MIIN_5_metaAnalysis.Rmd

---
title: "MIIN Part 5: Meta-analysis"
author: "Marissa Lee"
date: "January 21, 2015"
output: pdf_document
---

**Filename: MIIN_5_metaAnalysis.Rmd**  
**This markdown file does the following tasks:**  

1. Calculate global effect sizes and plot

2. Test for inclusion of categorical moderators.  Where a categorical moderator is warrented, make a forest plot and conduct post-hoc Tukeys: A) Study type, B) N fixer status, C) Ecosystem type, D) Quality  

3. Test for correlation between CWM traits and effect size values. Record the outcomes in a table. In cases where the slope coefficient significantly differs from 0, plot it.

```{r libraries, echo=TRUE}
#knitr::opts_chunk$set(cache=TRUE)

require(ggplot2)
require(gridExtra)
require(plyr)
require(reshape2)
require(metafor)

source('CODE/mytheme.R')
source('CODE/metaAnalysis/fxn_PrepForestPlot.R')
source('CODE/metaAnalysis/fxn_SaveQstats.R')
source('CODE/metaAnalysis/fxn_FitPlot.R')

figuresPath<-file.path(getwd()[1], "FIGURES_TABLES", "metaAnalysis") #where to put the saved plots
fig.height<-2.5 #inches
fig.width<- 2.5 #inches
fig.res<-300

dat<-read.table("DATA/DATA_SYNTHESIZED/metaDataset.txt", header=TRUE, sep="\t")
metaSummary<-read.table("DATA/metaSummary.txt", header=TRUE, sep="\t")
source('CODE/metaAnalysis/script_orderLevels.R')

#dat structure
summ<-ddply(dat, ~measCat+traitCat, summarize,
                 uniqueObs = length(unique(obsID)))
#summ
```

_________________________________________________________________
# 1. Calculate global effect sizes and plot
1A. Fit a nested random effects model. Model syntax is res <- rma.mv(yi, vi, random=list(~1 | paperID, ~1 | obsID), data=dat1, subset=measCat==MEASCAT[i], slab=paste(paperID,obsID, sep=","))
```{r calcGlob, echo=TRUE, warning=FALSE, message=FALSE}

#subset unique effect size data
dat1<-dat[!is.na(dat$yi),]
dat.meas<-ddply(dat1, ~obsID+measCat, summarize,
                yi = unique(yi),
                vi = unique(vi),
                n1i<-unique(n1i),
                n2i<-unique(n2i),
                paperID = unique(paperID))

colnames(dat.meas)<-c('obsID','measCat','yi','vi','n1i','n2i','paperID')

#run random effects models to determine grand effect sizes and produce funnel plots
res.list<-list()
res.pubBias.list<-list()
i<-0
newfilename<-"funnelPlots.pdf"
pdf(paste(figuresPath,'globalForest',newfilename, sep='/'), width = fig.width*3, height = fig.height*3)
par(mfrow=c(3,3))
for(i in 1:length(MEASCAT)){
  
  #subset by MEASCAT
  df<-subset(dat.meas, measCat==MEASCAT[i])
  #random effects model
  res <- rma.mv(yi, vi, 
                random= ~1 | paperID/obsID,
                data=df, 
                slab=as.character(obsID), 
                method='REML')
  res.list[[i]]<-res
  
  #evaluate publication bias
  ## funnel plot
  funnel(res, main=labels[i])
  
  ## correlation between effect size and sample size
  modtab<-summary(lm(yi~n1i, data=df))$coefficients
  est<-round(modtab['n1i','Estimate'], digits=3)
  pval<-round(modtab['n1i','Pr(>|t|)'], digits=3)
  spearman.r<-round(cor(x=df$n1i, y=df$yi, method='spearman'), digits=3)
  #file-drawer problem (aka fail-safe number)
  res.fsn<-fsn(yi, vi, data=df)
  fsnum.n<-res.fsn$fsnum
  fsnum.pval<-round(res.fsn$pval, digits=3)
  #save everything
  res.pubBias.list[[i]]<-data.frame(MEASCAT[i], est, pval, spearman.r, fsnum.n, fsnum.pval)
  
  #print progress
  print(paste(i, 'of', length(MEASCAT)))
}
dev.off()
names(res.list)<-MEASCAT
names(res.pubBias.list)<-MEASCAT

#random effects model results
resultdf<-PrepForestPlot(res.list) #save estimates, pvalues, and prep dataframe for plotting
resultdf
newfilename<-'globalMeans.txt'
write.table(resultdf, file=paste(figuresPath,'globalForest',newfilename, sep='/'), sep='\t')

#extract QE and QM information from res.list
Qstats<-SaveQstats(res.list) #save Q stats
Qstats
newfilename<-'globalMeans_Qstats.txt'
write.table(Qstats, file=paste(figuresPath,'globalForest',newfilename, sep='/'), sep='\t')

#evaluate publication bias -- correlation table
pubBias1.tab<-ldply(res.pubBias.list)
pubBias1.tab
newfilename<-'globalMeans_pubBias1.txt'
write.table(pubBias1.tab, file=paste(figuresPath,'globalForest',newfilename, sep='/'), sep='\t')

#show that yi's are normally distributed
# Q-Q plots
dat.measP<-dat.meas
dat.measP$measCat<-factor(dat.measP$measCat, levels=MEASCAT)
levels(dat.measP$measCat)<-labels
qq<-ggplot(dat.measP, aes(sample=yi)) + 
  facet_wrap(~measCat, scales='free', ncol=3) +
  stat_qq() + mytheme + ggtitle('QQ Plots of effect sizes')
qq

newfilename<-"globalMeans_qqPlots.pdf"
pdf(paste(figuresPath,'globalForest',newfilename, sep='/'), 
    width = fig.width*3, height = fig.height*3)
qq
dev.off()

