diff --git a/analysis/120518_07_2013-09-11-225029_d2r-wholeBrain_activeFraction.txt b/analysis/120518_07_2013-09-11-225029_d2r-wholeBrain_activeFraction.txt
deleted file mode 100644
index 668a00e..0000000
--- a/analysis/120518_07_2013-09-11-225029_d2r-wholeBrain_activeFraction.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-name actvFraction maxFraction minFraction meanFraction sdFraction meanActvFraction sdActvFraction actvFrames actvTimeFraction nonActvFrames nonActvTimeFraction
-cortex.L 0.94462 0.11391 0 0.011953 0.018076 0.021933 0.019512 1635 0.545 1365 0.455
-cortex.R 0.98936 0.1788 0 0.011325 0.019276 0.02026 0.021993 1677 0.559 1323 0.441
-V1.L 0.91092 0.49268 0 0.01166 0.048811 0.085525 0.10573 409 0.13633 2591 0.86367
-V1.R 1 0.49303 0 0.01563 0.049954 0.082406 0.087538 569 0.18967 2431 0.81033
-V2M.R 1 0.53362 0 0.010531 0.047479 0.10746 0.11235 294 0.098 2706 0.902
-V2M.L 0.99889 0.72727 0 0.015069 0.070498 0.15535 0.17184 291 0.097 2709 0.903
-V2L.R 0.99979 0.83261 0 0.0093884 0.051119 0.10279 0.13809 274 0.091333 2726 0.90867
-V2L.L 0.89476 0.47959 0 0.011748 0.048174 0.10396 0.10477 339 0.113 2661 0.887
-A1.L 0.94332 0.81434 0 0.0056521 0.038994 0.14745 0.13751 115 0.038333 2885 0.96167
-A1.R 0.89896 0.51003 0 0.0032766 0.029558 0.12287 0.13523 80 0.026667 2920 0.97333
-barrel.L 1 0.48431 0 0.014222 0.051387 0.10432 0.099942 409 0.13633 2591 0.86367
-barrel.R 1 0.48833 0 0.014424 0.050157 0.10278 0.094128 421 0.14033 2579 0.85967
-AS.L 1 0.61732 0 0.017578 0.063066 0.13626 0.12119 387 0.129 2613 0.871
-AS.R 1 0.72344 0 0.016043 0.064496 0.14073 0.13779 342 0.114 2658 0.886
-PPC.L 1 0.52809 0 0.013443 0.050858 0.11425 0.10241 353 0.11767 2647 0.88233
-PPC.R 0.99316 0.51777 0 0.0085395 0.043173 0.11698 0.11357 219 0.073 2781 0.927
-LS.L 1 0.73378 0 0.011153 0.061789 0.18904 0.17674 177 0.059 2823 0.941
-LS.R 1 0.88002 0 0.0092979 0.060153 0.19371 0.19978 144 0.048 2856 0.952
-FL.L 1 0.83896 0 0.013279 0.073959 0.21888 0.21303 182 0.060667 2818 0.93933
-FL.R 1 0.70978 0 0.013078 0.059922 0.16766 0.14208 234 0.078 2766 0.922
-HL.L 1 1 0 0.02413 0.10457 0.28726 0.23402 252 0.084 2748 0.916
-HL.R 1 0.96645 0 0.019074 0.098036 0.26614 0.26196 215 0.071667 2785 0.92833
-T.L 1 0.7411 0 0.017038 0.070106 0.15825 0.15284 323 0.10767 2677 0.89233
-T.R 1 0.47762 0 0.0091402 0.042031 0.10587 0.10127 259 0.086333 2741 0.91367
-RSA.L 0.99984 0.85963 0 0.021561 0.083406 0.18272 0.17196 354 0.118 2646 0.882
-RSA.R 1 0.52748 0 0.01318 0.053458 0.10572 0.11474 374 0.12467 2626 0.87533
-M1.L 0.99877 0.30664 0 0.012884 0.037771 0.071976 0.061002 537 0.179 2463 0.821
-M1.R 0.99979 0.38526 0 0.013534 0.039764 0.063539 0.065195 639 0.213 2361 0.787
-M2.L 0.90863 0.3212 0 0.0067742 0.026208 0.050934 0.054044 399 0.133 2601 0.867
-M2.R 0.99698 0.293 0 0.011429 0.035556 0.070262 0.060359 488 0.16267 2512 0.83733
\ No newline at end of file
diff --git a/analysis/quantif_20131105/.RData b/analysis/quantif_20131105/.RData
deleted file mode 100644
index 01c9cf4..0000000
Binary files a/analysis/quantif_20131105/.RData and /dev/null differ
diff --git a/analysis/quantif_20131105/.Rhistory b/analysis/quantif_20131105/.Rhistory
deleted file mode 100644
index c418161..0000000
--- a/analysis/quantif_20131105/.Rhistory
+++ /dev/null
@@ -1,2199 +0,0 @@
-m$yloca.norm <- round(m$y.loca/max(data$y.loca),digits=1)
-m$xloca.norm <- round(data$x.loca/max(data$x.loca),digits=1)#
-m$yloca.norm <- round(m$y.loca/max(data$y.loca),digits=1)
-m <- data#
-m$xloca.norm <- round(data$x.loca/max(data$x.loca),digits=1)#
-m$yloca.norm <- round(m$y.loca/max(data$y.loca),digits=1)
-m$xy <- interaction(m$xloca.norm,m$yloca.norm,drop=TRUE,sep=":")#
-#m$freq <- rep(1,length(data$z.loca))#
-ids <- c("xloca.norm","yloca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-ed(m)#
-m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.1)#
-y1 <- seq(0,1.0,by=0.1)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"),asp=1)
-m <- data#
-m$xloca.norm <- round(data$x.loca/max(data$x.loca),digits=1)#
-m$yloca.norm <- round(m$y.loca/max(data$y.loca),digits=1)#
-m$xy <- interaction(m$xloca.norm,m$yloca.norm,drop=TRUE,sep=":")#
-m$freq <- rep(1,length(data$z.loca))#
-ids <- c("xloca.norm","yloca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-ed(m)#
-m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.1)#
-y1 <- seq(0,1.0,by=0.1)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"),asp=1)
-m <- data#
-m$xloca.norm <- round(data$xloca/max(data$xloca),digits=1)#
-m$yloca.norm <- round(m$yloca/max(data$yloca),digits=1)#
-m$xy <- interaction(m$xloca.norm,m$yloca.norm,drop=TRUE,sep=":")#
-m$freq <- rep(1,length(data$z.loca))#
-ids <- c("xloca.norm","yloca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-#ed(m)#
-m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.1)#
-y1 <- seq(0,1.0,by=0.1)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"),asp=1)
-data$xloca
-data
-data$xloca
-data$yloca
-data$zloca
-colnames(data)
-rm(data)
-ls()
-rm(list=ls())
-ls()
-data <- read.delim("/Users/ackman/Desktop/120703_01_STATS-Centroids.txt",sep=" ")
-nrow(data)
-data$xloca
-data
-colnames(data)
-data$x.loca
-length(data$x.loca)
-length(data$y.loca)
-length(data$z.loca)
-data$x.loca/max(data$x.loca)
-length(data$x.loca)
-is.numeric(data$x.loca)
-max(data$x.loca)
-rm(data)
-data <- read.delim(pipe("pbpaste"))
-colnames(data)
-nrow(data)
-max(data$x.loca)
-data$x.loca
-length(data$x.loca)
-mean(data$x.loca)
-is.vector(data$x.loca)
-is.numeric(data$x.loca)
-quit()
-require(reshape)
-data <- read.delim(pipe("pbpaste"))
-data
-with(data,mean(x.loca))
-with(data,is.numeric(x.loca))
-help(is.numeric)
-with(data,is.double(x.loca))
-data <- read.delim(pipe("pbpaste"))
