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# source("f.lmer.reduced.model.rank3.scale.r")
# This function converts the variables passed in the function call to ranks based on titers, then renames the variables and adds them to the input df before returning
f.lmer.reduced.model.rank3.scale <- function(df.input, ...)
{
options(warn = 1)
set.seed(123) # set the seed to ensure repeatability in random generator functions
d.start.time <- Sys.time()
# print()
df.temp.full <- subset(df.data.flat, select = c("c.id", "c.age", "c.sex", "d.sample.date", "i.sample.number.2", "c.reproductive.status",
"c.prey.density.2.level", "c.prey.density.3.level",
"c.rank.2.level", "c.rank.3.level",
"d.age.months", "i.number.adult.female.by.year",
"d.mass", "d.minutes.blood", "d.pcv", "i.glucose.green", "d.total.solids",
"i.rank", "d.rank.proportion",
"d.prey.density", "d.precipitation",
"d.mic.90.bka.ec", "d.mic.90.bka.pm",
"d.mic.90.median.bka.ec", "d.mic.90.median.bka.pm",
# "d.prey.low.density.prior", "d.prey.low.density.current",
"d.blank.abs.corrected.total.igg", "d.blank.abs.corrected.total.igm", "d.blank.abs.ec.igg", "d.blank.abs.pm.igg",
"d.blank.abs.ec.igm", "d.blank.abs.pm.igm",
"i.cmi.bsa.igg", "i.cmi.bsa.igm", "i.cmi.cav2.igg", "i.cmi.cav2.igm", "i.cmi.ccv.igg", "i.cmi.ccv.igm", "i.cmi.cdv.igg", "i.cmi.cdv.igm",
"i.cmi.dirofilaria.igg", "i.cmi.dirofilaria.igm", "i.cmi.fiv.p24.igg", "i.cmi.fiv.p24.igm", "i.cmi.bsa.igg.rank", "i.cmi.cav2.igg.rank",
"i.cmi.ccv.igg.rank", "i.cmi.cdv.igg.rank", "i.cmi.dirofilaria.igg.rank", "i.cmi.fiv.p24.igg.rank",
"i.cmi.bsa.igm.rank", "i.cmi.cav2.igm.rank", "i.cmi.ccv.igm.rank", "i.cmi.cdv.igm.rank", "i.cmi.dirofilaria.igm.rank",
"i.cmi.fiv.p24.igm.rank", "d.serology.index.igg", "d.serology.index.rank.igg", "d.serology.index.igm", "d.serology.index.rank.igm",
"d.neutrophil.lymphocyte.ratio", "d.relative.eosinophils", "d.relative.lymphocytes", "d.relative.monocytes", "d.relative.neutrophils", "d.total.wbc",
"d.cortisol", "d.testosterone", "d.ars.cubs", "d.ars.grad", "d.ars.wean", "d.ars.24.month"
))
df.temp.full <- subset(df.temp.full, d.sample.date > as.Date("1jan1996", "%d%b%Y"))
df.temp.full$i.sample.years <- as.integer(format(df.temp.full$d.sample.date, "%Y"))
df.temp.full$i.sample.years <- df.temp.full$i.sample.year - min(df.temp.full$i.sample.year) + 1
df.temp.full$i.sample.days <- julian(df.temp.full$d.sample.date)
df.temp.full$i.sample.days <- julian(df.temp.full$d.sample.date) - min(julian(df.temp.full$d.sample.date)) + 1 # can't take log of zero, so start at day one instead of zero
df.temp.full$i.sample.months <- round((julian(df.temp.full$d.sample.date) - min(julian(df.temp.full$d.sample.date))) / 30, 0) + 1 # can't take log of zero, so start at month one instead of zero
# df.temp.full <- subset(df.temp.full, c.sex == "f" & i.sample.number.2 == 2)
df.temp.full <- subset(df.temp.full, c.sex == "f" & d.age.months >= 24)
# df.temp.full <- subset(df.temp.full, !is.na(i.rank))
df.temp.full <- subset(df.temp.full, !is.na(c.rank.3.level))
df.temp.full <- subset(df.temp.full, c.reproductive.status != "bado")
df.temp.full <- subset(df.temp.full, c.reproductive.status != "neither")
df.temp.full <- subset(df.temp.full, !is.na(c.reproductive.status))
df.temp.full <- subset(df.temp.full, !is.na(d.prey.density))
df.temp.full <- subset(df.temp.full, !(c.id == "mp" & i.sample.number.2 == 2))