```
1B. Plot the results of this study relative to previous meta-analyses
```{r calcGlob_Liao, echo=TRUE, warning=FALSE, message=FALSE, include=FALSE}

#get rid of NAs
metaSum<-metaSummary[!is.na(metaSummary$est),]

#column that indicates whether the CI overlaps 0
overlap0<-metaSum$cil<0 &  metaSum$ciu>0
metaSum$NoOverlap0<-!overlap0

#fix levels
metaSum$measCat<-factor(metaSum$measCat, levels=MEASCAT)
old.study<-c('thisStudy','liao','vila','castroDiez')
STUDY<-c('This Study','Liao 2008','Vila 2011','Castro-Diez 2014')
library(plyr)
metaSum$study<-mapvalues(metaSum$study, from = old.study, to = STUDY)
metaSum$study<-factor(metaSum$study, levels=STUDY)
colnames(metaSum)[1]<-'MEASCAT'
metaSum<-merge(metaSum,measTab)

#update effect size table for plotting with ggplot
metaSum$y<-NA
CATl<-rev(levels(metaSum$MEASCAT))
i<-0
for(i in 1:length(CATl)){
  metaSum[metaSum$MEASCAT==CATl[i] & metaSum$study==STUDY[4],'y']<-i-0.3 
  metaSum[metaSum$MEASCAT==CATl[i] & metaSum$study==STUDY[3],'y']<-i-0.1
  metaSum[metaSum$MEASCAT==CATl[i] & metaSum$study==STUDY[2],'y']<-i+0.1
  metaSum[metaSum$MEASCAT==CATl[i] & metaSum$study==STUDY[1],'y']<-i+0.3 
}
cols <- c("blue","darkgray","darkgray","darkgray")
shapes <- c(16,16,1,0)

#make ggplot plot
glob.sp<-ggplot(data=metaSum, aes(x=est, y=y, label=k, color=study, shape=study)) + mytheme + 
  geom_point(size=2) +
  geom_errorbarh(aes(xmin=cil,xmax=ciu), height=0) +
  geom_vline(xintercept=0,linetype="dashed") + 
  xlab('Effect size (RR)') + 
  ylab('') +
  geom_text(aes(x=ciu, y=y, hjust=-0.5, vjust=0.5),size=2.2, show_guide = FALSE) +
  theme(legend.title=element_text(size=8)) +
  scale_y_continuous(breaks=seq(1,length(MEASCAT)),labels=rev(labels), 
                     limits=c(0.5,9.5), expand=c(0,0)) +
  xlim(-2,2) +
  scale_color_manual(name='Source',values=cols) +
  scale_shape_manual(name='Source',values=shapes)
glob.sp

#save global forest plot
newfilename<-"globalForest_meta.pdf"
pdf(paste(figuresPath,'globalForest',newfilename, sep='/'), width = fig.width*1.8, height = fig.height*2.5)
glob.sp
dev.off()

```

_________________________________________________________________
# 2. Test for inclusion of categorical moderators.
2A. Fit a random-effects model with 1 of the categorical variables as a fixed effect. Model syntax is res <- rma.mv(yi, vi, mods= ~factor(Xmod), random=list(~1 | paperID, ~1 | obsID), data=dat1, subset=measCat==MEASCAT[i], slab=paste(paperID,obsID, sep=",")) where Xmod is one of the following: (1) Quality, (2) Ecosystem type, (3) Study type, (4) Invasive legumes present/absent, (5) Reference legumes present/absent
```{r calcFact, echo=TRUE, warning=FALSE, message=FALSE}
dat.meas<-ddply(dat, ~obsID+measCat, summarize,
                yi = unique(yi),
                vi = unique(vi),
                paperID = unique(paperID),
                measQuality= unique(measQuality),
                ecosystCat= unique(ecosystCat),
                studyType= unique(studyType),
                Nfix= unique(Nfix))
dat.tmp<-dat.meas[dat.meas$measCat %in% MEASCAT,]
dat.tmp<-dat.tmp[!is.na(dat.tmp$yi),]

#look for singularities
temp<-ddply(dat.tmp, ~measCat+Nfix, summarize, n=length(paperID))
temp
which(temp$n==1)

#for the ecosystCat, remove the 'other' level from the model
dat.tmp<-dat.tmp[dat.tmp$ecosystCat != 'other',]
dat.tmp$ecosystCat<-droplevels(dat.tmp$ecosystCat)

### fit 3-level random-effects models with a categorical mod
res<-list()
res.quality<-list()
res.ecosyst<-list()
res.studytype<-list()
res.Nfix<-list()
i<-0
for(i in 1:length(MEASCAT)){
  subdat<-subset(dat.tmp, measCat==MEASCAT[i])
  
  #reduced model
  res[[i]]<- rma.mv(yi, vi, 
                    random= ~1 | paperID/obsID,
                    data=subdat, slab=as.character(obsID),
                    method='REML')
  
  #full models
  res.quality[[i]]<- rma.mv(yi, vi, 
                            mods= ~factor(measQuality), 
                            random= ~1 | paperID/obsID, 
                            data=subdat, slab=as.character(obsID),
                            method='REML')
  res.ecosyst[[i]]<- rma.mv(yi, vi, 
                            mods= ~factor(ecosystCat), 
                            random= ~1 | paperID/obsID, 
                            data=subdat, slab=as.character(obsID),
                            method='REML')
  res.studytype[[i]]<- rma.mv(yi, vi, 
                              mods= ~factor(studyType), 
                              random= ~1 | paperID/obsID, 
                              data=subdat, slab=as.character(obsID),
                              method='REML')
  res.Nfix[[i]]<- rma.mv(yi, vi, 
                           mods= ~factor(Nfix), 
                           random= ~1 | paperID/obsID, 
                           data=subdat, slab=as.character(obsID),
                         method='REML')
  print(paste(i, 'of', length(MEASCAT)))
}
names(res)<-MEASCAT
names(res.quality)<-MEASCAT
names(res.ecosyst)<-MEASCAT
names(res.studytype)<-MEASCAT
names(res.Nfix)<-MEASCAT



#Test whether inclusion of any of these moderators are warrented
ANOVAParams<-function(fullMod, reducedMod){
  
  #do anova comparison
  #anova.comp<-anova(fullMod,reducedMod)
  QM<-fullMod$QM
  QMp<-fullMod$QMp
  
  #manually calculate the pseudo R2 values
  #compute the proportional reduction in the variance components as a sort of pseudo R-squared value
  pseudoR2.1<-(reducedMod$sigma2[1] - fullMod$sigma2[1]) / reducedMod$sigma2[1]
  pseudoR2.2<-(reducedMod$sigma2[2] - fullMod$sigma2[2]) / reducedMod$sigma2[2]
  pseudoR2.perc<-(sum(reducedMod$sigma2) - sum(fullMod$sigma2)) / sum(reducedMod$sigma2)
  anova.comp.df<-data.frame(QM=round(QM, digits=3),
                            QMp=round(QMp, digits=3),
                            pseudoR2.1=round(pseudoR2.1, digits=3), #pseudo R2 for the 1st sigma2
                            pseudoR2.2=round(pseudoR2.2, digits=3), #pseudo R2 for the 2nd sigma2
                            pseudoR2.perc=round(pseudoR2.perc, digits=3)
                            )
#   #put everything in a table
#   anova.comp.df<-data.frame(p.f=anova.comp$p.f, #number of parameters in the full model
#                             LRT=round(anova.comp$LRT, digits=3), #likelihood ratio statistic
#                             pval=round(anova.comp$pval, digits=3), #p-value for the likelihood ratio test
#                             pseudoR2.1=round(pseudoR2.1, digits=3), #pseudo R2 for the 1st sigma2
#                             pseudoR2.2=round(pseudoR2.2, digits=3), #pseudo R2 for the 2nd sigma2
#                             pseudoR2.perc=round(pseudoR2.perc, digits=3) #the proportional reduction in the total variance
                            #) 
  return(anova.comp.df)
}
i<-0
tmp.list<-list()
for(i in 1:length(MEASCAT)){
  
#   #reduced model parameters
#   p.r<-anova(res.quality[[i]], res[[i]])$p.r 
  
  #full vs reduced model anova and extract params
  qualityA<-ANOVAParams(fullMod=res.quality[[i]], reducedMod=res[[i]]) 
  ecosystA<-ANOVAParams(fullMod=res.ecosyst[[i]], reducedMod=res[[i]])
  studytypeA<-ANOVAParams(fullMod=res.studytype[[i]], reducedMod=res[[i]])
  NfixA<-ANOVAParams(fullMod=res.Nfix[[i]], reducedMod=res[[i]])
  tmpdf<-rbind(qualityA, ecosystA, studytypeA, NfixA) #same anova params in a dataframe
  anovaLabels<-c('qualityA','ecosystA','studytypeA','NfixA') #name all the rows
  
#   #add a column to hold the reduced model information
#   p.rCol<-rep(p.r, dim(tmpdf)[1])
  
  #save df
  tmp.list[[i]]<-data.frame(anovaLabels, tmpdf)
  
}
names(tmp.list)<-MEASCAT
anovaMods<-ldply(tmp.list)
anovaMods
newfilename<-'anovaMods.txt'
write.table(anovaMods, file=paste(figuresPath,'modForests',newfilename, sep='/'), sep='\t')