-with(data,max(x.loca))
-with(data,is.double(x.loca))
-with(data,length(x.loca))
-with(data,mean(x.loca))
-with(data,is.double(x.loca))
-with(data,lenght(x.loca))
-with(data,length(x.loca))
-data
-rm(data)
-data <- read.delim(pipe("pbpaste"),sep=" ")
-with(data,length(x.loca))
-with(data,max(x.loca))
-with(data,mean(x.loca))
-require(reshape)#
-#
-#-----------R code for normalized frequency contour plot-------------#
-quartz();#
-m <- data#
-m$x.loca.norm <- round(data$x.loca/max(data$x.loca),digits=1)#
-m$y.loca.norm <- round(m$y.loca/max(data$y.loca),digits=1)#
-m$xy <- interaction(m$x.loca.norm,m$y.loca.norm,drop=TRUE,sep=":")#
-m$freq <- rep(1,length(data$z.loca))#
-ids <- c("x.loca.norm","y.loca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-#ed(m)#
-m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.1)#
-y1 <- seq(0,1.0,by=0.1)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"),asp=1)
-m <- data#
-m$x.loca.norm <- round(data$x.loca/max(data$x.loca),digits=2)#
-m$y.loca.norm <- round(m$y.loca/max(data$y.loca),digits=2)#
-m$xy <- interaction(m$x.loca.norm,m$y.loca.norm,drop=TRUE,sep=":")#
-m$freq <- rep(1,length(data$z.loca))#
-ids <- c("x.loca.norm","y.loca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-#ed(m)#
-m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.1)#
-y1 <- seq(0,1.0,by=0.1)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"),asp=1)
-max(m$freq)
-max(m$x.loca)
-max(m$x.loca.norm)
-mean(m$x.loca)
-mean(m$x.loca.norm)
-colnames(d4)
-colnames(m)
-m$x.loca.norm
-m <- data#
-m$x.loca.norm <- round(data$x.loca/max(data$x.loca),digits=3)#
-m$y.loca.norm <- round(m$y.loca/max(data$y.loca),digits=3)#
-m$xy <- interaction(m$x.loca.norm,m$y.loca.norm,drop=TRUE,sep=":")#
-m$freq <- rep(1,length(data$z.loca))#
-ids <- c("x.loca.norm","y.loca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-#ed(m)#
-#m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.001)#
-y1 <- seq(0,1.0,by=0.001)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"),asp=1)
-matchidx
-x1
-m$x.loca.norm
-x1
-m$x.loca.norm
-m <- data#
-m$x.loca.norm <- round(data$x.loca/max(data$x.loca),digits=1)#
-m$y.loca.norm <- round(m$y.loca/max(data$y.loca),digits=1)#
-m$xy <- interaction(m$x.loca.norm,m$y.loca.norm,drop=TRUE,sep=":")#
-m$freq <- rep(1,length(data$z.loca))#
-ids <- c("x.loca.norm","y.loca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-#ed(m)#
-m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.1)#
-y1 <- seq(0,1.0,by=0.1)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"),asp=1)
-m <- data#
-m$x.loca.norm <- round(data$x.loca/max(data$x.loca),digits=1)#
-m$y.loca.norm <- round(m$y.loca/max(data$y.loca),digits=1)#
-m$xy <- interaction(m$x.loca.norm,m$y.loca.norm,drop=TRUE,sep=":")#
-m$freq <- rep(1,length(data$z.loca))#
-ids <- c("x.loca.norm","y.loca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-#ed(m)#
-#m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.1)#
-y1 <- seq(0,1.0,by=0.1)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"),asp=1)
-m <- data#
-m$x.loca.norm <- round(data$x.loca/max(data$x.loca),digits=1)#
-m$y.loca.norm <- round(m$y.loca/max(data$y.loca),digits=1)#
-m$xy <- interaction(m$x.loca.norm,m$y.loca.norm,drop=TRUE,sep=":")#
-m$freq <- rep(1,length(data$z.loca))#
-ids <- c("x.loca.norm","y.loca.norm","xy")#
-meas <- c("freq")#
-m <- as.data.frame(m)#
-d3 <- with(m,melt(m,id.var=ids,measure.var=meas))#
-d4 <- cast(d3,fun.aggregate=sum)#
-m <- d4#
-#ed(m)#
-#m$freq <- m$freq/max(m$freq)#
-#
-#make continous frame for a matrix for inputs to contour#
-x1 <- seq(0,1.0,by=0.1)#
-y1 <- seq(0,1.0,by=0.1)#
-df <- expand.grid(x=x1,y=y1)#
-df$xy <- interaction(df$x,df$y,drop=TRUE,sep=":")#
-matchidx <- match(df$xy,m$xy)#
-tmp <- m$freq[matchidx]#
-tmp[which(is.na(tmp))] <- 0#
-df$z <- tmp#
-tmp <- data.frame(x=df$x,y=df$y,z=df$z)#
-mat <- matrix(tmp$z,nrow=sqrt(nrow(tmp)))#
-#filled.contour(mat,color=terrain.colors)#
-filled.contour(mat,color=colorRampPalette(c("blue", "white", "red"), space = "rgb"))
-max(data$x.loca)
-max(data$y.loca)
-help(matrix)
-tmp <– matrix(data = rep(0,512*696), nrow = 512, ncol = 696)
-tmp <– matrix(nrow = 512, ncol = 696)
-rm(tmp)
-tmp <– matrix(nrow = 512, ncol = 696)
-tmp<–matrix(nrow = 512, ncol = 696)
-help(matrix)
-tmp<–matrix(data = 0,nrow = 512, ncol = 696)
-matrix(data = 0,nrow = 512, ncol = 696)
-mat <- matrix(data = 0,nrow = 512, ncol = 696)
-tmp
-tmp <- matrix(data = 0,nrow = 512, ncol = 696)
-rm(tmp)
-dim(mat)
-image(tmp)
-image(mat)
-rm(mat)
-rm(tmp)
-rm(mat)
-m <- data#
-m$x.loca.norm <- round(data$x.loca)#
-m$y.loca.norm <- round(data$y.loca)#
-tmp <– matrix(data = 0, nrow = 512, ncol = 696)#
-#
-for(i in length(m$x.loca.norm)) {#
-tmp[m$y.loca.norm(i),m$x.loca.norm(i)] <- tmp[m$y.loca.norm(i),m$x.loca.norm(i)] + 1#
-}
-tmp <– matrix(data = 0, nrow = 512, ncol = 696)
-matrix(data = 0, nrow = 512, ncol = 696)
-tmp = matrix(data = 0, nrow = 512, ncol = 696)
-for(i in length(m$x.loca.norm)) {#
-tmp[m$y.loca.norm(i),m$x.loca.norm(i)] <- tmp[m$y.loca.norm(i),m$x.loca.norm(i)] + 1#
-}
-for (i in 1:length(m$x.loca.norm)) {#
-tmp[m$y.loca.norm(i),m$x.loca.norm(i)] <- tmp[m$y.loca.norm(i),m$x.loca.norm(i)] + 1#
-}
-1:length(m$x.loca.norm))
-1:length(m$x.loca.norm)
-for (i in 1:length(m$x.loca.norm)) {#
-#tmp[m$y.loca.norm(i),m$x.loca.norm(i)] <- tmp[m$y.loca.norm(i),m$x.loca.norm(i)] + 1#
-tmp[m$y.loca.norm(i),m$x.loca.norm(i)] <- 1#
-}
-for (i in 1:length(m$x.loca.norm)) {#
-tmp[m$y.loca.norm[i],m$x.loca.norm[i]] <- tmp[m$y.loca.norm[i],m$x.loca.norm[i]] + 1#
-}
-image(tmp)
-help(image)
-heatmap(tmp)
-quit()
-NA <- 0.5
-NA <- 0.5
-numapp <- 0.5
-ls <- 200
-lambda <- 925
-n <- 1.33