df.temp.full <- subset(df.temp.full, !(c.id == "gol" & i.sample.number.2 == 3))
df.temp.full <- subset(df.temp.full, !(c.id == "cr" & i.sample.number.2 == 1))
# df.temp.full$d.prey.density <- log((df.temp.full$d.prey.low.density.prior + df.temp.full$d.prey.low.density.current) / 2)
# df.temp.full$c.prey.density <- df.temp.full$c.prey.density.3.level
# df.temp.full$c.prey.density <- df.temp.full$c.prey.density.2.level
df.temp.full$i.rank <- scale(df.temp.full$i.rank, scale = FALSE)
df.temp.full$d.prey.density <- scale((df.temp.full$d.prey.density), scale = FALSE)
df.temp.full$d.precipitation <- scale((df.temp.full$d.precipitation), scale = FALSE)
print("summary(df.temp.full$d.precipitation)")
print(summary(df.temp.full$d.precipitation))
df.temp.full$d.age.months <- scale((df.temp.full$d.age.months), scale = FALSE)
df.temp.full$d.date <- scale((df.temp.full$i.sample.months), scale = FALSE) # lm and lmer model response variables
print("summary(df.temp.full$d.date)")
print(summary(df.temp.full$d.date))
df.temp.full$d.cortisol <- scale((df.temp.full$d.cortisol), scale = FALSE)
df.temp.full$d.testosterone <- scale((df.temp.full$d.testosterone), scale = FALSE)
df.temp.full$d.mass <- scale(df.temp.full$d.mass, scale = FALSE)
df.temp.full$d.minutes.blood <- scale(df.temp.full$d.minutes.blood, scale = FALSE)
df.temp.full$d.pcv <- scale(df.temp.full$d.pcv, scale = FALSE)
df.temp.full$d.mic.90.bka.ec <- log2(df.temp.full$d.mic.90.bka.ec)
df.temp.full$d.mic.90.bka.pm <- log2(df.temp.full$d.mic.90.bka.pm)
df.temp.full$d.total.igg <- log(df.temp.full$d.blank.abs.corrected.total.igg)
df.temp.full$d.total.igm <- sqrt(df.temp.full$d.blank.abs.corrected.total.igm)
df.temp.full$d.ec.igg <- df.temp.full$d.blank.abs.ec.igg
df.temp.full$d.pm.igg <- df.temp.full$d.blank.abs.pm.igg
df.temp.full$d.ec.igm <- df.temp.full$d.blank.abs.ec.igm
df.temp.full$d.pm.igm <- df.temp.full$d.blank.abs.pm.igm
print(length(df.temp.full$c.id))
print(length(unique(df.temp.full$c.id)))
print(length(unique(df.temp.full$d.rank.proportion)))
cv.dependent.factors <- c("d.mic.90.median.bka.ec", "d.mic.90.median.bka.pm", "d.total.igg", "d.total.igm")
cv.dependent <- c("d.mic.90.bka.ec", "d.mic.90.bka.pm", "d.total.igg", "d.total.igm")
cv.dependent <- c("d.total.igm")
# cv.dependent <- c("d.mic.90.median.bka.ec", "d.mic.90.median.bka.pm")
m.cor <- cor(df.temp.full[, 11:73], use = "pairwise.complete.obs", method = c("spearman"))
write.csv(m.cor, file = "temp.cor.csv")
# c.links <- c("probit", "cloglog", "loglog", "cauchit", "Aranda-Ordaz", "Aranda-Ordaz", "log-gamma")
c.links <- c("logit")
par(mfrow = c(2,3))
i.counter.2 <- 1
i.counter.2.stop <- length(cv.dependent)
for(i.counter.2 in i.counter.2:i.counter.2.stop) {
c.dependent.factor <- cv.dependent.factors[i.counter.2]
c.dependent <- cv.dependent[i.counter.2]
# print(paste(" **************** c.independent: ", c.independent, "**********************"))
print(paste(" **************** c.dependent: ", c.dependent, "**********************"))
warning() # this command should flush the warning messages, which should allow me to locate where any future warnings are being generated
print("Before subsetting within the loop.")
i.full.c.id <- length(df.temp.full$c.id)
i.full.unique.c.id <- length(unique(df.temp.full$c.id))
# We can only include records in the clmm models that do not include NAs, so we subset the df.temp.full to df.temp each time throug the loop.