```
2Bi. Study type
```{r studytype, echo=TRUE, warning=FALSE, message=FALSE}
currMeasFac<-anovaMods[anovaMods$anovaLabels=='studytypeA' & anovaMods$QMp < 0.05,'.id']
currMeasFac

#Run post-hoc comparisions among Study type levels, pull out means and CI
i<-0
posthoc.list<-list()
pred.list<-list()
for(i in 1:length(currMeasFac)){
  #1. calc posthoc pvals
  posthocR<-anova(res.studytype[[currMeasFac[i]]], 
      L=rbind(c(1,-1,0,0), #fs vs ea
              c(1,0,-1,0), #fs vs er
              c(1,0,0,-1), #fs vs gh
              c(0,1,-1,0), #ea vs er
              c(0,1,0,-1), #ea vs gh
              c(0,0,1,-1)  #er vs gh
              ))
  levelcomp<-c('fs_ea','fs_er','fs_gh','ea_er','ea_gh','er_gh')
  posthoc.list[[i]]<-data.frame(levelcomp, pval=round(posthocR$pval, digits=4))
  #2. calc predicted values by level
  predR<-predict(res.studytype[[currMeasFac[i]]])
  tmp<-data.frame(obsID=predR$slab, pred=predR$pred, 
                  cilb=predR$ci.lb, ciub=predR$ci.ub, se=predR$se)
  ind.studyType<-ddply(dat.tmp, ~obsID, summarize, studyType1=unique(studyType))
  tmp1<-merge(tmp, ind.studyType)
  tmp2<-ddply(tmp1, ~studyType1, summarize,
        pred = unique(pred),
        cilb = unique(cilb),
        ciub = unique(ciub),
        se = unique(se),
        k = length(obsID))
  pred.list[[i]]<-tmp2
  }
names(posthoc.list)<-currMeasFac
posthocR<-ldply(posthoc.list)
posthocR

names(pred.list)<-currMeasFac
predR<-ldply(pred.list)
predR

#update effect size table for plotting
colnames(predR)[1]<-'MeasFac'
selectMeas<-unique(predR$MeasFac)
MeasFacl<-rev(levels(factor(MEASCAT[MEASCAT %in% selectMeas], levels=MEASCAT[MEASCAT %in% selectMeas])))
MeasNamesl<-rev(measTab[measTab$MEASCAT %in% selectMeas,'labels'])
colnames(predR)[2]<-'CAT'
CAT<-levels(dat$studyType)
predR$y<-NA
i<-0
for(i in 1:length(MeasFacl)){
  predR[predR$CAT==CAT[1] & predR$MeasFac==MeasFacl[i],'y']<-i+0.3
  predR[predR$CAT==CAT[2] & predR$MeasFac==MeasFacl[i],'y']<-i+0.1
  predR[predR$CAT==CAT[3] & predR$MeasFac==MeasFacl[i],'y']<-i-0.1
  predR[predR$CAT==CAT[4] & predR$MeasFac==MeasFacl[i],'y']<-i-0.3
}
predR

#assign post-hoc letters (order:field study, expt addition, expt removl, gh)
#subset(posthocR, .id=='biom')
phTno<-c('a','b','b','b') 
phTsom<-c('a','ab','ab','b')
predR$posthocL<-c(phTno,phTsom)

#assign pretty names and symbols
faclimits<-rev(c('Greenhouse study',
             'Field removal',
             'Field addition',
             'Field observation'))
facShapes<-c(16,17,15,18)
predR$annLabel<-paste(predR$posthocL, ' (',predR$k,')', sep='')

#ggplot
predR1<-predR #get rid of the CIs for a level with only 1 data point
predR1[predR1$MeasFac == 'som' & predR1$CAT == 'field expt addition',c('cilb','ciub')]<-NA
stud.sp<-ggplot(data=predR1,aes(x=pred,y=y, shape=CAT, label=annLabel)) + 
  geom_point(aes(shape=CAT),size=2) +
  geom_errorbarh(aes(xmin=cilb,xmax=ciub), height=0)+
  geom_vline(xintercept=0,linetype="dashed") + mytheme +
  xlab('Std. Mean Diff. (Inv-Ref)') + ylab('') +
  scale_y_continuous(breaks=c(1,2),labels=MeasNamesl) +
  scale_shape_manual(name="Study design",
                     labels=faclimits,
                     values=facShapes)+
  geom_text(aes(x=ciub, y=y, hjust=-0.3, vjust=0.5),size=2.2,show_guide = FALSE) +
  annotate("text", x=1, y=1.1, label=' (1)', size=2.2)+ #manually add the posthoc letters and k here
  scale_x_continuous(expand=c(0,0), limits=c(-1.5,5.3)) +
  theme(legend.title=element_text(size=8))
stud.sp 
#theme(axis.text.y=element_text(angle=90, hjust=0.5, size=10)) 

newfilename<-"studytypeForest.pdf"
pdf(paste(figuresPath,'modForests',newfilename, sep='/'), width = fig.width*1.8, height = fig.height*1)
stud.sp 
dev.off()