-((2*pi*(numapp^2))/(lambda*n*ls))*(z^2)*exp(-2*z/ls)
-z <- 1760
-((2*pi*(numapp^2))/(lambda*n*ls))*(z^2)*exp(-2*z/ls)
-quit()
-help(igraph)
-require(igraph)
-library(help="igraph")
-help(walktrap.community)
-g <- erdos.renyi.game(10, 5/10) %du% erdos.renyi.game(9, 5/9)
-g <- add.edges(g, c(0, 11))
-g <- subgraph(g, subcomponent(g, 0))
-spinglass.community(g, spins=2)
-spinglass.community(g, vertex=0)
-g
-(19*18)/2
-40/171
-g <- graph.full(5) %du% graph.full(5) %du% graph.full(5)#
-g <- add.edges(g, c(0,5, 0,10, 5, 10))#
-wtc <- walktrap.community(g)#
-memb <- community.to.membership(g, wtc$merges, steps=12)#
-modularity(g, memb$membership)
-quit()
-demo()
-demo(image)
-demo(graphics)
-quartz()
-x <- rand(1,100)
-x <- randn(1,100)
-x <- rnorm(1,100)
-x
-help(rnorm)
-x <- rnorm(100)
-x
-y <- rnorm(100)
-plot(x,y)
-par <- opar
-plot(x,y)
-par <- opar
-x <- rnorm(100)
-y <- rnorm(100)
-plot(x,y)
-quit()
-help("Deprecated")
-help(find.package)
-quit()
-quit(0
-)
-quit()
-data <- read.delim("landdata_states.txt")
-head(data)
-tail(data)
-require(ggplot2)#
-require(reshape)
-require(reshape2)
-df <- subset(data,STATE=="CT"|STATE=="VT"|STATE=="NH"|STATE=="KY")
-p <- ggplot(df, aes(x=Date,y=Land.Value,group=STATE)) #
-p + geom_point(aes(color=STATE,shape=STATE)) + geom_line(aes(color=STATE)) + scale_shape_manual(value = c(15, 16)) + theme_bw() + opts(aspect.ratio=1) + scale_colour_brewer(palette="Set1") #+ opts(axis.text.x=theme_text(angle=-90, hjust=0))
-p <- ggplot(df, aes(x=Date,y=Land.Value,group=STATE)) #
-p + geom_point(aes(color=STATE,shape=STATE)) + geom_line(aes(color=STATE))
-is.factor(df$Date)
-is.date(df$Date)
-data <- read.delim("landdata_states.txt")
-is.factor(df$Date)
-df <- subset(data,STATE=="CT"|STATE=="VT"|STATE=="NH"|STATE=="KY")
-is.factor(df$Date)
-p <- ggplot(df, aes(x=Date,y=Land.Value.usd,group=STATE)) #
-p + geom_point(aes(color=STATE,shape=STATE)) + geom_line(aes(color=STATE)) + scale_shape_manual(value = c(15, 16)) + theme_bw()
-p <- ggplot(df, aes(x=Date,y=Land.Value.usd,group=STATE)) #
-p + geom_point(aes(color=STATE,shape=STATE)) + geom_line(aes(color=STATE))
-is.factor(df$Land.Value.usd)
-head(df)
-is.factor(df$Date)
-is.factor(df$Home.Value.usd)
-as.numeric(df$Home.Value.usd)
-as.vector(df$Home.Value.usd)
-help(as)
-as.integer(df$Home.Value.usd)
-data <- read.delim("landdata_states.txt")
-df <- subset(data,STATE=="CT"|STATE=="VT"|STATE=="NH"|STATE=="KY")
-is.factor(df$Date)
-is.factor(df$Home.Value.usd)
-as.integer(df$Home.Value.usd)
-is.integer(df$Home.Value.usd)
-is.numeric(df$Home.Value.usd)
-p <- ggplot(df, aes(x=Date,y=Land.Value.usd,group=STATE)) #
-p + geom_point(aes(color=STATE,shape=STATE)) + geom_line(aes(color=STATE))
-is.numeric(df$Land.Value.usd)
-is.factor(df$Land.Value.usd)
-tail(df)
-as.integer(df$Land.Value.usd)
-as.integer(data$Land.Value.usd)
-tail(data)
-levels(df$Land.Value.usd)
-df$Land.Value.usd
-levels(df$Land.Value.usd)
-df$Land.Value.usd
-tail(df)
-df$Land.Value.usd <- as.numeric(level(df$Land.Value.usd))
-df$Land.Value.usd <- as.numeric(levels(df$Land.Value.usd))
-tail(df)
-p <- ggplot(df, aes(x=Date,y=Land.Value.usd,group=STATE)) #
-p + geom_point(aes(color=STATE,shape=STATE)) + geom_line(aes(color=STATE))
-is.factor(df$Land.Value.usd)
-as.character(df$Land.Value.usd)
-as.numeric(as.character(df$Land.Value.usd))
-is.factor(as.numeric(as.character(df$Land.Value.usd)))
-is.vector(as.numeric(as.character(df$Land.Value.usd)))
-df <- subset(data,STATE=="CT"|STATE=="VT"|STATE=="NH"|STATE=="KY")#
-df$Land.Value.usd <- as.numeric(as.character(df$Land.Value.usd))
-p <- ggplot(df, aes(x=Date,y=Land.Value.usd,group=STATE)) #
-p + geom_point(aes(color=STATE,shape=STATE)) + geom_line(aes(color=STATE))
-data <- read.delim("landdata_states.txt") #first removed the " $ % , symbols from the dataset and changed headers
-df <- subset(data,STATE=="CT"|STATE=="VT"|STATE=="NH"|STATE=="KY")
-p <- ggplot(df, aes(x=Date,y=Land.Value.usd,group=STATE)) #
-p + geom_point(aes(color=STATE,shape=STATE)) + geom_line(aes(color=STATE))
-p <- ggplot(df, aes(x=Date,y=Land.Value.usd,group=STATE)) #
-p + geom_line(aes(color=STATE))
-p + geom_line(aes(color=STATE)) + theme_bw() + opts(aspect.ratio=1) + scale_colour_brewer(palette="Set1")
-ggsave(file="geomline_landvalue-time-STATE.pdf")
-p <- ggplot(df, aes(x=Date,y=Land.Share.frac*100,group=STATE)) #
-p + geom_line(aes(color=STATE)) + theme_bw() + opts(aspect.ratio=1) + scale_colour_brewer(palette="Set1")
-ggsave(file="geomline_landshare-time-STATE.pdf")
-summary(cars)
-plot(cars)
-library(devtools)
-require(googleVis)
-cars
-asis
-install.packages('googleVis')
-require(googleVis)
-demo(googleVis)
-fruits
-help(gvisMotionChart)
-M1
-Fruits
-Volcano
-volcano
-quit(_
-quit()
-actvFraction <- c(0.2838, 0.31114, 0.14775, 0.058811, 0.104, 0.079014, 0.015483)
-motorState <- c("quiet","quiet","quiet","active","active","active","active")
-df <- data.frame(actvFraction, motorState)
-df
-summary(df)
-help(summary)
-table(df)
-lm.df <- with(df, lm(actvFraction ~ motorState))
-summary(lm.df)
-sum(df$actvFraction)
-is.factor(df$motorState)
-help(sum)
-help(pylr)
-help.start(0
-help.start(0)
-help.start()
-require(plyr)
-dfx <- data.frame(#
- group = c(rep('A', 8), rep('B', 15), rep('C', 6)),#
- sex = sample(c("M", "F"), size = 29, replace = TRUE),#
- age = runif(n = 29, min = 18, max = 54)#
-)
-dfx
-ddply(dfx, .(group, sex), summarize,#
- mean = round(mean(age), 2),#
- sd = round(sd(age), 2))
-ddply(dfx, .(group, sex), summarize,#
- mean = round(mean(age), 2),#
- sd = round(sd(age), 2), len = length(age))
-help(summarize)
-ddply(dfx, (group, sex), summarize,#
- mean = round(mean(age), 2),#
- sd = round(sd(age), 2), len = length(age))
-ddply(dfx, .(group, sex), summarize,#