# If the number of records in the clmm.0 and clmm. models are not the same, then the anova pval and cftest pval will not match. Same goes for bootstrap values
df.temp <- subset(df.temp.full, !is.na(df.temp.full[[c.dependent]]))
print("summary(df.temp$d.dependent)")
print(summary(df.temp$d.dependent))
df.temp$d.dependent <- df.temp[[c.dependent]] # lm and lmer model response variables
print("summary(df.temp$d.dependent)")
print(summary(df.temp$d.dependent))
# df.temp$d.dependent <- (df.temp[[c.dependent]]) # lm and lmer model response variables
df.temp$d.dependent.factor <- ordered(df.temp[[c.dependent.factor]]) # clm and clmm models need to have factors for response variables
############################## The data used in the clm and clmm calls need to be a global varialbe or the accessory function ###############
############################################################################################################################################
############################################################################################################################################
############################################################################################################################################
# df.temp <<- df.temp
print("")
print(" ******* starting lmer models **************************")
l.models <- list()
print("1")
par(mfrow = c(3,3))
lmer.1 <- lmer((d.dependent) ~ (d.date)
+ c.rank.3.level
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[1] <- lmer.1
print("2")
par(mfrow = c(3,3))
lmer.2 <- lmer((d.dependent) ~ (d.date)
+ d.prey.density
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[2] <- lmer.2
print("3")
par(mfrow = c(3,3))
lmer.3 <- lmer((d.dependent) ~ (d.date)
+ c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[3] <- lmer.3
print("4")
par(mfrow = c(3,3))
lmer.4 <- lmer((d.dependent) ~ (d.date)
+ c.rank.3.level * d.prey.density
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[4] <- lmer.4
print("5")
par(mfrow = c(3,3))
lmer.5 <- lmer((d.dependent) ~ (d.date)
+ c.rank.3.level * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[5] <- lmer.5
print("6")
par(mfrow = c(3,3))
lmer.6 <- lmer((d.dependent) ~ (d.date)
+ d.prey.density * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[6] <- lmer.6
print("7")
par(mfrow = c(3,3))
lmer.7 <- lmer((d.dependent) ~ (d.date)
+ c.rank.3.level * d.prey.density
+ c.rank.3.level * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[7] <- lmer.7
print("8")
par(mfrow = c(3,3))
lmer.8 <- lmer((d.dependent) ~ (d.date)
+ c.rank.3.level * d.prey.density
+ d.prey.density * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[8] <- lmer.8
print("9")
par(mfrow = c(3,3))
lmer.9 <- lmer((d.dependent) ~ (d.date)
+ d.serology.index.rank.igg + d.serology.index.rank.igm
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[9] <- lmer.9
print("10")
par(mfrow = c(3,3))
lmer.10 <- lmer((d.dependent) ~ (d.date)
+ c.rank.3.level * d.prey.density
+ c.rank.3.level * c.reproductive.status
+ d.prey.density * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[10] <- lmer.10
print("11")
par(mfrow = c(3,3))
lmer.11 <- lmer((d.dependent) ~ (d.date)
+ d.age.months # + d.minutes.blood # + d.total.solids + d.mass +
+ c.rank.3.level * d.prey.density
+ c.rank.3.level * c.reproductive.status
+ d.prey.density * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[11] <- lmer.11
# plotLMER3d.fnc(lmer.11, pred = "c.rank.3.level", intr = "d.prey.density", plot.type = "contour")
# plotLMER3d.fnc(lmer.11, pred = "c.rank.3.level", intr = "c.reproductive.status", plot.type = "persp")
# plotLMER3d.fnc(lmer.11, pred = "d.prey.density", intr = "c.reproductive.status", plot.type = "persp")
# plotLMER3d.fnc(lmer.23, pred = "c.rank.3.level", intr = "d.precipitation", plot.type = "persp3d")
# plotRaw3d.fnc(data = df.temp, response = "d.dependent", pred = "c.rank.3.level", intr = "d.precipitation", plot.type = "contour")
# plotRaw3d.fnc(data = df.temp, response = "d.dependent", pred = "c.rank.3.level", intr = "d.precipitation", plot.type = "persp")
print("12")