```
2Bii. Nfix status
```{r nfix, echo=TRUE, warning=FALSE, message=FALSE}
currMeasFac<-anovaMods[anovaMods$anovaLabels=='NfixA' & anovaMods$QMp < 0.05,'.id']
currMeasFac

#levels(dat.tmp$Nfix) #check to make sure that this lines up with the way I organized the contrasts
#Run post-hoc comparisions among Nfix levels, pull out means and CI
i<-0
posthoc.list<-list()
pred.list<-list()
for(i in 1:length(currMeasFac)){
  #1. calc posthoc pvals
  posthocR<-anova(res.Nfix[[currMeasFac[i]]], 
      L=rbind(c(1,-1,0,0), #I-R- vs I-R+
              c(1,0,-1,0), #I-R- vs I+R-
              c(1,0,0,-1), #I-R- vs I+R+
              c(0,1,-1,0), #I-R+ vs I+R-
              c(0,1,0,-1), #I-R+ vs I+R+
              c(0,0,1,-1)  #I+R- vs I+R+
              ))
  levelcomp<-c('none_r','none_i','none_both','r_i','r_both','i_both')
  posthoc.list[[i]]<-data.frame(levelcomp, pval=round(posthocR$pval, digits=4))
  #2. calc predicted values by level
  predR<-predict(res.Nfix[[currMeasFac[i]]])
  tmp<-data.frame(obsID=predR$slab, pred=predR$pred, cilb=predR$ci.lb, ciub=predR$ci.ub, se=predR$se)
  ind.Fac<-ddply(dat.tmp, ~obsID, summarize, Level=unique(Nfix))
  tmp1<-merge(tmp, ind.Fac)
  tmp2<-ddply(tmp1, ~Level, summarize,
        pred = unique(pred),
        cilb = unique(cilb),
        ciub = unique(ciub),
        se = unique(se),
        k = length(obsID))
  pred.list[[i]]<-tmp2
  }
names(posthoc.list)<-currMeasFac
posthocR<-ldply(posthoc.list)
posthocR

names(pred.list)<-currMeasFac
predR<-ldply(pred.list)
predR

#update effect size table for plotting
colnames(predR)[1]<-'MeasFac'
selectMeas<-unique(predR$MeasFac)
MeasFacl<-rev(levels(factor(MEASCAT[MEASCAT %in% selectMeas], levels=MEASCAT[MEASCAT %in% selectMeas])))
MeasNamesl<-rev(measTab[measTab$MEASCAT %in% selectMeas,'labels'])
colnames(predR)[2]<-'CAT'
CAT<-levels(dat$Nfix)
predR$y<-NA
i<-0
for(i in 1:length(MeasFacl)){
  predR[predR$CAT==CAT[1] & predR$MeasFac==MeasFacl[i],'y']<-i+0.3
  predR[predR$CAT==CAT[2] & predR$MeasFac==MeasFacl[i],'y']<-i+0.1
  predR[predR$CAT==CAT[3] & predR$MeasFac==MeasFacl[i],'y']<-i-0.1
  predR[predR$CAT==CAT[4] & predR$MeasFac==MeasFacl[i],'y']<-i-0.3
}
predR

#assign post-hoc letters (order:field study, expt addition, expt removl, gh)
#subset(posthocR, .id=='litterbiom')
phTtoti<-c('b','a','b','ab') 
phTsoiln<-c('a','ab','b','ab')
phTsoilcn<-c('a','b','a','a') 
predR$posthocL<-c(phTtoti,phTsoiln, phTsoilcn)

#assign pretty names and symbols
faclimits<-unique(predR$CAT)
facShapes<-c(16,17,15,18)
predR$annLabel<-paste(predR$posthocL, ' (',predR$k,')', sep='')

#ggplot
faclimits_new<-c('No N-fixers','Ref. community N-fixers only','Invasive sp. N-fixers only','Ref. and inv. sp. N-fixers')


nfix.sp<-ggplot(data=predR,aes(x=pred,y=y, shape=CAT,label=annLabel)) + 
  geom_point(aes(shape=CAT),size=3) +
  geom_errorbarh(aes(xmin=cilb,xmax=ciub), height=0)+
  geom_vline(xintercept=0,linetype="dashed") + mytheme +
  xlab('Std. Mean Diff. (Inv-Ref)') + ylab('') +
  scale_y_continuous(breaks=seq(1,length(MeasFacl)),
                     labels=MeasNamesl) +
  scale_shape_manual(name="N-fixing status",
                     labels=faclimits_new,
                     values=facShapes)+
  geom_text(aes(x=ciub, y=y, hjust=-0.3, vjust=0.5),size=2.2,show_guide = FALSE) +
  scale_x_continuous(expand=c(0,0), limits=c(-1.3,2.3))+
  theme(legend.title=element_text(size=8))
nfix.sp
#theme(axis.text.y=element_text(angle=90, hjust=0.5, size=10))
  
newfilename<-"nfixForest.pdf"
pdf(paste(figuresPath,'modForests',newfilename, sep='/'), width = fig.width*2, height = fig.height*1.5)
nfix.sp
dev.off()