- mean = round(mean(age), 2),#
- sd = round(sd(age), 2))
-ddply(dfx, c("group", "sex"), summarize,#
- mean = round(mean(age), 2),#
- sd = round(sd(age), 2))
-ddply(dfx, c("group", "sex"), summarize,#
- mean = round(mean(age), 2),#
- sd = round(sd(age), 2), N = length(age))
-dfx
-sd(1)
-sd(c(1,2))
-help(sd)
-ddply(dfx, c("group", "sex"), summarize,#
- mean = round(mean(age), 2),#
- sd = round(sd(age), 2), N = length(age), se = sd/sqrt(N))
-ddply(dfx, c("group", "sex"), summarize,#
- mean = round(mean(age), 2),#
- sd = round(sd(age), 2), N = sum(!is.na(age)), se = sd/sqrt(N))
-help(summarize)
-ddply(df, c("motorState"), summarize,#
-sum = round(sum(actvFraction)),#
-mean = round(mean(actvFraction), 2),#
-sd = round(sd(actvFraction), 2), #
-N = sum(!is.na(age)), #
-se = sd/sqrt(N))
-ddply(df, c("motorState"), summarize,#
-sum = round(sum(actvFraction)),#
-mean = round(mean(actvFraction), 2),#
-sd = round(sd(actvFraction), 2), #
-N = sum(!is.na(actvFraction)), #
-se = sd/sqrt(N))
-ddply(df, c("motorState"), summarize,#
-sum = round(sum(actvFraction),2),#
-mean = round(mean(actvFraction), 2),#
-sd = round(sd(actvFraction), 2), #
-N = sum(!is.na(actvFraction)), #
-se = sd/sqrt(N))
-ddply(df, c("motorState"), summarize,#
-sum = round(sum(actvFraction),2),#
-mean = round(mean(actvFraction), 2),#
-sd = round(sd(actvFraction), 2), #
-N = length(actvFraction), #
-se = sd/sqrt(N))
-help(aggregate)
-with(df,aggregate(actvFraction,by=motorState,mean))
-with(df,aggregate(actvFraction ~ motorState,mean))
-aggregate(actvFraction ~ motorState,df,mean)
-aggregate(actvFraction ~ motorState,df,sum)
-myFormula <- (actvFraction ~ motorState)#
-#
- ddply(df, myFormula, summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction), 2),#
- sd = round(sd(actvFraction), 2), #
- N = length(actvFraction), #
- se = sd/sqrt(N))
-ddply(df, ~ motorState, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(df, actvFraction ~ motorState, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-dfx
-ddply(df, .(actvFraction ~ motorState), summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(df, actvFraction ~ motorState, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(df, ~ motorState, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(dfx, ~ group, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(dfx, sex ~ group, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(dfx, age ~ group, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(dfx, ~ group + age, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(dfx, ~ group + sez, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(dfx, ~ group + sex, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(dfx, ~ group + sex, summarize,#
- sum = round(sum(age),2),#
- N = length(age))
-ddply(dfx, sex ~ group, summarize,#
- sum = round(sum(age),2),#
- N = length(age))
-as.quoted(sex,group)
-myFormula <- ~ group + sex
-myFormula
-ddply(dfx, myFormula, summarize,#
- sum = round(sum(age),2),#
- N = length(age))
-myFormula <- ~ motorState#
- ddply(df, myFormula, summarize,#
- sum = round(sum(actvFraction),2),#
- N = length(actvFraction))
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction), 2),#
- med = round(median(actvFraction),2),#
- std = round(sd(actvFraction), 2), #
- N = sum(!is.na(actvFraction), #
- sem = std/sqrt(N),#
- CI95 = qnorm(0.975)*sem,#
- medSD = round(mad(actvFraction)),#
- seMed<-1.25*sem)
-)
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction), 2),#
- med = round(median(actvFraction),2),#
- std = round(sd(actvFraction), 2), #
- N = sum(!is.na(actvFraction), #
- sem = std/sqrt(N),#
- CI95 = qnorm(0.975)*sem,#
- medSD = round(mad(actvFraction),2),#
- seMed = 1.25*sem)
-))
-help(mad)
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction),2),#
- med = round(median(actvFraction),2),#
- std = round(sd(actvFraction),2), #
- N = sum(!is.na(actvFraction), #
- sem = std/sqrt(N))
-)
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction), 2),#
- sd = round(sd(actvFraction), 2), #
- N = length(actvFraction), #
- se = sd/sqrt(N))
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction), 2),#
- sd = round(sd(actvFraction), 2), #
- N = length(actvFraction), #
- sem = sd/sqrt(N))
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction), 2),#
- std = round(sd(actvFraction), 2), #
- N = length(actvFraction), #
- se = std/sqrt(N))
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction),2),#
- med = round(median(actvFraction),2),#
- std = round(sd(actvFraction),2), #
- N = sum(!is.na(actvFraction)), #
- sem = std/sqrt(N),#
- CI95 = qnorm(0.975)*sem,#
- medSD = round(mad(actvFraction),2),#
- seMed = 1.25*sem)
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction),2),#
- std = round(sd(actvFraction),2), #
- N = sum(!is.na(actvFraction)), #
- sem = std/sqrt(N),#
- CI95 = qnorm(0.975)*sem,#
- median = round(median(actvFraction),2),#
- medSD = round(mad(actvFraction),2),#
- seMed = 1.25*sem)
-all.vars(myFormula)
-ddply(df, c("motorState"), summarize,#
- sum = round(sum(actvFraction),2),#
- mean = round(mean(actvFraction),2),#
- std = round(sd(actvFraction),2), #
- N = sum(!is.na(actvFraction)), #
- sem = std/sqrt(N),#
- CI95 = qnorm(0.975)*sem,#
- median = round(median(actvFraction),2),#
- medSD = round(mad(actvFraction),2),#
- seMed = 1.25*sem)
-help(t.test)
-measVar = "actvFraction"#
-groupVars = c("motorState")
-myFormula <- as.formula(paste(measVar, paste(groupVars, collapse=" + "), sep=" ~ "))
-myFormula
-t.test(myFormula,df)
-help(t.test)
-t.test(myFormula,df,alternative="less")
-t.test(myFormula,df,alternative="greater")
-printSummary <- function(data=NULL, measVar, groupVars=NULL, na.rm=FALSE,#
- conf.interval=.95, .drop=TRUE) {#
