PCAs and reweights of the values for each species is finally done and in a format that can be easily used to run the evol.rate.mcmc()

Script to impute data:

#impute missing values by taking mean of all other values of of other species ofthat character 

i<-1 #set the index

species.removed<-c()

#sift through all babbler species

while (i <= length(babblers$UniqueID_1)){

  #missing wing lengths; if the Wing Length is NA for that value

  if(is.na(babblers$Wing.Length[i]) == TRUE){

    #select the wing lengths of all birds which match this species with missing wing length

    wing.species.vector<-babblers$Wing.Length[which(babblers$Species[i] == babblers$Species)]

    #make a new vector without the missing value

    wing.species.filler<-wing.species.vector[which(is.na(wing.species.vector) == FALSE)]

    #calculate the mean

    wing.impute<-mean(wing.species.filler)

    #fill in missing value with the mean for that bird

    babblers$Wing.Length[i]<-wing.impute

  }

  

  #repeat the above method for all characters

  if(is.na(babblers$Tail.Length[i]) == TRUE){

    tail.species.vector<-babblers$Tail.Length[which(babblers$Species[i] == babblers$Species)]

    tail.species.filler<-tail.species.vector[which(is.na(tail.species.vector) == FALSE)]

    tail.impute<-mean(tail.species.filler)

    babblers$Tail.Length[i]<-tail.impute

  }

  

  

  

  if(is.na(babblers$Bill.Length[i]) == TRUE){

    BL.species.vector<-babblers$Bill.Length[which(babblers$Species[i] == babblers$Species)]

    BL.species.filler<-BL.species.vector[which(is.na(BL.species.vector) == FALSE)]

    BL.impute<-mean(BL.species.filler)

    babblers$Bill.Length[i]<-BL.impute

  }

  

  if(is.na(babblers$Bill.Width[i]) == TRUE){

    BW.species.vector<-babblers$Bill.Width[which(babblers$Species[i] == babblers$Species)]

    BW.species.filler<-BW.species.vector[which(is.na(BW.species.vector) == FALSE)]

    BW.impute<-mean(BW.species.filler)

    babblers$Bill.Width[i]<-BW.impute

  }

  

  if(is.na(babblers$Bill.Depth[i]) == TRUE){

    BD.species.vector<-babblers$Bill.Depth[which(babblers$Species[i] == babblers$Species)]

    BD.species.filler<-BD.species.vector[which(is.na(BD.species.vector) == FALSE)]

    BD.impute<-mean(BD.species.filler)

    babblers$Bill.Depth[i]<-BD.impute

  }

  

  if(is.na(babblers$Tarsus.Length[i]) == TRUE){

    TL.species.vector<-babblers$Tarsus.Length[which(babblers$Species[i] == babblers$Species)]

    TL.species.filler<-TL.species.vector[which(is.na(TL.species.vector) == FALSE)]

    TL.impute<-mean(BD.species.filler)

    babblers$Tarsus.Length[i]<-TL.impute

  }

  

  if(is.na(babblers$Hallux[i]) == TRUE){

    hallux.species.vector<-babblers$Hallux[which(babblers$Species[i] == babblers$Species)]

    hallux.species.filler<-hallux.species.vector[which(is.na(hallux.species.vector) == FALSE)]

    hallux.impute<-mean(hallux.species.filler)

    babblers$Hallux[i]<-hallux.impute

  }

  i<-i+1

}

Script to scale by geometric mean:

#make empty columns for geometric mean scale

babblers$gm.wing<-0

babblers$gm.tail<-0

babblers$gm.BL<-0

babblers$gm.BW<-0

babblers$gm.BD<-0

babblers$gm.tarsus<-0

babblers$gm.hallux<-0

#Size-adjust the raw data by dividing each individual by the geometric mean 

j<-1

could.not.scale<-c()

while(j<=length(babblers$UniqueID_1)){

  #grab all the data of that species and put in vector

  temp<-c(babblers$Wing.Length[j], babblers$Tail.Length[j], babblers$Bill.Length[j],

          babblers$Bill.Width[j], babblers$Bill.Depth[j], babblers$Tarsus.Length[j], 

          babblers$Hallux[j])

  temp<-na.omit(temp)

  #calculate the geometric mean of meausrements for that species

  geo.mean<-exp(mean(log(temp)))

  

  if(is.nan(geo.mean)==FALSE){

  #divide all values for that species by the geometric mean

  geo.wing<-(babblers$Wing.Length[j])/geo.mean

  babblers$gm.wing[j]<-geo.wing

  

  geo.tail<-babblers$Tail.Length[j]/geo.mean

  babblers$gm.tail[j]<-geo.tail

  

  geo.BL<-babblers$Bill.Length[j]/geo.mean

  babblers$gm.BL[j]<-geo.BL

  

  geo.BW<-babblers$Bill.Width[j]/geo.mean

  babblers$gm.BW[j]<-geo.BW

  

  geo.BD<-babblers$Bill.Depth[j]/geo.mean

  babblers$gm.BD[j]<-geo.BD

  

  geo.tarsus<-babblers$Tarsus.Length[j]/geo.mean

  babblers$gm.tarsus[j]<-geo.tarsus

  

  geo.hallux<-babblers$Hallux[j]/geo.mean

  babblers$gm.hallux[j]<-geo.hallux

 }

 else{

    append(could.not.scale, babblers$UniqueID_1[j])

  }

  #reset the temp

  temp<-c()

  

  #increment to next species

  j<-j+1

}

cat("The following species could not be scaled:", could.not.scale, "\n")

Script to do PCA with geometric mean values and put rescaled scores into new data frame:

#PCA for all measurements

pca.result <- prcomp(~babblers$gm.wing + babblers$gm.tail + babblers$gm.BL + babblers$gm.BW + babblers$gm.BD + 

       babblers$gm.tarsus + babblers$gm.hallux, data = babblers, retx = T)

#standard deviation of PCA

sd <- pca.result$sdev

#make vector of weights

loadings <- pca.result$rotation

#value of the rotated data (scores)

scores <- pca.result$x

#make a new data frame

new.babblers<-as.data.frame(babblers)

#make empty columns in the data frame

new.babblers$pca1<-0

new.babblers$pca2<-0

new.babblers$pca3<-0

new.babblers$pca4<-0

new.babblers$pca5<-0

new.babblers$pca6<-0

new.babblers$pca7<-0

#put PCA Scores into the data frame

new.babblers$pca1<-scores[,1]

new.babblers$pca2<-scores[,2]

new.babblers$pca3<-scores[,3]

new.babblers$pca4<-scores[,4]

new.babblers$pca5<-scores[,5]

new.babblers$pca6<-scores[,6]

new.babblers$pca7<-scores[,7]

new.babblers$pca# are the traits that will be used in the Bayesian analysis.
This site was really useful for understanding PCA:
http://strata.uga.edu/software/pdf/pcaTutorial.pdf