diff --git a/analysis/quantif_20131105/.Rhistory b/analysis/quantif_20131105/.Rhistory
new file mode 100644
index 0000000..c418161
--- /dev/null
+++ b/analysis/quantif_20131105/.Rhistory
@@ -0,0 +1,2199 @@
+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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