- require(plyr)#
-#
- #Handle NAs#
- len <- function (x, na.rm=FALSE) {#
- if (na.rm) sum(!is.na(x))#
- else length(x)#
- }#
-#
- #Main summary#
- data2 <- ddply(data, groupVars, .drop=.drop,#
- .fun = function(xx, col) {#
- c(N = len(xx[[col]], na.rm=na.rm),#
- mean = mean (xx[[col]], na.rm=na.rm),#
- sd = sd (xx[[col]], na.rm=na.rm)#
- )#
- },#
- measVar#
- )#
-#
- #Standard error of the mean#
- data2$se <- data2$sd / sqrt(data2$N)#
-#
- #Confidence interval of 95%#
- data2$CI95 = qnorm(0.975)*se#
-#
- return(data2)#
-}
-printSummary(df, measVar,groupVars)
-printSummary <- function(data=NULL, measVar, groupVars=NULL, na.rm=FALSE,#
- conf.interval=.95, .drop=TRUE) {#
- require(plyr)#
-#
- #Handle NAs#
- len <- function (x, na.rm=FALSE) {#
- if (na.rm) sum(!is.na(x))#
- else length(x)#
- }#
-#
- #Main summary#
- data2 <- ddply(data, groupVars, .drop=.drop,#
- .fun = function(xx, col) {#
- c(N = len(xx[[col]], na.rm=na.rm),#
- mean = mean (xx[[col]], na.rm=na.rm),#
- sd = sd (xx[[col]], na.rm=na.rm)#
- )#
- },#
- measVar#
- )#
-#
- #Standard error of the mean#
- data2$se <- data2$sd / sqrt(data2$N)#
-#
- #Confidence interval of 95%#
- data2$CI95 = qnorm(0.975)*data2$se#
-#
- return(data2)#
-}
-printSummary(df, measVar,groupVars)
-help(wilcox.test)
-wilcox.test(myFormula,df)
-printSummary <- function(data=NULL, measVar, groupVars=NULL, na.rm=FALSE,#
- conf.interval=.95, .drop=TRUE) {#
- require(plyr)#
- #Handle NAs#
- len <- function (x, na.rm=FALSE) {#
- if (na.rm) sum(!is.na(x))#
- else length(x)#
- }#
- #Main summary#
- data2 <- ddply(data, groupVars, .drop=.drop,#
- .fun = function(xx, col) {#
- c(N = len(xx[[col]], na.rm=na.rm),#
- sum = sum (xx[[col]], na.rm=na.rm),#
- mean = mean (xx[[col]], na.rm=na.rm),#
- sd = sd (xx[[col]], na.rm=na.rm)#
- )#
- },#
- measVar#
- )#
- #Standard error of the mean#
- data2$se <- data2$sd / sqrt(data2$N)#
- #Confidence interval of 95%#
- data2$CI95 = qnorm(0.975)*data2$se#
- return(data2)#
- }
-printSummary(df, measVar,groupVars)
-quit()
-require(ggplot2)
-mag <- c(5.0,2.5,2.0,1.0,0.6)#
-umperpx <- c(2.270,4.525,5.850,11.350,19.178)#
-data <- data.frame(mag,umperpx)
-data
-require(ggplot2)#
-#The following shows that the relationship between objective magnification and pixel dimensions clearly follows a power equation relationship, a log-log relationship which follows the form y = bx^m#
-qplot(log(mag),log(umperpx),data=data)#
-qplot(mag,log(umperpx),data=data)#
-qplot(mag,umperpx,data=data)
-qplot(log(mag),log(umperpx),data=data)
-qplot(mag,log(umperpx),data=data)
-qplot(mag,umperpx,data=data)
-qplot(mag,log(umperpx),data=data)
-qplot(log(mag),log(umperpx),data=data)
-lm.fit <- lm(umperpx ~ mag,data=data)
-lm.fit <- lm(log(umperpx) ~ log(mag),data=data)
-lm.fit
-summary(lm.fit)
-with(data, plot(mag, umperpx, log="xy"))#
-lines(seq(0.5,5,by=0.5), exp(predict(lm.fit, data.frame(mag = seq(0.5,5,by=0.5)))))
-with(data, plot(mag, umperpx))#
-lines(seq(0.5,5,by=0.5), predict(lm.fit, data.frame(mag = seq(0.5,5,by=0.5))))
-x=seq(0.5,5,by=0.01)#
-#plot(data,pch=22)#
-b = exp(2.439593) #the intercept estimate was predicted based on the log-transformed data, this must be converted back to the normal space with natural exponent exp()#
-m = -1.004096
-fitted.data <- data.frame(x = x, y = b*x^m)
-ggplot(data, aes(x = mag, y = umperpx)) + geom_line(data = fitted.data, aes(x = x, y = y), colour = "red") + geom_point() + title(main = "y=11.46837*x^-1.004096")
-quartz();#
-p <- ggplot(data, aes(x = mag, y = umperpx))#
-p + geom_line(data = fitted.data, aes(x = x, y = y), colour = "red") + geom_point()
-p + geom_line(data = fitted.data, aes(x = x, y = y), colour = "red") + geom_point() + title(main = "y=11.46837*x^-1.004096")
-help.start()
-p + geom_line(data = fitted.data, aes(x = x, y = y), colour = "red") + geom_point() + ggtitle(main = "y=11.46837*x^-1.004096")
-ggtitle("y=11.46837*x^-1.004096")
-p + geom_line(data = fitted.data, aes(x = x, y = y), colour = "red") + geom_point() + ggtitle("y=11.46837*x^-1.004096")
-x
-cbind(x,y)
-fitted.data
-ggsave(file="obj-pixeldim-power-law-regression")
-ggsave(file="obj-pixeldim-power-law-regression.pdf")
-quit()
-time <- c(0, 0.200, 1.2, 2.2, 5.6)
-frame <- c(1,2,7,12,29)
-marker <- c(0,0.001,1.207,2.212,5.628)
-plot(time,frame)
-help(diff)
-marker - time
-marker <- c(0,0.201,1.207,2.212,5.628)
-marker - time
-plot(time,marker)
-plot(frame,time-marker)
-plot(frame,marker-time)
-data = data.frame(time,frame,marker)
-lm.fit <- lm(marker-time ~ frame,data=data)
-lm.fit
-summary(lm.fit)
-lm.fit
-b = -0.0005725; m = 0.0009973;
-b
-m
-(0.200 - b)/m
-(0.200)/m
-200*0.2
-time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- frame <- c(1,2,6,26,101)#
- marker <- c(0.0005,0.2015,1.0055,5.0255,20.1004)
-plot(frame,marker-time)
-data = data.frame(time,frame,marker)#
- lm.fit <- lm(marker-time ~ frame,data=data)#
- lm.fit
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- lm.fit <- lm(marker-time ~ frame,data=data)#
- coef(lm.fit)
-coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- data = data.frame(time,frame,marker)#
- lm.fit <- lm(marker-time ~ frame,data=data)#
- coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-data <- data.frame(time,frame,marker)#
- lm.fit <- lm(marker-time ~ frame,data=data)#
- coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- frame <- c(1,2,6,26,101)#
- marker <- c(0.0005,0.2015,1.0055,5.0255,20.1004)#
- data <- data.frame(time,frame,marker)#
- lm.fit <- lm(marker-time ~ frame,data=data)#
- lm.fit
-coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- data <- data.frame(time,frame,marker)#
- data$diff <- marker-time#
- lm.fit <- lm(diff ~ frame,data=data)#
- coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-quartz()
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(frame,marker-time)
-plot(time,marker-time)
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time
-21*0.2
-21*5
-20.2*5
-time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- frame <- c(1,2,6,26,101)#
- marker <- c(0.0005,0.2015,1.0055,5.0255,20.1004)#
- marker - time
-time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- frame <- c(1,2,6,26,101)#
- marker <- c(0.0005,0.2015,1.0055,5.0255,20.1004)#
- marker - time#
- df <- data.frame(time,frame,marker)#
- lm.fit <- lm(marker-time ~ frame,data=df)#
- lm.fit#
- coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-quartz();#
- frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(time,marker-time)#
- df <- data.frame(time,frame,marker)#
- data$diff <- marker-time#
- lm.fit <- lm(diff ~ frame,data=df)#
- coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-quartz();#
- frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(time,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lm.fit <- lm(diff ~ frame,data=df)#
- coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(time,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- x <- coef(lmfit)["frame"] #
- (0.200-b)/m
-quartz();#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- frame <- c(1,2,6,26,101)#
- marker <- c(0.0005,0.2015,1.0055,5.0255,20.1004)#
- marker - time#
- plot(frame, marker-time)#
- df <- data.frame(time,frame,marker)#
- lm.fit <- lm(marker-time ~ frame,data=df)#
- lm.fit#
- coef(lm.fit) #
- b = coef(lm.fit)["(Intercept)"] #
- x = coef(lm.fit)["frame"] #
- (0.200-b)/m
-quartz();#
- frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- x <- coef(lmfit)["frame"] #
- (0.200-b)/m
-rm(lm.fit)
-rm(lmfit)
-time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- frame <- c(1,2,6,26,101)#
- marker <- c(0.0005,0.2015,1.0055,5.0255,20.1004)#
- marker - time#
- plot(frame, marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lm.fit <- lm(diff ~ frame,data=df)#
- lm.fit#
- coef(lm.fit) #
- b <- coef(lm.fit)["(Intercept)"] #
- x <- coef(lm.fit)["frame"] #
- (0.200-b)/m
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- x <- coef(lmfit)["frame"] #
- (0.200-b)/m
-df
-time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- frame <- c(1,2,6,26,101)#
- marker <- c(0.0005,0.2015,1.0055,5.0255,20.1004)#
- marker - time#
- plot(frame, marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time
-df
-(0.200-b)/m
-b
-m
-(0.200)/m
-b
-is.numeric(b)
-quartz();#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- frame <- c(1,2,6,26,101)#
- marker <- c(0.0005,0.2015,1.0055,5.0255,20.1004)#
- marker - time#
- plot(frame, marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lm.fit <- lm(diff ~ frame,data=df)#
- lm.fit#
- coef(lm.fit) #
- b <- coef(lm.fit)["(Intercept)"] #
- m <- coef(lm.fit)["frame"] #
- (0.200-b)/m
-quartz();#
- frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- m <- coef(lmfit)["frame"] #
- (0.200-b)/m
-10000/200
-coef(lmfit)
-1/600
-1/6100
-1/100
-1/30
-1/60
-1/300
-1/600
-quartz();#
- frame <- c(1,2,6,26,101)#
- time <- c(0.0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.0,0.2012,1.0016,5.0034,20.0102)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- m <- coef(lmfit)["frame"] #
- (0.200-b)/m
-frame <- c(2,6,26,101)#
- time <- c(0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.2012,1.0016,5.0034,20.0102)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- m <- coef(lmfit)["frame"] #
- (0.200)/m
-(0.2-b)/m
-1100*0.2
-quartz();#
- frame <- c(2,6,26,101)#
- time <- c(0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.2012,1.0016,5.0034,20.0102)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- m <- coef(lmfit)["frame"] #
- (0.200-b)/m
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- m <- coef(lmfit)["frame"] #
- (0.200-b)/m
-m*3000 + b
-m*3000
-b
-m
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.00002,0.20004,1.00012,5.00052,20.00202)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- m <- coef(lmfit)["frame"] #
- (0.200-b)/m
-m
-m*3000
-m*5000
-m*3000 + b
-frame <- c(1,2,6,26,101)#
- time <- c(0, 0.200, 1.0, 5.0, 20.0)#
- marker <- c(0.0,0.200,0.999960,4.9998,19.999240)#
- marker-time#
- plot(frame,marker-time)#
- df <- data.frame(time,frame,marker)#
- df$diff <- marker-time#
- lmfit <- lm(diff ~ frame,data=df)#
- coef(lmfit) #
- b <- coef(lmfit)["(Intercept)"] #
- m <- coef(lmfit)["frame"] #
- (0.200-b)/m
-m*3000 + b
-quit();
-help(pearson)
-??pearson
-help(cor)
-quit()
-edgelist<-read.delim('/Users/ackman/Data/2photon/131208/2014-01-07-003602/dCorr.txt')
-edgelist
-require(pylr)#
-df <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue = mean(rvalue),#
-sd = sd(rvalue), #
-N = length(rvalue), #
-se = sd/sqrt(N))
-require(plyr)#
-df <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue = mean(rvalue),#
-sd = sd(rvalue), #
-N = length(rvalue), #
-se = sd/sqrt(N))
-df
-df <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalueMean = mean(rvalue),#
-sd = sd(rvalue), #
-N = length(rvalue), #
-se = sd/sqrt(N))
-df
-df <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = sd/sqrt(N))
-df <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))
-df
-colnames(df)
-colnames(df)['rvalue.mean']
-colnames(df) == 'rvalue.mean'
-colnames(df)[['rvalue.mean']]
-colnames(df)
-colnames(df) == 'rvalue.mean'
-colnames(df)[colnames(df) == 'rvalue.mean']
-colnames(df)[colnames(df) == 'rvalue.mean'] <- 'rvalue'
-colname(df)
-colnames(df)
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(df,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-load(plyr)
-help(require)
-library(plyr)
-library(igraph)#