par(mfrow = c(3,3))
lmer.12 <- lmer((d.dependent) ~ (d.date)
+ d.age.months # + d.mass + d.minutes.blood + d.total.solids
+ d.serology.index.rank.igg # + d.serology.index.rank.igm
# + d.total.igg + d.total.igm
+ c.rank.3.level * d.prey.density
+ c.rank.3.level * c.reproductive.status
+ d.prey.density * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[12] <- lmer.12
# plotLMER3d.fnc(lmer.12, pred = "c.rank.3.level", intr = "d.prey.density", plot.type = "contour")
# plotLMER3d.fnc(lmer.12, pred = "c.rank.3.level", intr = "c.reproductive.status", plot.type = "persp")
# plotLMER3d.fnc(lmer.12, pred = "d.prey.density", intr = "c.reproductive.status", plot.type = "persp")
print("13")
par(mfrow = c(3,3))
lmer.13 <- lmer((d.dependent) ~ (d.date)
+ d.minutes.blood
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[13] <- lmer.13
print("14")
par(mfrow = c(3,3))
lmer.14 <- lmer((d.dependent) ~ (d.date)
+ d.age.months * c.reproductive.status # + d.minutes.blood # + d.total.solids + d.mass +
+ c.rank.3.level * d.prey.density
+ c.rank.3.level * c.reproductive.status
+ d.prey.density * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[14] <- lmer.14
print("15")
par(mfrow = c(3,3))
lmer.15 <- lmer((d.dependent) ~ (d.date)
+ d.age.months # + d.mass + d.minutes.blood + d.total.solids
+ d.serology.index.rank.igg # + d.serology.index.rank.igm
# + d.total.igg + d.total.igm
+ c.rank.3.level # * d.prey.density
+ c.reproductive.status
+ d.prey.density # * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[15] <- lmer.15
print("16")
par(mfrow = c(3,3))
lmer.16 <- lmer((d.dependent) ~ (d.date)
+ d.age.months # + d.mass + d.minutes.blood + d.total.solids
+ c.rank.3.level # * d.prey.density
+ c.reproductive.status
+ d.prey.density # * c.reproductive.status
+ d.precipitation
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[16] <- lmer.16
print("17")
par(mfrow = c(3,3))
lmer.17 <- lmer((d.dependent) ~ (d.date)
+ d.age.months # + d.mass + d.minutes.blood + d.total.solids
+ d.cortisol
+ d.testosterone
+ d.mass
+ c.rank.3.level # * d.prey.density
+ c.reproductive.status
+ d.prey.density # * c.reproductive.status
+ d.precipitation
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[17] <- lmer.17
print("18")
par(mfrow = c(3,3))
lmer.18 <- lmer((d.dependent) ~ (d.date)
+ d.age.months # + d.mass + d.minutes.blood + d.total.solids
# + d.cortisol
+ d.testosterone
+ d.mass
+ c.rank.3.level # * d.prey.density
+ c.reproductive.status * d.cortisol
+ d.prey.density # * c.reproductive.status
+ d.precipitation
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[18] <- lmer.18
print("19")
par(mfrow = c(3,3))
lmer.19 <- lmer((d.dependent) ~ (d.date)
+ d.precipitation # * d.prey.density
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[19] <- lmer.19
print("20")
par(mfrow = c(3,3))
lmer.20 <- lmer((d.dependent) ~ (d.date)
+ c.rank.3.level * d.prey.density
+ c.rank.3.level * d.precipitation
+ c.rank.3.level * c.reproductive.status
+ d.prey.density * d.precipitation
+ d.prey.density * c.reproductive.status
+ d.precipitation * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[20] <- lmer.20
print("21")
par(mfrow = c(3,3))
lmer.21 <- lmer((d.dependent) ~ (d.date)
+ d.age.months
+ c.rank.3.level * d.prey.density
+ c.rank.3.level * d.precipitation
+ c.rank.3.level * c.reproductive.status
+ d.prey.density * d.precipitation
+ d.prey.density * c.reproductive.status
+ d.precipitation * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[21] <- lmer.21
print("22")
par(mfrow = c(3,3))
lmer.22 <- lmer((d.dependent) ~ (d.date)
+ d.age.months * c.rank.3.level
+ d.age.months * d.prey.density
+ d.age.months * d.precipitation
+ d.age.months * c.reproductive.status
+ c.rank.3.level * d.prey.density
+ c.rank.3.level * d.precipitation
+ c.rank.3.level * c.reproductive.status
+ d.prey.density * d.precipitation
+ d.prey.density * c.reproductive.status
