mdlama/milkweed

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In mdlama/milkweed:

Initialization

library(milkweed)
library(dplyr)
library(tidyr)
library(ggplot2)
library(lme4)
library(AICcmodavg)
library(vegan)
library(flextable)
library(webshot)

requirePackages("DHARMa")

data("stemdata")
metadata <- stemdata
metadata$year <- factor(metadata$year)
metadata$transect <- factor(metadata$transect)

# https://stat.ethz.ch/pipermail/r-sig-mixed-models/2014q4/022870.html
glmerCtrl <- glmerControl(optimizer = c("bobyqa"), optCtrl = list(maxfun=50000))
lmerCtrl <- lmerControl(optimizer = c("bobyqa"), optCtrl = list(maxfun=50000))

mwCache <- getOption("milkweed.cache")

metadata_usc <- metadata %>% filter(!is.na(h_apical),
                                    !is.na(herb_avg),
                                    !is.na(fec.flower))

metadata_sc <- metadata_usc %>% mutate_at(.vars = vars(h_apical, herb_avg),
                                          .funs = ~ as.numeric(scale(.))) 

# Get scaling parameters (really just mean and sd)
h_apical_sc <- scale(metadata_usc$h_apical)
herb_avg_sc <- scale(metadata_usc$herb_avg)

Random Effects

Starting with site

Use nAGQ = 0 for quick approximations, but less exact form of parameter estimation.

compute <- params$compute
savefile <- "aictabs_F_RE_Site.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  # 3 terms
  flower.re.site_haThe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 2 terms
  flower.re.site_hePhaIhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (herb_avg + h_apical:herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhaIhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical + h_apical:herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical + herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 1 term
  flower.re.site_he.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_ha.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haIhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical:herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 0 terms
  flower.re.site_1.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (1|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  save(flower.re.site_haThe.year_haThe,
       flower.re.site_hePhaIhe.year_haThe,
       flower.re.site_haPhaIhe.year_haThe,
       flower.re.site_haPhe.year_haThe,
       flower.re.site_he.year_haThe,
       flower.re.site_ha.year_haThe,
       flower.re.site_haIhe.year_haThe,
       flower.re.site_1.year_haThe, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}

aictab(list(flower.re.site_haThe.year_haThe,
            flower.re.site_hePhaIhe.year_haThe,
            flower.re.site_haPhaIhe.year_haThe,
            flower.re.site_haPhe.year_haThe,
            flower.re.site_he.year_haThe,
            flower.re.site_ha.year_haThe,
            flower.re.site_haIhe.year_haThe,
            flower.re.site_1.year_haThe),
       modnames = c("Site: height*herb", 
                    "Site: herb + height:herb", 
                    "Site: height + height:herb", 
                    "Site: height + herb",
                    "Site: herb",
                    "Site: height",
                    "Site: height:herb",
                    "Site: 1"))

Now do year with height|site and height+height:herb|site.

compute <- params$compute
savefile <- "aictabs_F_RE_Site_Year_1.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  # 3 terms
  flower.re.site_ha.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 2 terms
  flower.re.site_ha.year_hePhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical:herb_avg + herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_ha.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_ha.year_haPhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical+herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 1 term
  flower.re.site_ha.year_he <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_ha.year_ha <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_ha.year_haIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 0 terms
  flower.re.site_ha.year_1 <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (1|year), data=metadata_sc, nAGQ=0, family=binomial())

  save(flower.re.site_ha.year_haThe,
       flower.re.site_ha.year_hePhaIhe,
       flower.re.site_ha.year_haPhaIhe,
       flower.re.site_ha.year_haPhe,
       flower.re.site_ha.year_he,
       flower.re.site_ha.year_ha,
       flower.re.site_ha.year_haIhe,
       flower.re.site_ha.year_1, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}

compute <- params$compute
savefile <- "aictabs_F_RE_Site_Year_2.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  # 3 terms
  flower.re.site_haPhaIhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 2 terms
  flower.re.site_haPhaIhe.year_hePhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical:herb_avg + herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhaIhe.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhaIhe.year_haPhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical+herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 1 term
  flower.re.site_haPhaIhe.year_he <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhaIhe.year_ha <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhaIhe.year_haIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 0 terms
  flower.re.site_haPhaIhe.year_1 <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (1|year), data=metadata_sc, nAGQ=0, family=binomial())

  save(flower.re.site_haPhaIhe.year_haThe,
       flower.re.site_haPhaIhe.year_hePhaIhe,
       flower.re.site_haPhaIhe.year_haPhaIhe,
       flower.re.site_haPhaIhe.year_haPhe,
       flower.re.site_haPhaIhe.year_he,
       flower.re.site_haPhaIhe.year_ha,
       flower.re.site_haPhaIhe.year_haIhe,
       flower.re.site_haPhaIhe.year_1, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}