```
2Biii. Quality
```{r quality, echo=TRUE, warning=FALSE, message=FALSE}
currMeasFac<-anovaMods[anovaMods$anovaLabels=='qualityA' & anovaMods$pval < 0.05,'.id']
currMeasFac
#note - som doesn't have all levels of measQuality, so you need to have a different set of contrasts
#levels(dat.tmp$measQuality) #check to make sure that this lines up with the way I organized the contrasts
#Run post-hoc comparisions among Nfix levels, pull out means and CI
# i<-0
# posthoc.list<-list()
# pred.list<-list()
# for(i in 1:length(currMeasFac)){
#   #1. calc posthoc pvals
#   if(i %in% c(1,2)){
#     posthocR<-anova(res.quality[[currMeasFac[i]]],
#                     # A+C+, A+C-, A-C+, A-C-
#                     L=rbind(c(1,-1,0,0), #A+C+ vs A+C-
#                             c(1,0,-1,0), #A+C+ vs A-C+
#                             c(1,0,0,-1), #A+C+ vs A-C-
#                             c(0,1,-1,0), #A+C- vs A-C+
#                             c(0,1,0,-1), #A+C- vs A-C-
#                             c(0,0,1,-1)  #A-C+ vs A-C-
#                     ))
#     levelcomp<-c('both_a','both_c','both_none','a_c','a_none','c_none')
#     posthoc.list[[i]]<-data.frame(levelcomp, pval=round(posthocR$pval, digits=4))
#   }else{
#     temp1<-subset(dat.tmp, measCat=='som')
#     unique(temp1$measQuality)
#     #levels: A+C-, A-C+, A-C-
#     posthocR<-anova(res.quality[[currMeasFac[i]]], 
#                     L=rbind(c(1,-1,0), #A+C- vs A-C+
#                             c(1,0,-1), #A+C- vs A-C-
#                             c(0,1,-1)  #A-C+ vs A-C-
#                     ))
#     levelcomp<-c('a_c','a_none','c_none')
#     posthoc.list[[i]]<-data.frame(levelcomp, pval=round(posthocR$pval, digits=4))
#   }
#   
#   #2. calc predicted values by level
#   predR<-predict(res.quality[[currMeasFac[i]]])
#   tmp<-data.frame(obsID=predR$slab, pred=predR$pred, cilb=predR$ci.lb, ciub=predR$ci.ub, se=predR$se)
#   ind.Fac<-ddply(dat.tmp, ~obsID+measCat, summarize, Level=measQuality)
#   ind.Fac.sub<-subset(ind.Fac, measCat==currMeasFac[i])
#   tmp1<-merge(tmp, ind.Fac.sub)
#   tmp2<-ddply(tmp1, ~Level, summarize,
#         pred = unique(pred),
#         cilb = unique(cilb),
#         ciub = unique(ciub),
#         se = unique(se),
#         k = length(obsID))
#   pred.list[[i]]<-tmp2
#   
#   }
# names(posthoc.list)<-currMeasFac
# posthocR<-ldply(posthoc.list)
# posthocR
# 
# names(pred.list)<-currMeasFac
# predR<-ldply(pred.list)
# predR
# 
# #update effect size table for plotting
# colnames(predR)[1]<-'MeasFac'
# selectMeas<-unique(predR$MeasFac)
# MeasFacl<-rev(levels(factor(MEASCAT[MEASCAT %in% selectMeas], levels=MEASCAT[MEASCAT %in% selectMeas])))
# MeasNamesl<-rev(measTab[measTab$MEASCAT %in% selectMeas,'labels'])
# colnames(predR)[2]<-'CAT'
# CAT<-levels(dat$measQuality)
# predR$y<-NA
# i<-0
# for(i in 1:length(MeasFacl)){
#   predR[predR$CAT==CAT[1] & predR$MeasFac==MeasFacl[i],'y']<-i+0.3
#   predR[predR$CAT==CAT[2] & predR$MeasFac==MeasFacl[i],'y']<-i+0.1
#   predR[predR$CAT==CAT[3] & predR$MeasFac==MeasFacl[i],'y']<-i-0.1
#   predR[predR$CAT==CAT[4] & predR$MeasFac==MeasFacl[i],'y']<-i-0.3
# }
# predR
# 
# #assign post-hoc letters (order ex:both, a, c, none)
# #subset(posthocR, .id=='som')
# phTnitrif<-c('','','','') #both vs c is marginally signif (p=0.059) 
# phTnminz<-c('ab','ab','b','a') #both vs c is marginally signif (p=0.080), c vs none is signif (p=0.038)
# phTsom<-c('ab','a','b') #c vs none pval=0.0128 
# predR$posthocL<-c(phTnitrif,phTnminz, phTsom)
# 
# #assign pretty names and symbols
# faclimits<-rev(c('No Manipulation','Units Converted',
#                  'Values Aggregated','Units Converted &\nValues Aggregated'))
# facShapes<-c(16,17,15,18)
# predR$annLabel<-paste(predR$posthocL, ' (',predR$k,')', sep='')
# 
# #ggplot
# qual.sp<-ggplot(data=predR,aes(x=pred,y=y, shape=CAT,label=annLabel)) + 
#   geom_point(aes(shape=CAT),size=3) +
#   geom_errorbarh(aes(xmin=cilb,xmax=ciub), height=0)+
#   geom_vline(xintercept=0,linetype="dashed") + mytheme +
#   xlab('Std. Mean Diff. (Inv-Ref)') + ylab('') +
#   scale_shape_manual(name="Factor",
#                      labels=faclimits,
#                      values=facShapes) +
#   scale_y_continuous(breaks=seq(1,length(MeasFacl)),
#                      labels=MeasNamesl) +
#   geom_text(aes(x=ciub, y=y, hjust=-0.3, vjust=0.5),size=2.2,show_guide = FALSE) +
#   scale_x_continuous(expand=c(0,0),limits=c(-3,2.3))
# qual.sp + theme(axis.text.y=element_text(angle=90, hjust=0.5, size=10))
# 
# newfilename<-"qualityForest.png"
# png(paste(figuresPath,'modForests',newfilename, sep='/'), units='in', width = fig.width*2, height = fig.height*2, res=fig.res)
# qual.sp + theme(axis.text.y=element_text(angle=90, hjust=0.5, size=10))
# dev.off()