+ d.precipitation * c.reproductive.status
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[22] <- lmer.22
print("23")
par(mfrow = c(3,3))
lmer.23 <- lmer((d.dependent) ~ (d.date)
+ c.rank.3.level * d.precipitation
+ c.rank.3.level * d.age.months
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
l.models[23] <- lmer.23
plotLMER3d.fnc(lmer.23, pred = "c.rank.3.level", intr = "d.precipitation", plot.type = "contour")
plotLMER3d.fnc(lmer.23, pred = "c.rank.3.level", intr = "d.precipitation", plot.type = "persp")
# plotLMER3d.fnc(lmer.23, pred = "c.rank.3.level", intr = "d.precipitation", plot.type = "persp3d")
plotRaw3d.fnc(data = df.temp, response = "d.dependent", pred = "c.rank.3.level", intr = "d.precipitation", plot.type = "contour")
plotRaw3d.fnc(data = df.temp, response = "d.dependent", pred = "c.rank.3.level", intr = "d.precipitation", plot.type = "persp")
print("24")
lmer.24 <- lmer((d.dependent) ~ (d.date)
# + c.rank.3.level
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
print("25")
lmer.25 <- lmer((d.dependent) ~ (d.date)
+ d.cortisol
+ (1 | c.id),
data = df.temp, REML = FALSE, na.action = na.omit, control = list(maxIter = 1000))
print(cftest(lmer.25))
print(pamer.fnc(lmer.25))
plot(resid(lmer.25) ~ fitted(lmer.25), main = terms(lmer.25))
hist(resid(lmer.25), main = paste(c.dependent, ", lmer.25", sep = ""))
qqnorm(resid(lmer.25))
qqline(resid(lmer.25))
mcp.fnc(lmer.25)
print("d")
save(l.models, file = "test.RData")
# save.image()
# unlink("test.RData")
i.number.of.models <- 23
i <- 1
for(i in i:i.number.of.models)
{
warning()
print(" ********************************************************************************************" )
print(paste(c.dependent, ", loop: ", i))
par(mfrow = c(2,2))
# print("l.models[[i]]")
# print(l.models[[i]])
print("length(resid(l.models[[i]]))")
print(length(resid(l.models[[i]])))
print(cftest(l.models[[i]]))
if(i != 17 & i != 19 & i != 20 & i != 22 & i != 23)
{
# print(pamer.fnc(l.models[[i]]), ndigits = 8)
}
plot(resid(l.models[[i]]) ~ fitted(l.models[[i]]), main = terms(l.models[[i]]))
hist(resid(l.models[[i]]), main = paste(c.dependent, ", lmer.", i, sep = ""))
qqnorm(resid(l.models[[i]]))
qqline(resid(l.models[[i]]))
mcp.fnc(l.models[[i]])
# plotRaw3d.fnc(l.models[[i]], pred = "c.rank.3.level", intr = "d.precipitation")
# plotLMER3d.fnc(l.models[[i]], pred = "c.rank.3.level", intr = "d.precipitation")
# print(lillie.test(resid(l.models[[i]])))
print(ad.test(resid(l.models[[i]])))
# print(cvm.test(resid(l.models[[i]])))
}
# df.temp.full$d.total.igg <- df.temp.full$d.blank.abs.corrected.total.igg
# df.temp.full$d.total.igm <- df.temp.full$d.blank.abs.corrected.total.igm
# df.temp.full$d.ec.igg <- df.temp.full$d.blank.abs.ec.igg
# df.temp.full$d.pm.igg <- df.temp.full$d.blank.abs.pm.igg
# df.temp.full$d.ec.igm <- df.temp.full$d.blank.abs.ec.igm
# df.temp.full$d.pm.igm <- df.temp.full$d.blank.abs.pm.igm
t.aicc <- aictab(cand.set = list(lmer.1, lmer.2, lmer.3, lmer.4, lmer.5, lmer.6, lmer.7, lmer.8, lmer.9, lmer.10, lmer.11,
lmer.12, lmer.13, lmer.14, lmer.15, lmer.16, lmer.17, lmer.18, lmer.19, lmer.20, lmer.21, lmer.22, lmer.23, lmer.24),
modnames = c("lmer.1", "lmer.2", "lmer.3", "lmer.4", "lmer.5", "lmer.6", "lmer.7", "lmer.8", "lmer.9", "lmer.10",
"lmer.11", "lmer.12", "lmer.13", "lmer.14", "lmer.15", "lmer.16", "lmer.17", "lmer.18", "lmer.19", "lmer.20", "lmer.21", "lmer.22",
"lmer.23", "lmer.24"),
sort = TRUE)
print("t.aicc")
print(t.aicc)
write.csv(t.aicc, file = paste("t.aicc.", cv.dependent[i.counter.2], ".csv", sep = ""))
print("")
par(mfrow = c(3,3))
}
par(mfrow = c(1,1))
plot(1,1, main = paste("space filler"))
# df.output.final <- cbind(df.output.final, i.replicates)
# write.csv(x = df.output.final, file = paste("df.ouput.final.", c.independent, ".csv", sep = ""))
# print("df.output.final.csv is written", sep = "")
d.end.time <- Sys.time()
print(d.start.time)
print(d.end.time)
}
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