-library(RColorBrewer)
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(df,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-rthresh <- 0.2#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(df,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-edgelist<-read.delim('/Users/ackman/Data/2photon/120518i/2014-01-03-231550/dCorr.txt'
-)
-edgelist<-read.delim('/Users/ackman/Data/2photon/120518i/2014-01-03-231550/dCorr.txt')
-df <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(df)[colnames(df) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.2#
-fnm <- '120518'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(df,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-rthresh <- 0.1#
-fnm <- '120518'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(df,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-summary(g)
-print(g)
-print(fastGreeyCom)
-print(fastGreedyCom)
-print(fastgreedyCom)
-degree(g)#
-degree.distribution(g)#
-degree.distribution(g,cumulative = TRUE)
-average.path.length(g)
-library(ggplot2)
-df <- data.frame(degree(g))#
-colnames(df) <- c("degree")#
-p <- ggplot(df, aes(x=degree)) + xlab("degree") + theme_bw()#
-p + geom_histogram(binwidth = 2) + scale_colour_brewer(palette="Set1") + opts(aspect.ratio=1) #raw counts#
-ggsave(file=paste("120518_07-degreeDist", format(Sys.time(),"%y%m%d-%H%M%S"), ".pdf",sep=""))
-g <- barabasi.game(1000) # increase this number to have a better estimate#
-d <- degree(g, mode="in")#
-fit1 <- power.law.fit(d+1, 10)#
-fit2 <- power.law.fit(d+1, 10, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-g
-d
-df <- data.frame(degree(g))#
-colnames(df) <- c("degree")#
-p <- ggplot(df, aes(x=degree)) + xlab("degree") + theme_bw()#
-p + geom_histogram(binwidth = 2) + scale_colour_brewer(palette="Set1") + opts(aspect.ratio=1) #raw counts
-This should approximately yield the correct exponent 3#
-g <- barabasi.game(1000) # increase this number to have a better estimate#
-d <- degree(g, mode="in")#
-fit1 <- power.law.fit(d+1, 10)#
-fit2 <- power.law.fit(d+1, 10, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)#
-#
-df <- data.frame(degree(g))#
-colnames(df) <- c("degree")#
-p <- ggplot(df, aes(x=degree)) + xlab("degree") + theme_bw()#
-p + geom_histogram(binwidth = 2) + scale_colour_brewer(palette="Set1") + opts(aspect.ratio=1) #raw counts#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-title("barabasi.game(1000), powerlaw")#
-quartz.save(file=paste(dateStr, "-degreeDist-", "barabasiGame-powerlaw", ".pdf",sep=""), type = "pdf")
-ggsave(file=paste(dateStr, "-degreeDist-", "barabasiGame-powerlaw", ".pdf",sep=""))
-This should approximately yield the correct exponent 3#
-g <- barabasi.game(33) # increase this number to have a better estimate#
-d <- degree(g, mode="in")#
-fit1 <- power.law.fit(d+1, 10)#
-fit2 <- power.law.fit(d+1, 10, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)#
-#
-df <- data.frame(degree(g))#
-colnames(df) <- c("degree")#
-p <- ggplot(df, aes(x=degree)) + xlab("degree") + theme_bw()#
-p + geom_histogram(binwidth = 2) + scale_colour_brewer(palette="Set1") + opts(aspect.ratio=1) #raw counts#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-title("barabasi.game(33), powerlaw")#
-ggsave(file=paste(dateStr, "-degreeDist-", "barabasiGame-powerlaw", ".pdf",sep=""))
-fit1
-fit2
-d
-This should approximately yield the correct exponent 3#
-g <- barabasi.game(1000) # increase this number to have a better estimate#
-d <- degree(g, mode="in")#
-fit1 <- power.law.fit(d+1, 10)#
-fit2 <- power.law.fit(d+1, 10, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-fit2
-fit2
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-print(fastgreedyCom)#
-degree(g)#
-degree.distribution(g)#
-degree.distribution(g,cumulative = TRUE)#
-average.path.length(g) #
-diameter(g)
-centrality(g)
-hub.score(g)
-hub.score(g)$vector
-------Histogram of degree distribution-------------------------------------------------------------#
-df <- data.frame(degree(g))#
-colnames(df) <- c("degree")#
-p <- ggplot(df, aes(x=degree)) + xlab("degree") + theme_bw()#
-p + geom_histogram(binwidth = 2) + scale_colour_brewer(palette="Set1") + opts(aspect.ratio=1) #raw counts#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-ggsave(file=paste(dateStr, "-degreeDist-", fnm, ".pdf",sep=""))
-edgelist<-read.delim('/Users/ackman/Data/2photon/131208/2014-01-07-003602/dCorr.txt')
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-print(fastgreedyCom)#
-degree(g)#
-degree.distribution(g)#
-degree.distribution(g,cumulative = TRUE)#
-average.path.length(g) #
-diameter(g)#
-hub.score(g)$vector
-hub.score(g)
-------Histogram of degree distribution-------------------------------------------------------------#
-df <- data.frame(degree(g))#
-colnames(df) <- c("degree")#
-p <- ggplot(df, aes(x=degree)) + xlab("degree") + theme_bw()#
-p + geom_histogram(binwidth = 2) + scale_colour_brewer(palette="Set1") + opts(aspect.ratio=1) #raw counts#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-ggsave(file=paste(dateStr, "-degreeDist-", fnm, ".pdf",sep=""))
-help(degree)
-g <- graph.ring(10)#
-degree(g)#
-g2 <- erdos.renyi.game(1000, 10/1000)#
-degree.distribution(g2)
-plot(degree.distribution(g2))
-plot(degree.distribution(g2))
-quartz;plot(degree.distribution(g2))
-quartz; plot(degree.distribution(g2))
-edgelist<-read.delim('/Users/ackman/Data/2photon/131208/2014-01-07-003602/dCorr.txt')
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-quartz()
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-print(fastgreedyCom)#
-degree(g)#
-degree.distribution(g)#
-degree.distribution(g,cumulative = TRUE)#
-average.path.length(g) #
-diameter(g)#
-hub.score(g)$vector
