I am dropping Actinodura ramsayi.
Thursday, June 6, 2013
| LY_0134 |
Actinodura | ramsayi | 8.3 | NA | 10.44 | 5.34 | 5.86 | 25.57 | NA | ||
| LY_0135 | Actinodura | ramsayi | 7.9 | NA | 10.86 | 6.24 | 5.64 | NA | NA |
I only have two specimens for Actinodura ramsayi but am missing the tail and hallux measurements for both so cannot impute the missing data. It really messes up the PCA and I will not be able to get trait data for this node if I do not fill these in. Should I take the mean of those samples from the entire Actinodura genus? Or drop the species?
Tuesday, June 4, 2013
When running PRGmatic v1.7, if you get an error:
"Can't exec "./cap3": No such file or directory at ./PRGmatic.pl line 116, <STDIN> line 8.
contigFile is Species_01_trimmed.fasta.cap.contigs
No such file or directory at ./PRGmatic.pl line 127, <STDIN> line 8."
Go to PRGmatic folder in home directory (or wherever it is), click >Dependent Software and you probably need to unzip "cap3.macosx.intel64.zip". Unzip this this folder and move all of the contents to the PRGmatic main directory.
"Can't exec "./cap3": No such file or directory at ./PRGmatic.pl line 116, <STDIN> line 8.
contigFile is Species_01_trimmed.fasta.cap.contigs
No such file or directory at ./PRGmatic.pl line 127, <STDIN> line 8."
Go to PRGmatic folder in home directory (or wherever it is), click >Dependent Software and you probably need to unzip "cap3.macosx.intel64.zip". Unzip this this folder and move all of the contents to the PRGmatic main directory.
Thursday, May 30, 2013
Implemented code from Revell, et al. (2012) in supplemental material
http://onlinelibrary.wiley.com/store/10.1111/j.1558-5646.2011.01435.x/asset/supinfo/EVO_1435_sm_suppmat.pdf?v=1&s=cb7aa4c4e828d51fd4fe1519178c9c31fd956dc6
No problems compiling the code but had to spend awhile tweaking my data set to get the names of the character data species to match the names of the nodes.
Started playing around with data to see if I could get something to orient myself. Picked a random tree from the posterior distribution of trees from Reddy/Moyle (2012) paper. Sampled randomly from my data for Wing Length and after getting all of my names to match up, I could run:
result<-evol.rate.mcmc(test.tree,x)
min_split<-minSplit(tree,result$mcmc[21:101,c("node","bp")])
mcmc<-posterior.evolrate(tree,min_split,result$mcmc[21:101,],result$tips[201:1001])
Error in if (where == 0 || where == "root") where <- ROOTx :
missing value where TRUE/FALSE needed
I ran into an error at this point and I think it is a problem that could be patched with tweaking of of the split.tree() function obtained from Revell's blog (see comment by Jessica):
http://blog.phytools.org/2011/04/updates-to-evolratemcmc-new-analysis-of.html
Seemed to fix the problem. Now onto formatting my real data set...
http://onlinelibrary.wiley.com/store/10.1111/j.1558-5646.2011.01435.x/asset/supinfo/EVO_1435_sm_suppmat.pdf?v=1&s=cb7aa4c4e828d51fd4fe1519178c9c31fd956dc6
No problems compiling the code but had to spend awhile tweaking my data set to get the names of the character data species to match the names of the nodes.
Started playing around with data to see if I could get something to orient myself. Picked a random tree from the posterior distribution of trees from Reddy/Moyle (2012) paper. Sampled randomly from my data for Wing Length and after getting all of my names to match up, I could run:
result<-evol.rate.mcmc(test.tree,x)
min_split<-minSplit(tree,result$mcmc[21:101,c("node","bp")])
mcmc<-posterior.evolrate(tree,min_split,result$mcmc[21:101,],result$tips[201:1001])
Error in if (where == 0 || where == "root") where <- ROOTx :
missing value where TRUE/FALSE needed
I ran into an error at this point and I think it is a problem that could be patched with tweaking of of the split.tree() function obtained from Revell's blog (see comment by Jessica):
http://blog.phytools.org/2011/04/updates-to-evolratemcmc-new-analysis-of.html
Seemed to fix the problem. Now onto formatting my real data set...
Thursday, May 23, 2013
| PCR#: | LY0002 | 5/22/13 | |
| 25 μL reaction | |||
| # Tubes: | 13 | ||
| MasterMix | Single Tube (μL) | Total in MM (μL) | |
| EconoTaq MM | 12.5 | 162.5 | |
| H20 | 2.5 | 32.5 | |
| FW | 3 | 39 | |
| RV | 3 | 39 | |
| DNA | 4 | X | |
| TOTAL: | 25 | ||
| FW Primer | THYF | ||
| RV Primer | THYR | ||
| Tube# | Sample | ||
| 1 | 223167 | ||
| 2 | 582151 | ||
| 3 | 20568 | ||
| 4 | 588022 | ||
| 5 | 104610 | ||
| 6 | 110278 | ||
| 7 | 108297 | ||
| 8 | 15575 | ||
| 9 | 588479 | hardly any DNA--need to extract more | |
| 10 | 113765 | hardly any DNA--need to extract more | |
| 11 | control | ||
| Program: | Omar1 | ||
| Annealing Temp: | 50 | longer annealing time (:45) | |
| PCR#: | LY0003 | 5/22/13 | |
| 25 μL reaction | |||
| # Tubes: | 13 | ||
| MasterMix | Single Tube (μL) | Total in MM (μL) | |
| EconoTaq MM | 12.5 | 162.5 | |
| H20 | 2.5 | 32.5 | |
| FW | 3 | 39 | |
| RV | 3 | 39 | |
| DNA | 4 | X | |
| TOTAL: | 25 | ||
| FW Primer | STAT5A F | ||
| RV Primer | STAT5A R | ||
| Tube# | Sample | ||
| 1 | 223167 | ||
| 2 | 582151 | ||
| 3 | 20568 | ||
| 4 | 588022 | need to extract more DNA | |
| 5 | 104610 | need to extract more DNA | |
| 6 | 110278 | ||
| 7 | 108297 | ||
| 8 | 15575 | ||
| 9 | 588479 | hardly any DNA--need to extract more | |
| 10 | 113765 | hardly any DNA--need to extract more | |
| 11 | control | ||
| Program: | touchdowntchup | ||
| Annealing Temp: | 54 | ||
Tuesday, May 21, 2013
Project for Evolution 2013 meeting: predict if diversification of morphology = diversification of species.
