results.intra)
vec.rel.grad.set <- sapply(list.all.results.intra,
# gradient < 0.001 as recommend by Bolker
vec.rel.grad.set <- sapply(list.all.results,
function(list.t) { max(abs(list.t[['relgrad']]))})
vec.rel.grad.set < 0.001