```
2Biv. Ecosystem type
```{r ecosyst, echo=TRUE, warning=FALSE, message=FALSE}
currMeasFac<-anovaMods[anovaMods$anovaLabels=='ecosystA' & anovaMods$pval < 0.05,'.id']
currMeasFac

# #levels(dat.tmp$ecosystCat) #check to make sure that this lines up with the way I organized the contrasts
# #Run post-hoc comparisions among Nfix levels, pull out means and CI
# i<-0
# posthoc.list<-list()
# pred.list<-list()
# for(i in 1:length(currMeasFac)){
#   #1. calc posthoc pvals
#   # forest, shrubland, grassland, wetland
#   posthocR<-anova(res.ecosyst[[currMeasFac[i]]], 
#       L=rbind(c(1,-1,0,0), #forest vs shrub
#               c(1,0,-1,0), #forest vs grass
#               c(1,0,0,-1), #forest vs wet
#               c(0,1,-1,0), #shrub vs grass
#               c(0,1,0,-1), #shrub vs wet
#               c(0,0,1,-1) #grass vs wet
#               ))
#   levelcomp<-c('f_s','f_g','f_w',
#                      's_g','s_w',
#                            'g_w')
#   posthoc.list[[i]]<-data.frame(levelcomp, pval=round(posthocR$pval, digits=4))
#   #2. calc predicted values by level
#   predR<-predict(res.ecosyst[[currMeasFac[i]]])
#   tmp<-data.frame(obsID=predR$slab, pred=predR$pred, cilb=predR$ci.lb, ciub=predR$ci.ub, se=predR$se)
#   ind.Fac<-ddply(dat.tmp, ~obsID, summarize, Level=unique(ecosystCat))
#   tmp1<-merge(tmp, ind.Fac)
#   tmp2<-ddply(tmp1, ~Level, summarize,
#         pred = unique(pred),
#         cilb = unique(cilb),
#         ciub = unique(ciub),
#         se = unique(se),
#         k = length(obsID))
#   pred.list[[i]]<-tmp2
#   }
# names(posthoc.list)<-currMeasFac
# posthocR<-ldply(posthoc.list)
# posthocR
# 
# names(pred.list)<-currMeasFac
# predR<-ldply(pred.list)
# predR
# 
# #update effect size table for plotting
# colnames(predR)[1]<-'MeasFac'
# selectMeas<-unique(predR$MeasFac)
# MeasFacl<-rev(levels(factor(MEASCAT[MEASCAT %in% selectMeas], levels=MEASCAT[MEASCAT %in% selectMeas])))
# MeasNamesl<-rev(measTab[measTab$MEASCAT %in% selectMeas,'labels'])
# colnames(predR)[2]<-'CAT'
# CAT<-levels(dat.tmp$ecosystCat)
# predR$y<-NA
# i<-0
# for(i in 1:length(MeasFacl)){
#   predR[predR$CAT==CAT[1] & predR$MeasFac==MeasFacl[i],'y']<-i+0.3
#   predR[predR$CAT==CAT[2] & predR$MeasFac==MeasFacl[i],'y']<-i+0.1
#   predR[predR$CAT==CAT[3] & predR$MeasFac==MeasFacl[i],'y']<-i-0.1
#   predR[predR$CAT==CAT[4] & predR$MeasFac==MeasFacl[i],'y']<-i-0.3
# }
# predR
# 
# #assign post-hoc letters (order:f, s, g, w)
# #subset(posthocR, .id=='soiln')
# phTammonif<-c('a','b','b','b') 
# phTsoiln<-c('b','bc','a','ac') #check this one
# predR$posthocL<-c(phTammonif,phTsoiln)
# 
# #assign pretty names and symbols
# faclimits<-rev(c('Wetland','Grassland','Shrubland','Forest'))
# facShapes<-c(16,17,15,18)
# predR$annLabel<-paste(predR$posthocL, ' (',predR$k,')', sep='')
# 
# #ggplot
# eco.sp<-ggplot(data=predR,aes(x=pred,y=y, shape=CAT,label=annLabel)) + 
#   geom_point(aes(shape=CAT),size=3) +
#   geom_errorbarh(aes(xmin=cilb,xmax=ciub), height=0)+
#   geom_vline(xintercept=0,linetype="dashed") + mytheme +
#   xlab('Std. Mean Diff. (Inv-Ref)') + ylab('') +
#   scale_y_continuous(breaks=seq(1,length(MeasFacl)),
#                      labels=MeasNamesl) +
#   scale_shape_manual(name="Factor",
#                      labels=faclimits,
#                      values=facShapes)+
#   geom_text(aes(x=ciub, y=y, hjust=-0.3, vjust=0.5),size=2.2,show_guide = FALSE) +
#   scale_x_continuous(expand=c(0,0), limits=c(-1.1,2))
# eco.sp + theme(axis.text.y=element_text(angle=90, hjust=0.5, size=10))
# 
# newfilename<-"ecosystForest.png"
# png(paste(figuresPath,'modForests',newfilename, sep='/'), units='in', width = fig.width*1.5, height = fig.height*1.5, res=fig.res)
# eco.sp + theme(axis.text.y=element_text(angle=90, hjust=0.5, size=10))
# dev.off()

```

_________________________________________________________________
# 3. Test for correlation between CWM traits and effect size values
3A. Fit a random-effects model with 1 of the continuous trait variables as a fixed effect. Model syntax is res <- rma.mv(yi, vi, mods= ~Xmod, random=list(~1 | paperID, ~1 | obsID), data=dat1, subset=measCat==MEASCAT[i], slab=paste(paperID,obsID, sep=",")) where Xmod is one of the following 12 combinations:  
-> 1 of 3 plant communities: (1) InvSpInvArea_cwm, (2) NatArea_cwm, (3) CWMDiff_cwm  
-> 1 of 4 trait types: (1) percN, (2) litterpercN, (3) cn, (4) littercn  
where...
'InvSpInvArea_cwm' is the invasive species community weighted mean trait value;
'NatArea_cwm' is the reference area plant community weighted mean trait value;
'CWMDiff_cwm' is the dissimilarity between the Invaded - Reference area plant community weighted mean trait value;
'percN' is leaf %N;
'litterpercN' is litter %N;
'cn' is leaf C:N;
'littercn' is litter C:N;
```{r fitreg, echo=TRUE, warning=FALSE, message=FALSE}
INVlist<-FitPlot(dat, k=1, qualColumn='InvSpInvArea_qualRank', colorN=5)
NATlist<-FitPlot(dat, k=2, qualColumn='NatArea_qualRank', colorN=5)
DIFFlist<-FitPlot(dat, k=3, qualColumn='CWMDiff_qualRank', colorN=10)
DIFF2list<-FitPlot(dat, k=4, qualColumn='CWMDiff2_qualRank', colorN=10)

#same thing, but instead color plots by invasive species' N-fixing status instead of trait quality
source('CODE/metaAnalysis/fxn_FitPlot_Nfix.R')
INVlist_Nfix<-FitPlot_Nfix(dat, k=1, nfixColumn='Nfix')
DIFFlist_Nfix<-FitPlot_Nfix(dat, k=3, nfixColumn='Nfix')
DIFF2list_Nfix<-FitPlot_Nfix(dat, k=4, nfixColumn='Nfix')


INVtab<-rbind(ldply(INVlist[['results']][['percN']]),
              ldply(INVlist[['results']][['litterpercN']]),
              ldply(INVlist[['results']][['cn']]),
              ldply(INVlist[['results']][['littercn']]))[,-1]
NATtab<-rbind(ldply(NATlist[['results']][['percN']]),
              ldply(NATlist[['results']][['litterpercN']]),
              ldply(NATlist[['results']][['cn']]),
              ldply(NATlist[['results']][['littercn']]))[,-1]
DIFFtab<-rbind(ldply(DIFFlist[['results']][['percN']]),
              ldply(DIFFlist[['results']][['litterpercN']]),
              ldply(DIFFlist[['results']][['cn']]),
              ldply(DIFFlist[['results']][['littercn']]))[,-1]
DIFF2tab<-rbind(ldply(DIFF2list[['results']][['percN']]),
              ldply(DIFF2list[['results']][['litterpercN']]),
              ldply(DIFF2list[['results']][['cn']]),
              ldply(DIFF2list[['results']][['littercn']]))[,-1]


newfilename<-'INVtab.txt'
write.table(INVtab, file=paste(figuresPath,'allRegressionTable',newfilename, sep='/'), sep='\t', row.names=FALSE)
newfilename<-'NATtab.txt'
write.table(NATtab, file=paste(figuresPath,'allRegressionTable',newfilename, sep='/'), sep='\t', row.names=FALSE)
newfilename<-'DIFFtab.txt'
write.table(DIFFtab, file=paste(figuresPath,'allRegressionTable',newfilename, sep='/'), sep='\t', row.names=FALSE)
newfilename<-'DIFF2tab.txt'
write.table(DIFF2tab, file=paste(figuresPath,'allRegressionTable',newfilename, sep='/'), sep='\t', row.names=FALSE)


INVtab[INVtab$pVal <0.1 & INVtab$measType %in% MEASCAT,]
INVlist[['qual']][['percN']][['soiln']] #trait quality has a marginal influence - studies with low quality trait data tend to have higher ESs
INVlist[['qual']][['percN']][['soilcn']]
INVlist[['qual']][['litterpercN']][['soilcn']]

NATtab[NATtab$pVal <0.1 & NATtab$measType %in% MEASCAT,]
NATlist[['qual']][['cn']][['nminz']]
NATlist[['qual']][['cn']][['soilcn']]

DIFFtab[DIFFtab$pVal <0.1 & DIFFtab$measType %in% MEASCAT,]
DIFFlist[['qual']][['litterpercN']][['nh']]
DIFFlist[['qual']][['litterpercN']][['toti']]
DIFFlist[['qual']][['cn']][['toti']] #trait quality has a marginal influence - studies with low quality trait data tend to have higher ESs
DIFFlist[['qual']][['cn']][['nminz']]
DIFFlist[['qual']][['cn']][['soilcn']]
DIFFlist[['qual']][['littercn']][['nh']]
DIFFlist[['qual']][['littercn']][['toti']]
DIFFlist[['qual']][['littercn']][['nminz']]

DIFF2tab[DIFF2tab$pVal <0.1 & DIFF2tab$measType %in% MEASCAT,]
DIFF2list[['qual']][['percN']][['nh']] #trait quality has a marginal influence - studies with low quality trait data tend to have higher ESs
DIFF2list[['qual']][['litterpercN']][['soilcn']]
DIFF2list[['qual']][['cn']][['nh']] # trait quality has a influence - studies with low quality trait data tend to have higher ESs
DIFF2list[['qual']][['cn']][['nminz']]
DIFF2list[['qual']][['cn']][['soilcn']]
DIFF2list[['qual']][['littercn']][['nh']]
DIFF2list[['qual']][['littercn']][['toti']]


