search for: meanx

...an ### dummy data building test <- data.frame(Subject=rep(c("A", "B", "C", "D"), each=6), Type=rep(c("EEE", "FFF", "GGG", "HHH", "III", "JJJ"), times=4), MeanX=abs(rnorm(24)), MeanZ=abs(rnorm(24))) ### data for regrouping all means all <- data.frame(Type=levels(factor(test$Type)), MeanX = c(0), MeanZ = c(0)) ### building means... for(s in levels(factor(test$Subject))) { tmp <- data.frame(Type = all$Type) for(t in tmp...

...2,1) v=matrix(c(1,0,0,1),2,2) Y=function(X1,X2,mu=m,V=v) { X=matrix(c(X1,X2),2,1) a=(1/((2*pi)*sqrt(det(V))))*exp((-0.5)*(t(X-mu)%*%solve(V)%*%(X-mu))) a[1] } x1=seq(-1,1) x2=x1 Z=outer(x1,x2,FUN="Y",mu=m,V=v) persp(x1,x2,Z) my code: BINORMAL<-function(varx=1,vary=1,covxy=0,meanx=0,meany=0) { #the following function plots the density of a bi variate normal distribution covXY<-matrix(c(varx,covxy,covxy,vary),2,2) A<-solve(covXY) #up<-max(meanx+4*varx^.5,meanx-4*varx^.5,meany+4*vary^.5,meany-4*vary^.5) #x <- seq(-up,up,length=50) #y <- x x <- seq(m...

...in 1:n) { epsilon<-rnorm(length(s),0,sqrt(var)) for (j in 1:(length(s))) { data[j,i]<-((alpha*s[j]) / (s[j] + beta)) + (epsilon[j]*(s[j]/(s[j] + beta))) b<-data[,i] } #print(b) X<-b[-1]/s[-1] #print(X) #print(X) #print(length(b[-1])) #print(length(X)) meanX<-mean(X) #print(meanX) meanV<-mean(b) #print(meanV) betachap[i]<-((meanX*squareproduct(b[-1],b[-1]))- (meanV*squareproduct(X,b[-1])))/((meanV*squareproduct(X,X))- (meanX*squareproduct(X,b[-1]))) alphachap[i]<-meanV + betachap[i]*meanX varchap[i]<-((squareproduct(b[-1],b[-1])) + (2*b...

Hi, as an R-beginner, I have a recoding problem and hope you can help me: I am working on a SPSS dataset, which I loaded into R (load("C:/...) I have 2 existing Variables: "ID" and "X" , and one variable to be computed: meanX.dependID (=mean of X for all rows in which ID has the same value) ID = subject ID. Since it is a longitudinal dataset, there are repeated measurement points for each subject, each of which appears in a new row. So, each ID value appears in many rows. (e.g. ID ==1 in row 1:5; ID ==2 in rows 6:...

...dy = for (i in 1:length(n)){ split <- length(data)/(n[i]) for (j in 1:(n[i]/2)){ x = data[(1 + (j-1)*(2*split)):(round(split) + (j-1)*(2*split))] dx = cbind(dx,x) y = data[((round(split)+1) + (j-1)*(2*split)):(2*j*split)] dy = cbind(dy,y) }} dx = dx[,2:dim(dx)[2]] dy = dy[,2:dim(dy)[2]] k=0 meanx=0 meany=0 sdx=0 sdy=0 nx=0 ny=0 for (k in 1:dim(dx)[2]) { meanx[k] = mean(unique(dx[,k])) meany[k] = mean(unique(dy[,k])) sdx[k] = sd(unique(dx[,k])) sdy[k] = sd(unique(dy[,k])) nx[k] = length(unique(dx[,k])) ny[k] = length(unique(dy[,k])) } t = (meanx-meany)/sqrt((sdx^2/nx) + (sdy^2/ny)) df = ((...

Hi Experts, I am new to R, and was wondering how to do 3D linear regression in R. In other words, I need to Fit a 3-Dimensional Line to Data Points (input). I googled before posting this, and found that it is possible in Matlab and other commercial packages. For example, see the Matlab link:

Hello, we have 80 text files with matrices. Each matrix represents a map (rows for latitude and columns for longitude), the 80 maps represent steps in time. In addition, we have a vector x of length 80. We would like to compute a regression between matrices (response through time) and x and create maps representing coefficients, r2 etc. Problem: the 80 matrices are of the size 4000 x 3500 and we

I have a scatter plot with 10000 points.? I would like to add a line that bins every 50 points and connects the average of each bin.? I'm looking for something similar to line type "m" in Stata. With this dataset of 10000 points, I would also like to bin the data and make boxplots at certain intervals, so that I have a set of boxplots to represent each bin.? I would also like the

I want to plot a heatmap without reordering the columns. This works fine in "heatmap": > heatmap(meanX[selected,], col=cm.colors(256), Colv=NA) But in "heatmap.2" I get: > heatmap.2(meanX[selected,], col=cm.colors(256), Colv=NA) Error in if (!is.logical(Colv) || Colv) ddc <- reorder(ddc, Colv) : missing value where TRUE/FALSE needed (Note that instructions for the use of "C...

...01 14 5 15 45678 1902 15 4 16 45678 1900 16 3 17 45678 1901 17 2 18 45678 1902 18 1 result<-by(example[,3:4], example$id, by(example[,3:4], example$Year,colMeans, na.rm=T)) Error in FUN(X[[1L]], ...) : could not find function "FUN desired result should look like: id Year meanx mean y 1 12345 1900 ... ... 2 12345 1901 ... 3 12345 1902 ... 4 54321 1900 5 54321 1901 6 54321 1902 7 45678 1900 8 45678 1901 9 45678 1902 [[alternative HTML version deleted]]

..."lambda" "R2" [6] "RSS" "Cp" "actions" "entry" "Gamrat" [11] "arc.length" "Gram" "beta" "mu" "normx" [16] "meanx" I do not know what does RSS, arc.length, mu mean??? If mu means \hat{miu}, it does not seems to be right. I did not simulate my data with mean. besides I caculated R2 by myself but it cannot match the R2here I think R2=1-sum((y-x%*%lobj$beta)^2)/var(y)*19 #I have 20 observations...my R2...

Hi, I am new in R and have some frustrations as how to set debug breaks during emacs R debug. I use debug () as where or which function to debug. But during the debug, e.g., I have a for loop at the beginning of the function code and want the code execution to jump through that for loop and set a break after that. How to do that? Is there a web site detailing the syntax of the debugging of R?

Dear all, I have used following code but everytime I encounter a problem of not having coefficients for all the variables in the predictor set. # code rm(list=ls()) library(caret) # generating response and design matrix X<-matrix(rnorm(50*100),nrow=50) y<-rnorm(50*1) # Applying caret package con<-trainControl(method="cv",number=10) data<-NULL data<- train(X,y,