similar to: fixed trimmed mean for j-group

Hello, I haven't found errors in your code. I implemented the test in the paper (the first, fixed symetric mean) and it also gives me zero Type I errors, when alpha = 0.05. Try to see the value of min(pv) or to plot the histogram of 'pv', hist(pv) and you'll see that there are no significant p-values, at that level. Anyway I'll continue to look at it, but my first

Hello everyone, I want to calculate type I error rate for modified F statistic for one way robust anova. I need to find the j group trimmed mean and winsorized sum of squared deviations. Here I attached my code for j=2 to make it simple. Originally I have j=4. Hope you can help. I need to run it for 1000 times My problem is: i) the value of F-test obtain from my simulation below is in negative

Hello Everyone, My name is Thomas and I have been using R for one week. I recently found your site and have been able to search the archives of posts. This has given me some great information that has allowed me to craft an initial design to an inquiry I would like to make into the breakdown of McNemar's test. I have read an intro to R manual and the posting guides and hope I am not violating

Dear UseRs, MY PROBLEM IS A SMALL PIECE OF A REAL BIG AND A COMPLICATED PROBLEM. IF I DELIBERATE IN A VERY SIMPLE WAY THEN ALL I WANT IS TO PUT ALL THE POSSIBLE COMBINATIONS OF 75 DISTANCE MATRICES (BY TAKING 4 MATRICES, MORE COMMONLY 75C4), in the following equation. t<-as.matrix((MAT1)^2+(MAT2)^2+(MAT3)^2+(MAT4)^2+,upper=T,diag=T)) Then "1215450" values of "t"(one for

I would use replicate() to do an operation with random numbers repeatedly: ``` mysim <- replicate(10, { two.mat <- matrix(rnorm(4), 2, 2) four.mat <- matrix(rnorm(16), 4, 4) list(two.mat = two.mat, four.mat = four.mat) }) ``` which should give you a matrix-list. You can slice this matrix-list just like normal, then cbind it in one step: ``` two.mat <-

> > I am looking for a more elegant way to write below code. > > #Simulation results have different dimensions > mysim <- lapply(1:10, function(y) { > two.mat <- matrix(rnorm(4), nrow = 2) > four.mat <- matrix(rnorm(16), nrow = 4) > list(two.mat = two.mat, four.mat = four.mat) #results with different dimensions > }) > > #Collect different

rm(list=ls()) yx.df<-read.csv("c:/MK-2-72.csv",sep=',',header=T,dec='.') dim(yx.df) #get X matrix y<-yx.df[,1] x<-yx.df[,2:643] #conver to matrix mat<-as.matrix(x) #get row number rownum<-nrow(mat) #remove the constant parameters mat1<-mat[,apply(mat,2,function(.col)!(all(.col[1]==.col[2:rownum])))] dim(yx.df) dim(mat1) #remove columns with numbers of

Hi, I had the following code: testp <- rcorr(t(datcm1),type = "pearson") mat1 <- testp[[1]][,] > 0.6 mat2 <- testp[[3]][,] < 0.05 mat3 <- mat1 + mat2 The resulting mat3 (smaller version) matrix looks like: NA 0 0 0 0 NA 0 NA 0 0 NA 2 0 0 2 NA To get to the number of times a '2' appears in the rows, I was

Dear all, I have a collection of 5000 entries which represent the evolutionary rates of 3 animals. I would like to show the differences between the rates of all 3 animals and have tried using the function parallel (from the lattice package) and pairs() function. The parallel function would have been perfect save for the large number of data (5000). The pairs() function doesn't show

Dear R heleprs   Suppose I have following data   Scenarios combination_names     series1    series2 Sc1 MAT2 GAU1 7.26554 8.409778 Sc2 MAT2 GAU2 7.438128 8.130275 Sc3 MAT3 GAU1 8.058422 8.06457 Sc4 MAT1 GAU2 8.179855 8.022071 Sc5 MAT3 GAU2 8.184033 8.191831 Sc6 MAT3 GAU2 7.50312 8.232425 Sc7 MAT1 GAU2 7.603291 8.200993 Sc8 MAT1 GAU1 8.221755 8.380097 Sc9 MAT3 GAU2 7.904908