```
3B. Plot some/all of the regressions
3Bi. Loop through all plots: 1 page per trait type x community with 9 ES panels
```{r plotregAll, echo=TRUE, warning=FALSE, message=FALSE}
#plot notes:
#predicted values calculated based on only the fixed effects
#ci = 95% confidence interval on the fixed effect coefficients

#add panel labels
INVlist1<-AddPanelTitles(INVlist)
NATlist1<-AddPanelTitles(NATlist)
DIFFlist1<-AddPanelTitles(DIFFlist)
DIFF2list1<-AddPanelTitles(DIFF2list)

#plot and save
PLANTlist<-list(INVlist1, NATlist1, DIFFlist1, DIFF2list1)
PLANTlabel<-c('INV','NAT','DIFF','DIFF2')
xlabel.PlantText<-c('Invasive species','Reference area','Invaded - Reference area','Inv Sp. - Reference area')
xlabel.TraitText<-c('Leaf %N','Litter %N','Leaf C:N','Litter C:N')
l<-0
for(l in 1:length(PLANTlist)){
  i<-0
  for (i in 1:length(TRAIT)){
    CURRTRAIT<-PLANTlist[[l]][[i]]
    xlabel<- textGrob(paste(xlabel.PlantText[l],xlabel.TraitText[i], sep=", "), x=0.5,
                      y=unit(1,'lines'))
    
    #open image file connection
    newfilename<-paste(paste(PLANTlabel[l],TRAIT[i], sep="_"),'.pdf',sep="")
    pdf(paste(figuresPath,'allRegressionPlots',newfilename, sep='/'), 
        width = fig.width*3.5, height = fig.height*3.5)

    grid.arrange(
      textGrob('Std. Mean Diff. (Inv-Ref)', y=0.5,x=unit(1,'lines'), rot=90), # ylabel topleft
      do.call(arrangeGrob,  CURRTRAIT),
      textGrob(" "), #bottom left
      xlabel, #bottom right
      widths = unit.c(unit(2.5, "lines"), unit(1, "npc") - unit(2.5, "lines")), 
      heights = unit.c(unit(1, "npc") - unit(2.5, "lines"), unit(2.5, "lines")), 
      nrow=2, ncol=2
    )
    
    dev.off()
    
  }
}

```

a. Invasive sp. traits
```{r plotreg_inv, echo=TRUE, warning=FALSE, message=FALSE}
newfilename<-"INVreg.pdf"
pdf(paste(figuresPath,newfilename, sep='/'), width = fig.width, height = fig.height)
INVlist[['figures']][['percN']][['soiln']] + 
    xlab('Invasive sp. Leaf %N') + ylab('Soil N effect size')
dev.off()

#showing N-fixing status
newfilename<-"INVreg_Nfix.pdf"
pdf(paste(figuresPath,newfilename, sep='/'), width = fig.width*1.75, height = fig.height)
INVlist_Nfix[['figures']][['percN']][['soiln']] + 
    xlab('Invasive sp. Leaf %N') + ylab('Soil N effect size')
dev.off()
#small points have large variance
#large points have small variance

```

b. Reference traits
```{r plotreg_ref, echo=TRUE, warning=FALSE, message=FALSE}
# newfilename<-"NATreg.png"
# png(paste(figuresPath,newfilename, sep='/'), units='in', width = fig.width*1.8, height = fig.height*.88, res=fig.res)
# grid.arrange(
#   NATlist[['figures']][['cn']][['nminz']] + 
#     xlab('Ref. CWM leaf C:N') + ylab('Mineralization effect size') + ggtitle('a'),
#   NATlist[['figures']][['cn']][['soilcn']] + 
#     xlab('Ref. CWM leaf C:N') + ylab('Soil C:N effect size') + ggtitle('b'),
#   ncol=2
# )
# dev.off()
#small points have large variance
#large points have small variance

