compare sd of FA and matlab fit

rcode/compareFA_sd_fcs.R

+# compare standard deviation of different measurments with fits std and FluctuationAnalyzer estimated std
+# Author: Antonio Politi
+
+library('ggplot2')
+
+# import data ----
+setwd('C:\\Users\\toni\\Dropbox\\NPCMaturation\\matlabcode\\FCS\\example_data\\Alexa')
+fitfiles <- list.files(pattern = '*.1c_opt.cof')
+corfiles <- list.files(pattern = '*.1c_opt.cor')
+fitall <- list()
+for (i  in seq_along(fitfiles)) {
+  cof <- read.csv(fitfiles[i], sep = '\t')
+  cof <- cof[, c(1,2,3,4,5)]
+  cof <- na.omit(cof)
+
+  colnames(cof) <- c('tau', 'Ch1', 'fitCh1', 'Ch2', 'fitCh2')
+  cor <- read.csv(corfiles[i], sep = '\t')
+  colnames(cor) <- c('tau', 'Ch1', 'sdCh1', 'Ch2', 'sdCh2')
+  idx <- match(cof$tau, cor$tau)
+  cof$sdfitCh1 <- sqrt((cof$fitCh1 - cof$Ch1)^2)
+  cof$sdfitCh2 <- sqrt((cof$fitCh2 - cof$Ch2)^2)
+
+  cof <- cbind(cof, cor[idx, c('sdCh1',  'sdCh2')])
+  cof$id <- i
+  fitall <- rbind(fitall, cof)
+}
+
+# plot data -----
+msd <- aggregate(cbind(Ch1, sdCh1, sdfitCh1) ~ tau, data = fitall, function(fitall) c(mean = mean(fitall), sd = sd(fitall)))
+
+ggplot(data = msd ) +
+  geom_point(aes(x= tau, y = Ch1[,'sd'], color = 'sd (avg. 6 rep.)')) +
+  geom_point(aes(x= tau, y = sdfitCh1[, 'mean'], color = 'sqrt((G_exp - G_fit)^2)')) +
+  geom_point(aes(x= tau, y = sdCh1[, 'mean'], color = 'sd_FA')) +
+  geom_point(aes(x= tau, y = sdCh1[, 'mean']/sqrt(20), color = 'sd_FA/sqrt(20)')) +
+  scale_x_continuous(trans = 'log') + ylab('Estimated standard deviation') + guides(color=guide_legend(title=NULL))