Sorry for the verbose example. I want to row bind two matrices, and all works except I want the column labelled "row" to be sequential in the new matrix, shown as "mat3" here, i.e. needs to be 1:6 and not 1:3 repeated twice. Any suggestions? Thanks J > colnm1 <- c("row","ti","counti") > colnm2 <-

Dear all, Ket say I have 3 matrices : mat1 <- matrix(rnorm(16), 4) mat2 <- matrix(rnorm(16), 4) mat3 <- matrix(rnorm(16), 4) Now I want to merge those three matrices to a single one with dimension 4*3=12 and 4 wherein on resulting matrix, row 1,4,7,10 will be row-1,2,3,4 of "mat1", row 2,5,8,11 will be row-1,2,3,4 of "mat2" and row 3,6,8,12 will be row-1,2,3,4 of

Hi, How to turn a character string into an unevaluated R object? I want to load some files in a directory into data matrix R objects. I could do this with read.table and assign (see below). Then, I want to turn the character string representing a file name (the evaluated expression of i) into an unevaluated R object. Basically, I want to create matrices whose names are the same as the related file

Hi, I'm almost there with my Yasm printer, however, I've stumbled upon this. Using this: ; ModuleID = 'data.u' %window = type { %visobj, %vec2, %vec2, %vec2, %string, %color, i32, i32, %string, %string, %string, i1, i1, i1, i1, i1, i1, i32, i8* } %visobj = type { %object, i1, i1, i1, i1, %color, %vec4, %vec4, %vec4, %vec4, i32, %mat4, %mat4, %mat4, %mat4, %material*,

Hello R-helpers, I want to ask your opinion since I am not so sure how to do it. This is regarding one part of my paper project and my situation is: Stage I I have 2 groups and for each group I need to compute the following steps; i) Generate 3 random numbers from normal distribution and square them. ii) Repeat step 1 for 15 times and at the end I will get 15 random numbers. I already done

Dear All I new to using R and am struggling with some matrix multiplication. I have two matrices, one containing random numbers, these are multiplied together to get another matrix which is different each time. When I put in another for loop to repeat this process a multiple times the matrices are all the same. I?m sure there is a way to keep the randomness of the different matrices but I think

Hi.. i have an expression of the form: model1<-nls(y~beta1*(x1+(k1*x2)+(k1*k1*x3)+(k2*x4)+(k2*k1*x5)+(k2*k2*x6)+(k3*x7)+(k3*k4*x8)+(k3*k2*x9)+(k3*k3*x10)+ (k4*x11)+(k4*k1*x12)+(k4*k2*x13)+(k4*k3*x14)+(k4*k4*x15)+(k5*x16)+(k5*k1*x17)+(k5*k2*x18)+(k5*k3*x19)+

The example below creates parallel time-series plots of three different y variables conditioned by a dichotomous factor. In the graphical layout, ? Each y variable inhabits its own row and is plotted on its own distinct scale. ? Each level of the factor has its own column, but within each row the scale is held constant across columns. ? The panels fit tightly (as they do

On my CentOS 5 box, in a C++ program that does much arithmetic, including numerous matrix multiplications, I have a situation in in which the result depends on the nature of nearby I/O. Thus, with all arithmetic done with type double, and where values are mostly in the range [-1.0e0,+1.0e0] or nearby, I do: cerr << "some stuff" << endl; mat3 = matmult(mat1,mat2); I

I have a matrix which I wanted to convert to a data frame. As I could not succeed and resorted to export to csv and reimport it again. Why did I fail in the attempt and how can I achieve what I wanted without this roundabouts? The original matrix: > str(comb_model0) num [1:90, 1:4] 3.5938 0.0274 0.0342 0.0135 0.0207 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:90]