-mean(degree(g))
-d <- degree(g)#
-fit1 <- power.law.fit(d+1, 10)#
-fit2 <- power.law.fit(d+1, 10, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-fit2
-hist(degree(g))
-help(power.law.fit)
-d <- degree(g)#
-fit1 <- power.law.fit(d+1)#
-fit2 <- power.law.fit(d+1, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d)#
-fit2 <- power.law.fit(d, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d
-d <- degree(g)#
-fit1 <- power.law.fit(d,2)#
-fit2 <- power.law.fit(d,2, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d,3)#
-fit2 <- power.law.fit(d,3, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d,4)#
-fit2 <- power.law.fit(d,4, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d,5)#
-fit2 <- power.law.fit(d,5, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d,8)#
-fit2 <- power.law.fit(d,8, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d,7)#
-fit2 <- power.law.fit(d,7, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d,6)#
-fit2 <- power.law.fit(d,6, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d)
-fit1$alpha
-fit1$xmin
-fit1
-edgelist<-read.delim('/Users/ackman/Data/2photon/120518i/2014-01-03-231550/dCorr.txt')
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-edgelist<-read.delim('/Users/ackman/Data/2photon/120518i/2014-01-03-231550/dCorr.txt')
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-print(fastgreedyCom)#
-degree(g)#
-degree.distribution(g)#
-degree.distribution(g,cumulative = TRUE)#
-average.path.length(g) #
-diameter(g)#
-hub.score(g)$vector
-mean(degree(g))
-d <- degree(g)#
-fit1 <- power.law.fit(d)
-fit1
-d <- degree(g)#
-fit1 <- power.law.fit(d,7)#
-fit2 <- power.law.fit(d,7, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-d <- degree(g)#
-fit1 <- power.law.fit(d,3)#
-fit2 <- power.law.fit(d,3, implementation="R.mle")#
-#
-fit1$alpha#
-coef(fit2)#
-fit1$logLik#
-logLik(fit2)
-edgelist<-read.delim('/Users/ackman/Data/2photon/131208/2014-01-07-003602/dCorr.txt')
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-quartz()
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.1#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-rthresh <- 0.15#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-edgelist<-read.delim('/Users/ackman/Data/2photon/120518i/2014-01-03-231550/dCorr.txt')
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.15#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-rthresh <- 0.15#
-fnm <- 'younger'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-edgelist<-read.delim('/Users/ackman/Data/2photon/131208/2014-01-07-003602/dCorr.txt')
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.15#
-fnm <- 'P8'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")
-edgelist<-read.delim('/Users/ackman/Data/2photon/131208/2014-01-07-003602/dCorr.txt')
-help(for)
-help()
-rthresh <- 0.2#
-fnm <- '131208_01'#
-fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-d3 <- subset(edgelist,filename==fnm2)#
-d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d4,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-# quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-for(i in c('131208_01','131208_03','131208_04','131208_05')) {#
-for(j in c(0.1,0.15,0.2)) {#
-rthresh <- j#
-fnm <- '131208_03'#
-fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-d3 <- subset(edgelist,filename==fnm2)#
-d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d4,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-# quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")#
-}#
-}
-for(j in c(0.1,0.15,0.2)) {#
-for(i in c('131208_01','131208_03','131208_04','131208_05')) {#
-rthresh <- j#
-fnm <- i#
-fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-d3 <- subset(edgelist,filename==fnm2)#
-d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d4,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-# quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-# quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")#
-}#
-}
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.15#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-edgelist<-read.delim('/Users/ackman/Data/2photon/120518i/2014-01-03-231550/dCorr.txt')
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.15#
-fnm <- '131208'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-d2 <- ddply(edgelist, c("node1","node2"), summarize,#
-rvalue.mean = mean(rvalue),#
-rvalue.sd = sd(rvalue), #
-N = length(rvalue), #
-rvalue.sem = rvalue.sd/sqrt(N))#
-colnames(d2)[colnames(d2) == 'rvalue.mean'] <- 'rvalue'#
-#
-rthresh <- 0.15#
-fnm <- '120518'#
-# fnm2 <- paste(fnm,".tif",sep="")#
-lo <- 'layout.fruchterman.reingold'#
-# lo <- 'layout.kamada.kawai'#
-# lo <- 'layout.lgl'#
-# d3 <- subset(edgelist,filename==fnm2)#
-# d4 <- with(d3,data.frame(node1,node2,rvalue))#
-edgelist2<-subset(d2,rvalue > rthresh)#
-g <- graph.data.frame(edgelist2, directed=FALSE)#
-E(g)$weight <- E(g)$rvalue#
-E(g)$width <- 1#
-E(g)[ weight >= 0.3 ]$width <- 3#
-E(g)[ weight >= 0.5 ]$width <- 5#
-fastgreedyCom<-fastgreedy.community(g,weights=E(g)$weight)#
-V(g)$color <- fastgreedyCom$membership#
-quartz();#
-# palette(rainbow(max(V(g)$color),alpha=0.5))#
-mypalette <- adjustcolor(brewer.pal(max(V(g)$color),"Set1"),0.6)#
-palette(mypalette)#
-plot(g, layout=eval(parse(text=lo)), edge.width=E(g)$width, edge.color="black", vertex.label.color="black")#
-# palette("default")#
-title(paste(fnm,', fastgreedy default, ', lo, 'r>', rthresh))#
-dateStr=format(Sys.time(),"%y%m%d-%H%M%S")#
-quartz.save(file=paste(dateStr, "-", fnm, ".png",sep=""), type = "png", dpi=150)#
-quartz.save(file=paste(dateStr, "-", fnm, ".pdf",sep=""), type = "pdf")
-quit()
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