```

c. Trait dissimilarity - invaded and reference areas
```{r plotreg_diff, echo=TRUE, warning=FALSE, message=FALSE}
newfilename<-"DIFFreg.pdf"
pdf(paste(figuresPath,newfilename, sep='/'), width = fig.width*2, height = fig.height*2)
grid.arrange(

  DIFFlist[['figures']][['litterpercN']][['toti']] + 
    xlab('Inv.-Ref. CWM litter %N') + ylab('Total inorg. N effect size') + ggtitle('a'),

  DIFFlist[['figures']][['littercn']][['toti']] + 
    xlab('Inv.-Ref. CWM litter C:N') + ylab('Total inorg. N effect size') + ggtitle('b'),

  DIFFlist[['figures']][['cn']][['nminz']] + 
    xlab('Inv.-Ref. CWM leaf C:N') + ylab('Mineralization effect size') + ggtitle('c'),
  
  DIFFlist[['figures']][['cn']][['soilcn']] + 
    xlab('Inv.-Ref. CWM leaf C:N') + ylab('Soil C:N effect size') + ggtitle('d'),
  ncol=2
)
dev.off()


#colors are invaders' N-fixing status
newfilename<-"DIFFreg_Nfix.pdf"
pdf(paste(figuresPath,newfilename, sep='/'), width = fig.width*2, height = fig.height*2)
grid.arrange(

  DIFFlist_Nfix[['figures']][['litterpercN']][['toti']] + 
    xlab('Inv.-Ref. CWM litter %N') + ylab('Total inorg. N effect size') + 
    ggtitle('a') + guides(color=FALSE),

  DIFFlist_Nfix[['figures']][['littercn']][['toti']] + 
    xlab('Inv.-Ref. CWM litter C:N') + ylab('Total inorg. N effect size') + 
    ggtitle('b')+ guides(color=FALSE),

  DIFFlist_Nfix[['figures']][['cn']][['nminz']] + 
    xlab('Inv.-Ref. CWM leaf C:N') + ylab('Mineralization effect size') + 
    ggtitle('c')+ guides(color=FALSE),
  
  DIFFlist_Nfix[['figures']][['cn']][['soilcn']] + 
    xlab('Inv.-Ref. CWM leaf C:N') + ylab('Soil C:N effect size') + 
    ggtitle('d')+ guides(color=FALSE),
  ncol=2
)
dev.off()

#small points have large variance
#large points have small variance
```

d. Trait dissimilarity - comparison of plots
```{r plotreg_diff2, echo=TRUE, warning=FALSE, message=FALSE}
newfilename<-"DIFF2reg_compare.pdf"
pdf(paste(figuresPath,newfilename, sep='/'), width = fig.width*2, height = fig.height*4)

grid.arrange(
    DIFFlist[['figures']][['litterpercN']][['toti']] + 
      xlab('Inv.-Ref. CWM litter %N') + ylab('Total inorg. N effect size') + 
      ggtitle('a'),
    DIFF2list[['figures']][['litterpercN']][['toti']] +
      xlab('Inv. Sp. - Ref litter %N') + ylab('Total inorg. N effect size') + 
      ggtitle('b'),
    
    DIFFlist[['figures']][['littercn']][['toti']] + 
      xlab('Inv.-Ref. CWM litter C : N') + ylab('Total inorg. N effect size') + 
      ggtitle('c'),
    DIFF2list[['figures']][['littercn']][['toti']] + 
      xlab('Inv. Sp. - Ref litter C : N') + ylab('Total inorg. N effect size') + 
      ggtitle('d'),
    
    DIFFlist[['figures']][['cn']][['nminz']] + 
      xlab('Inv.-Ref. CWM leaf C : N') + ylab('Mineralization effect size') + 
      ggtitle('e'),
    DIFF2list[['figures']][['cn']][['nminz']] + 
      xlab('Inv. Sp. - Ref leaf C : N') + ylab('Mineralization effect size') + 
      ggtitle('f'),
    
    DIFFlist[['figures']][['cn']][['soilcn']] + 
      xlab('Inv.-Ref. CWM leaf C : N') + ylab('Soil C : N effect size') + 
      ggtitle('g'),
    DIFF2list[['figures']][['cn']][['soilcn']] + 
      xlab('Inv. Sp. - Ref leaf C : N') + ylab('Soil C : N effect size') + 
      ggtitle('h'),
    ncol=2
    
    )

dev.off()


#showing which are N-fixers
newfilename<-"DIFF2reg_compare_Nfix.pdf"
pdf(paste(figuresPath,newfilename, sep='/'), width = fig.width*2, height = fig.height*4)
grid.arrange(
    DIFFlist_Nfix[['figures']][['litterpercN']][['toti']] + 
      xlab('Inv.-Ref. CWM litter %N') + ylab('Total inorg. N effect size') + 
      ggtitle('a')+ guides(color=FALSE),
    DIFF2list_Nfix[['figures']][['litterpercN']][['toti']] +
      xlab('Inv. Sp. - Ref litter %N') + ylab('Total inorg. N effect size') + 
      ggtitle('b')+ guides(color=FALSE),
    
    DIFFlist_Nfix[['figures']][['littercn']][['toti']] + 
      xlab('Inv.-Ref. CWM litter C : N') + ylab('Total inorg. N effect size') + 
      ggtitle('c')+ guides(color=FALSE),
    DIFF2list_Nfix[['figures']][['littercn']][['toti']] + 
      xlab('Inv. Sp. - Ref litter C : N') + ylab('Total inorg. N effect size') + 
      ggtitle('d')+ guides(color=FALSE),
    
    DIFFlist_Nfix[['figures']][['cn']][['nminz']] + 
      xlab('Inv.-Ref. CWM leaf C : N') + ylab('Mineralization effect size') + 
      ggtitle('e')+ guides(color=FALSE),
    DIFF2list_Nfix[['figures']][['cn']][['nminz']] + 
      xlab('Inv. Sp. - Ref leaf C : N') + ylab('Mineralization effect size') + 
      ggtitle('f')+ guides(color=FALSE),
    
    DIFFlist_Nfix[['figures']][['cn']][['soilcn']] + 
      xlab('Inv.-Ref. CWM leaf C : N') + ylab('Soil C : N effect size') + 
      ggtitle('g')+ guides(color=FALSE),
    DIFF2list_Nfix[['figures']][['cn']][['soilcn']] + 
      xlab('Inv. Sp. - Ref leaf C : N') + ylab('Soil C : N effect size') + 
      ggtitle('h')+ guides(color=FALSE),
    ncol=2
    
    )
dev.off()

#small points have large variance
#large points have small variance

#DIFF2list_Nfix[['figures']][['cn']][['soilcn']] 


```