aictab(list(flower.re.site_ha.year_haThe,
       flower.re.site_ha.year_hePhaIhe,
       flower.re.site_ha.year_haPhaIhe,
       flower.re.site_ha.year_haPhe,
       flower.re.site_ha.year_he,
       flower.re.site_ha.year_ha,
       flower.re.site_ha.year_haIhe,
       flower.re.site_ha.year_1,
       flower.re.site_haPhaIhe.year_haThe,
       flower.re.site_haPhaIhe.year_hePhaIhe,
       flower.re.site_haPhaIhe.year_haPhaIhe,
       flower.re.site_haPhaIhe.year_haPhe,
       flower.re.site_haPhaIhe.year_he,
       flower.re.site_haPhaIhe.year_ha,
       flower.re.site_haPhaIhe.year_haIhe,
       flower.re.site_haPhaIhe.year_1),
       modnames = c("Site: height, Year: height*herb", 
                    "Site: height, Year: herb + height:herb", 
                    "Site: height, Year: height + height:herb", 
                    "Site: height, Year: height + herb",
                    "Site: height, Year: herb",
                    "Site: height, Year: height",
                    "Site: height, Year: height",
                    "Site: height, Year: 1",
                    "Site: height+height:herb, Year: height*herb", 
                    "Site: height+height:herb, Year: herb + height:herb", 
                    "Site: height+height:herb, Year: height + height:herb", 
                    "Site: height+height:herb, Year: height + herb",
                    "Site: height+height:herb, Year: herb",
                    "Site: height+height:herb, Year: height",
                    "Site: height+height:herb, Year: height:herb",
                    "Site: height+height:herb, Year: 1"))

Winners: Site: height, Year: heightherb Site: height+height:herb, Year: heightherb

Starting with year

Use nAGQ = 0 for quick approximations, but less exact form of parameter estimation.

compute <- params$compute
savefile <- "aictabs_F_RE_Year.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  # 3 terms
  flower.re.site_haThe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 2 terms
  flower.re.site_haThe.year_haPhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical+herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haThe.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haThe.year_haIhePhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical:herb_avg + herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 1 term
  flower.re.site_haThe.year_ha <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haThe.year_he <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haThe.year_haIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 0 terms
  flower.re.site_haThe.year_1 <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (1|year), data=metadata_sc, nAGQ=0, family=binomial())

  save(flower.re.site_haThe.year_haThe,
       flower.re.site_haThe.year_haIhePhe,
       flower.re.site_haThe.year_haPhaIhe,
       flower.re.site_haThe.year_haPhe,
       flower.re.site_haThe.year_he,
       flower.re.site_haThe.year_ha,
       flower.re.site_haThe.year_haIhe,
       flower.re.site_haThe.year_1, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}

aictab(list(flower.re.site_haThe.year_haThe,
       flower.re.site_haThe.year_haIhePhe,
       flower.re.site_haThe.year_haPhaIhe,
       flower.re.site_haThe.year_haPhe,
       flower.re.site_haThe.year_he,
       flower.re.site_haThe.year_ha,
       flower.re.site_haThe.year_haIhe,
       flower.re.site_haThe.year_1),
       modnames = c("Year: height*herb", 
                    "Year: herb + height:herb", 
                    "Year: height + height:herb", 
                    "Year: height + herb",
                    "Year: herb",
                    "Year: height",
                    "Year: height:herb",
                    "Year: 1"))

Now do site with height*herb|year (already done above) and height+height:herb|year.

compute <- params$compute
savefile <- "aictabs_F_RE_Year_Site.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  # 3 terms
  flower.re.site_haThe.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 2 terms
  flower.re.site_hePhaIhe.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (herb_avg + h_apical:herb_avg|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhaIhe.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical + h_apical:herb_avg|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhe.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical + herb_avg|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 1 term
  flower.re.site_he.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (herb_avg|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_ha.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haIhe.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical:herb_avg|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 0 terms
  flower.re.site_1.year_haPhaIhe <- glmer(fec.flower ~ h_apical*herb_avg + (1|site/transect) + (h_apical+h_apical:herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  save(flower.re.site_haThe.year_haPhaIhe,
       flower.re.site_hePhaIhe.year_haPhaIhe,
       flower.re.site_haPhaIhe.year_haPhaIhe,
       flower.re.site_haPhe.year_haPhaIhe,
       flower.re.site_he.year_haPhaIhe,
       flower.re.site_ha.year_haPhaIhe,
       flower.re.site_haIhe.year_haPhaIhe,
       flower.re.site_1.year_haPhaIhe, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}