Collected over 600 measurements from AMNH of wing length, tail length, bill length, bill width, bill depth, tarsus length, and hallux. I still need to combine the data collected from the museums in Vietnam.
First step is to impute missing data by taking the mean of that character from other measurements of the same species.
The following is the R script I wrote to do this:
#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
}
#if there are any values that canont be computed, remove them from the data set
if(is.nan(babblers$Wing.Length[i]) == TRUE){
babblers<-babblers[-i,]
append(species.removed, babblers$UniqueID_1[i])
}
i<-i+1
}
cat("The following species were removed from the analysis:", species.removed, "\n")
I have then scaled each point to take size into account by dividing each by the geometric mean of all characters for each species. The following is the R script I wrote in order to do that:
#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])
#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$Wing.Length[j])/geo.mean
babblers$Wing.Length[j]<-geo.wing
geo.tail<-babblers$Tail.Length[j]/geo.mean
babblers$Tail.Length[j]<-geo.tail
geo.BL<-babblers$Bill.Length[j]/geo.mean
babblers$Bill.Length[j]<-geo.BL
geo.BW<-babblers$Bill.Width[j]/geo.mean
babblers$Bill.Width[j]<-geo.BW
geo.BD<-babblers$Bill.Depth[j]/geo.mean
babblers$Bill.Depth[j]<-geo.BD
geo.tarsus<-babblers$Tarsus.Length[j]/geo.mean
babblers$Tarsus.Length[j]<-geo.tarsus
geo.hallux<-babblers$Hallux[j]/geo.mean
babblers$Hallux[j]<-geo.hallux
}
else{
append(could.not.scale, babblers$UniqueID_1[j])
}
#reset the temp
temp<-c()
#increment to next species
j<-j+1
}
cat("The following species could not be scaled:", could.not.scale, "\n")
Now I can run a PCA on the 7 measurements to determine what holds the most variance:
Collected over 600 measurements from AMNH of wing length, tail length, bill length, bill width, bill depth, tarsus length, and hallux. I still need to combine the data collected from the museums in Vietnam.
First step is to impute missing data by taking the mean of that character from other measurements of the same species.
The following is the R script I wrote to do this:
#impute missing values by taking mean of all other values of of other species ofthat character
i<-1 #set the index
species.removed<-c()
#sift through all babbler species
while (i < length(babblers$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$Hallux[i]<-hallux.impute
}
#if there are any values that canont be computed, remove them from the data set
if(is.nan(babblers$Wing.Length[i]) == TRUE){
babblers<-babblers[-i,]
append(species.removed, babblers$UniqueID_1[i])
}
i<-i+1
}
cat("The following species were removed from the analysis:", species.removed, "\n")
I have then scaled each point to take size into account by dividing each by the geometric mean of all characters for each species. The following is the R script I wrote in order to do that:
#Size-adjust the raw data by dividing each individual by the geometric mean
j<-1
could.not.scale<-c()
while(j<length(babblers$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$Hallux[j])
#calculate the geometric mean of meausrements for that species
geo.mean<-exp(mean(log(temp)))
if(is.nan(geo.mean)==FALSE){
#divide all values for that species by the geometric mean
geo.wing<-(babblers$Wing.Length[j])/geo.mean
babblers$Wing.Length[j]<-geo.wing
geo.tail<-babblers$Tail.Length[j]/geo.mean
babblers$Tail.Length[j]<-geo.tail
geo.BL<-babblers$Bill.Length[j]/geo.mean
babblers$Bill.Length[j]<-geo.BL
geo.BW<-babblers$Bill.Width[j]/geo.mean
babblers$Bill.Width[j]<-geo.BW
geo.BD<-babblers$Bill.Depth[j]/geo.mean
babblers$Bill.Depth[j]<-geo.BD
geo.tarsus<-babblers$Tarsus.Length[j]/geo.mean
babblers$Tarsus.Length[j]<-geo.tarsus
geo.hallux<-babblers$Hallux[j]/geo.mean
babblers$Hallux[j]<-geo.hallux
}
else{
append(could.not.scale, babblers$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")
Now I can run a PCA on the 7 measurements to determine what holds the most variance:
#PCA for all measurements
pca.result <- prcomp(~babblers$Wing.Length + babblers$Tail.Length + babblers$Bill.Length + babblers$Bill.Width + babblers$Bill.Depth + babblers$Tarsus.Length + babblers$Hallux, data = babblers, retx = T)
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