diff --git a/.gitignore b/.gitignore index 8a356f0..4e5d851 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ .Rhistory .DS_Store .Rapp.history +Aim1/.RData +Aim1/phylo/.RData diff --git a/Aim1/Rplot.png b/Aim1/Rplot.png new file mode 100644 index 0000000..6f9e13e Binary files /dev/null and b/Aim1/Rplot.png differ diff --git a/Aim1/code/test_weibull.R b/Aim1/code/test_weibull.R index 0ce55a6..be904b9 100644 --- a/Aim1/code/test_weibull.R +++ b/Aim1/code/test_weibull.R @@ -34,7 +34,8 @@ for(i in 1:length(strains)){ if (repObs["logabund"][[1]][2] - repObs["logabund"][[1]][1] > 1){ repObs <- repObs[-c(1), ] } - repObs["prop"] <- repObs$logabund / repObs$logabund[1] + #repObs["prop"] <- repObs$logabund / repObs$logabund[1] + repObs["prop"] <- log(repObs$Abund / repObs$Abund[1]) #KBS0703.1$prop <- KBS0703.1$logabund/KBS0703.1$logabund[1] # Initial parameters #A = 200 # Initial death (larger = slower) @@ -56,11 +57,11 @@ for(i in 1:length(strains)){ new.start[names(start) %in% names(mod.start)]<-mod.start pscale<-as.numeric(new.start) names(pscale)<-names(new.start) - #fit <- mle2(minuslogl=logabund ~ dnorm(mean = c * (time / a)^(b-1) * exp(-1*(time/a)^b), sd = z), + #fit <- mle2(minuslogl=prop ~ dnorm(mean = exp( -1 * ((time / a)^ b)), sd = z), # start = new.start, data = repObs, # control=list(parscale=pscale, maxit=1000), - # method="Nelder-Mead", hessian = T) - fit <- mle2(minuslogl=prop ~ dnorm(mean = exp( -1 * ((time / a)^ b)), sd = z), + # method="Nelder-Mead", hessian = T) + fit <- mle2(minuslogl=prop ~ dnorm(mean = -1 * ((time / a)^ b), sd = z), start = new.start, data = repObs, control=list(parscale=pscale, maxit=1000), method="Nelder-Mead", hessian = T) @@ -95,19 +96,21 @@ for(i in 1:length(strains)){ ### *** Comment/Uncomment following code to make pdf figs*** ### title=paste(strains[i]," rep ",reps[j]) - plot(repObs$time,repObs$prop,main=title,ylim=c(0,1)) + plot(repObs$time,repObs$prop,main=title,ylim=c(min(repObs$prop),0)) predTime=seq(0,max(repObs$time)) print(strains[i]) print(reps[j]) #exp( -1 * ((time / a)^ b)) #coef(best.fit)[3] - lines(repObs$time, exp( -1 * ((repObs$time / coef(best.fit)[1] )^ coef(best.fit)[2])), + #lines(repObs$time, exp( -1 * ((repObs$time / coef(best.fit)[1] )^ coef(best.fit)[2])), + # lwd=4, lty=2, col = "red") + lines(repObs$time, (-1 * ((repObs$time / coef(best.fit)[1] )^ coef(best.fit)[2])), lwd=4, lty=2, col = "red") counter=counter+1 } } } - + dev.off() summ=summ[!is.na(summ[,1]),] #colnames(summ)=c('strain','rep','a','b','c','z','AIC', 'a.CI.2.5', 'a.CI.97.5', 'b.CI.2.5', 'b.CI.97.5', 'c.CI.2.5', 'c.CI.97.5', 'z.CI.2.5', 'z.CI.97.5') diff --git a/Aim1/data/weibull_log.R b/Aim1/data/weibull_log.R new file mode 100644 index 0000000..9a364ce --- /dev/null +++ b/Aim1/data/weibull_log.R @@ -0,0 +1,123 @@ +rm(list = ls()) +getwd() +setwd("~/GitHub/LTDE/") + +library('bbmle') + +## Load Data +obs <- read.csv("data/longtermdormancy_20170620_nocomments.csv", + header = TRUE, stringsAsFactors = FALSE) +## Adding 1 to deal with log(0) observations +obs$Abund <- as.numeric(obs$Colonies) * 10 ^ as.numeric(obs$Dilution) + 1 +strains <- sort(unique(obs$Strain)) +strains <- strains[table(obs$Strain)>10] +obs <- obs[obs$Strain%in%strains,] +summ <- matrix(NA,length(strains)*max(obs$Rep),8) + +pdf('output/decayFitsWeibull_log.pdf') # Uncomment to create pdf that will plot data and fits +counter <- 1 + +for(i in 1:length(strains)){ + strainObs=obs[obs$Strain==strains[i],] + + reps=unique(strainObs$Rep) + for(j in 1:length(reps)){ + + repObs=strainObs[strainObs$Rep==reps[j],] + # minimum of 10 data points + if(nrow(repObs)>10){ + start=repObs[1,1] + time=(as.numeric(strptime(repObs$Firstread_date,format="%d-%b-%y",tz="EST"))- + as.numeric(strptime(start,format="%d-%b-%y",tz="EST")))/(3600*24) + repObs["time"] <- time + 1 + repObs["logabund"] <- log10(repObs$Abund) + if (repObs["logabund"][[1]][2] - repObs["logabund"][[1]][1] > 1){ + repObs <- repObs[-c(1), ] + } + #repObs["prop"] <- repObs$logabund / repObs$logabund[1] + repObs["prop"] <- log(repObs$Abund / repObs$Abund[1]) + #KBS0703.1$prop <- KBS0703.1$logabund/KBS0703.1$logabund[1] + # Initial parameters + #A = 200 # Initial death (larger = slower) + #B = 1 # Bend (upper = 1 = first-order decay) + #C = round(max(repObs$logabund),1) # intercept + #Z = 6 # Error + grids<-list(a=c(1,10,50,100,200),b=c(0.1,0.5,1,1.1,1.5),z=c(0.1,1,10)) + #start<-list(a=NA,b=NA,c=round(max(repObs$logabund),1),z=NA) + start<-list(a=NA,b=NA,z=NA) + grid.starts<-as.matrix(expand.grid(grids)) + ncombos<-dim(grid.starts)[[1]] + # cycle through each combo + res.mat<-matrix(NA,nrow=ncombos,ncol=I(length(start)+1)) + res.mod<-list() + for(k in 1:dim(grid.starts)[[1]]){ + #some how need to match grid parameters to start lists. + mod.start<-as.list(grid.starts[k,]) + new.start<-start + new.start[names(start) %in% names(mod.start)]<-mod.start + pscale<-as.numeric(new.start) + names(pscale)<-names(new.start) + #fit <- mle2(minuslogl=prop ~ dnorm(mean = exp( -1 * ((time / a)^ b)), sd = z), + # start = new.start, data = repObs, + # control=list(parscale=pscale, maxit=1000), + # method="Nelder-Mead", hessian = T) + fit <- mle2(minuslogl=prop ~ dnorm(mean = -1 * ((time / a)^ b), sd = z), + start = new.start, data = repObs, + control=list(parscale=pscale, maxit=1000), + method="Nelder-Mead", hessian = T) + res.mat[k,]<-c(coef(fit),AIC(fit)) + res.mod[[k]]<-fit + } + colnames(res.mat)<-c(names(coef(fit)),"AIC") + best.fit<-res.mod[[which(res.mat[,'AIC']==min(res.mat[,'AIC']))[1]]] + summ[counter,1]=strains[i] + summ[counter,2]=reps[j] + #CIs <- confint( profile(best.fit)) + print(coef(best.fit)) + # a + summ[counter,3]=coef(best.fit)[1] + # b + summ[counter,4]=coef(best.fit)[2] + # c + summ[counter,5]=coef(best.fit)[3] + #summ[counter,5]= round(max(repObs$logabund),1) + # z + summ[counter,6]=AIC(best.fit) + #summ[counter,8]=CIs[1,1] + #summ[counter,9]=CIs[1,2] + #summ[counter,10]=CIs[2,1] + #summ[counter,11]=CIs[2,2] + #summ[counter,12]=CIs[3,1] + #summ[counter,13]=CIs[3,2] + #summ[counter,14]=CIs[4,1] + #summ[counter,15]=CIs[4,2] + summ[counter,7]=length(repObs$time) + ln_half <- log(0.5, base = exp(1)) *-1 + half_life <- (ln_half ^ (1 / coef(best.fit)[2])) * coef(best.fit)[1] + summ[counter,8]=half_life + + + ### *** Comment/Uncomment following code to make pdf figs*** ### + title=paste(strains[i]," rep ",reps[j]) + plot(repObs$time,repObs$prop,main=title,ylim=c(min(repObs$prop),0)) + predTime=seq(0,max(repObs$time)) + print(strains[i]) + print(reps[j]) + #exp( -1 * ((time / a)^ b)) + #coef(best.fit)[3] + #lines(repObs$time, exp( -1 * ((repObs$time / coef(best.fit)[1] )^ coef(best.fit)[2])), + # lwd=4, lty=2, col = "red") + lines(repObs$time, (-1 * ((repObs$time / coef(best.fit)[1] )^ coef(best.fit)[2])), + lwd=4, lty=2, col = "red") + counter=counter+1 + } + } +} + +dev.off() +summ=summ[!is.na(summ[,1]),] +#colnames(summ)=c('strain','rep','a','b','c','z','AIC', 'a.CI.2.5', 'a.CI.97.5', 'b.CI.2.5', 'b.CI.97.5', 'c.CI.2.5', 'c.CI.97.5', 'z.CI.2.5', 'z.CI.97.5') +colnames(summ)=c('strain','rep','a','b','z','AIC', 'N.obs', 'Half_life') + +write.csv(summ,"data/weibull_log_results.csv") + diff --git a/Aim1/data/weibull_log_results.csv b/Aim1/data/weibull_log_results.csv new file mode 100644 index 0000000..19e1293 --- /dev/null +++ b/Aim1/data/weibull_log_results.csv @@ -0,0 +1,101 @@ +"","strain","rep","a","b","z","AIC","N.obs" +"1","ATCC13985","1","57.5760113048932","0.67648262511964","0.897515049149791","179.029681892667","66" +"2","ATCC13985","2","50.8228410333533","0.644201373307315","0.908467873538774","180.631220734182","66" +"3","ATCC13985","3","3.17305345368234","0.358214889962649","1.28869850214531","193.330613847646","56" +"4","ATCC13985","4","8.05002131657765","0.416766253171727","0.936877799008328","157.612138836215","56" +"5","ATCC43928","1","5.08906729412192","0.423031576343636","1.29607036126235","227.536125949181","66" 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