compute <- params$compute
savefile <- "aictabs_F_RE_Site.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  # 3 terms
  flower.re.site_haThe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical*herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 2 terms
  flower.re.site_hePhaIhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (herb_avg + h_apical:herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhaIhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical + h_apical:herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haPhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical + herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 1 term
  flower.re.site_he.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_ha.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  flower.re.site_haIhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical:herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  # 0 terms
  flower.re.site_1.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (1|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=0, family=binomial())

  save(flower.re.site_haThe.year_haThe,
       flower.re.site_hePhaIhe.year_haThe,
       flower.re.site_haPhaIhe.year_haThe,
       flower.re.site_haPhe.year_haThe,
       flower.re.site_he.year_haThe,
       flower.re.site_ha.year_haThe,
       flower.re.site_haIhe.year_haThe,
       flower.re.site_1.year_haThe, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}

aictab(list(flower.re.site_haThe.year_haPhaIhe,
       flower.re.site_hePhaIhe.year_haPhaIhe,
       flower.re.site_haPhaIhe.year_haPhaIhe,
       flower.re.site_haPhe.year_haPhaIhe,
       flower.re.site_he.year_haPhaIhe,
       flower.re.site_ha.year_haPhaIhe,
       flower.re.site_haIhe.year_haPhaIhe,
       flower.re.site_1.year_haPhaIhe,
       flower.re.site_haThe.year_haThe,
       flower.re.site_hePhaIhe.year_haThe,
       flower.re.site_haPhaIhe.year_haThe,
       flower.re.site_haPhe.year_haThe,
       flower.re.site_he.year_haThe,
       flower.re.site_ha.year_haThe,
       flower.re.site_haIhe.year_haThe,
       flower.re.site_1.year_haThe),
       modnames = c("Site: height*herb, Year: height+height:herb", 
                    "Site: herb + height:herb, Year: height+height:herb", 
                    "Site: height + height:herb, Year: height+height:herb", 
                    "Site: height + herb, Year: height+height:herb",
                    "Site: herb, Year: height+height:herb",
                    "Site: height, Year: height+height:herb",
                    "Site: height:herb, Year: height+height:herb",
                    "Site: 1, Year: height+height:herb",
                    "Site: height*herb, Year: height*herb", 
                    "Site: herb + height:herb, Year: height*herb", 
                    "Site: height + height:herb, Year: height*herb", 
                    "Site: height + herb, Year: height*herb",
                    "Site: herb, Year: height*herb",
                    "Site: height, Year: height*herb",
                    "Site: height:herb, Year: height*herb",
                    "Site: 1, Year: height*herb"))

Winners: Site: height, Year: heightherb Site: height + height:herb, Year: heightherb

Winners for both

Site: height, Year: heightherb Site: height + height:herb, Year: heightherb

Singularity check

Start with more complex model.

compute <- params$compute
savefile <- "aictabs_F_Sing_1.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  flower.fe.haThe.re.site_haPhaIhe.year_haThe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical*herb_avg|year), data=metadata_sc, nAGQ=1, family=binomial(), control=glmerCtrl)

  save(flower.fe.haThe.re.site_haPhaIhe.year_haThe, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}
rePCA(flower.fe.haThe.re.site_haPhaIhe.year_haThe)

Remove year interaction term.

compute <- params$compute
savefile <- "aictabs_F_Sing_2.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  flower.fe.haThe.re.site_haPhaIhe.year_haPhe <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical+herb_avg|year), data=metadata_sc, nAGQ=1, family=binomial(), control=glmerCtrl)

  save(flower.fe.haThe.re.site_haPhaIhe.year_haPhe, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}
rePCA(flower.fe.haThe.re.site_haPhaIhe.year_haPhe)

Remove herb_avg from year.

compute <- params$compute
savefile <- "aictabs_F_Sing_3.RData"

if (!file.exists(file.path(mwCache,savefile)) || (compute)) {
  flower.fe.haThe.re.site_haPhaIhe.year_ha <- glmer(fec.flower ~ h_apical*herb_avg + (h_apical+h_apical:herb_avg|site/transect) + (h_apical|year), data=metadata_sc, nAGQ=1, family=binomial(), control=glmerCtrl)

  save(flower.fe.haThe.re.site_haPhaIhe.year_ha, 
       file=file.path(mwCache,savefile))
} else {
  load(file.path(mwCache,savefile))
}
rePCA(flower.fe.haThe.re.site_haPhaIhe.year_ha)


mdlama/milkweed documentation built on Sept. 15, 2022, 9:24 a.m.

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