similar to: delete.response leaves response in attribute dataClasses

Below is a SIMEX object that was generated with the "simex" function from the "simex" package applied to a logistic regression fit. From this mountain of information I would like to extract all of the values summarized in this line: .. ..$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ... Can someone suggest how to go about doing this? I can extract the

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

Someone sent me a bug report for survival2.44.1-1 that involves a model with both cluster and offset.? It turns out to be a 3 part issue with [.terms and my own untangle.specials routine.?? I've spent an evening sorting out the details. ? 1. The delete.response() function doesn't remove the response from the dataClasses attribute, which leads to a later failure in [.terms for

Hi, I've performed an lda and obtained a classification table for some of my data: > efa.dfa<-lda(groups~.,efa.scores.8,CV=T) > str(efa.dfa) List of 5 $ class : Factor w/ 2 levels "1","2": 1 2 1 2 1 1 2 2 1 2 ... $ posterior: num [1:160, 1:2] 0.99083 0.00852 0.93983 0.23186 0.85931 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:160]

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

Greetings R experts, I'm having some difficulty recovering lda objects that I've saved within sublists using the save.image() function. I am running a script that exports a variety of different information as a list, included within that list is an lda object. I then take that list and create a list of that with all the different replications I've run. Unfortunately I've been

Hi everyone, Let me have a dataframe named ?mydata? and created as below, *> n=c(5,5,5,5) #number of trils > x1=c(2,3,1,3) ) #number of successes > x2=c(5,5,5,5) #number of successes > x3=c(0,0,0,0) #number of successes > x4=c(5,0,5,0) #number of successes > mydata=data.frame(n,x1,x2,x3,x4) > mydata* n x1 x2 x3 x4 1 5 2 5 0 5 2 5 3 5 0 0 3 5 1 5 0 5 4 5 3 5 0

I have a sparse contingency table (most cells are 0): > xtabs(~.,data[,idx:(idx+4)]) , , x3 = 1, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 31 2 0 0 112 3 0 0 94 , , x3 = 2, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 0 2 0 0 0 3 0 0 0 , , x3 = 3, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 0 2 0 0 0 3 0 0 0 , , x3 = 1, x4

Hi R-helpers, I have a character object called dd that has 32 elements each of which is a model formula contained within quotation marks. Here's what it looks like: > dd [1] "lm(y ~ 1,data=Cement)" "lm(y ~ X,data=Cement)" "lm(y ~ X1,data=Cement)" [4] "lm(y ~ X2,data=Cement)" "lm(y ~

Hi, Using S=1000 and simdata <- replicate(S, generate(3000)) #If you want both "m1" and "m0" #here the missing values are 0 res1<-sapply(seq_len(ncol(simdata.psm1)),function(i) {x1<-merge(simdata.psm0[,i],simdata.psm1[,i],all=TRUE); x1[is.na(x1)]<-0; x1}) res1[,997:1000] #????? [,1]???????? [,2]???????? [,3]???????? [,4]??????? #x1??? Numeric,3000 Numeric,3000

Dette er en melding med flere deler i MIME-format. --=_alternative 004613C000257091_= Content-Type: text/plain; charset="US-ASCII" And some more informastion I forgot. R does not crash if I write out the formula: set.seed(123) x1 <- runif(1000) x2 <- runif(1000) x3 <- runif(1000) x4 <- runif(1000) x5 <- runif(1000) x6 <- runif(1000) x7 <- runif(1000) x8 <-

I'm using specify.model for the sem package. I can't figure out how to represent the residual errors for the observed variables for a CFA model. (Once I get this working I need to add some further constraints.) Here is what I've tried: model.sa <- specify.model() F1 -> X1,l11, NA F1 -> X2,l21, NA F1 -> X3,l31, NA F1 -> X4,l41, NA F1 -> X5, NA, 0.20

Dear R-users I have a probelm running stepAIC in R1.7.1 I wrote a program which used stepAIC as a part of it, and it worked fine while I was using the previous version of R1.7.0. However, I found the program did not work any more. Now, R produces a message which tells "Error in as.data.frame.default(data) : can't coerce function into a data.frame" every time I run the part of

On January 5, I posted here concerning this issue (https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=14767), and I have not heard any answers from people who think this is not a bug, or a simple accident in the delete.response function. The output from delete.response on a terms object alters the formula by removing the dependent variable. It removes the response from the "variables"

Hello I have data like this x1 x2 x3 x4 x5 I want to create a matrix similar to a correlation matrix, but with the difference between the two values, like this x1 x2 x3 x4 x5 x1 x2-x1 x3-x1 x4-x1 x5-x1 x2 x3-x2 x4-x2 x5-x2 x3 x4-x3 x5-x3 x4 x5-x4 x5 Then I

There's something peculiar that I do not understand here. However, did you realize that the thing you are assigning into parts of `a' is NULL? Check you're my.test.boot.ci.1: It's NULL. Be that as it may, I get: > a <- data.frame(matrix(1:4, nrow=2), X3=NA, X4=NA) > a X1 X2 X3 X4 1 1 3 NA NA 2 2 4 NA NA > a[a$X1 == 1,]$X3 <- NULL > a X1 X2 X3 X4 1 1

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

Dear UseR I have a dataset, for instance x1(A, B, C), x2(M,F), x3(X1,X2,X3,X4) and x4(W,F,P). I want to make a small dataset with the random tuple. I know package e1071 can handle a random discrete, > library(e1071) > x1 <- rdiscrete(6, c(2,2,2), c("A","B","C")) > x2 <- rdiscrete(6, c(3,3), c("M","F")) > x3 <-

Hello, I found a potential problem in R 2.1.0 (and R 2.0.1) I expect that > tmp <- FUN(x1, x2, x3, x4) > as.data.frame(tmp) is the same as > as.data.frame(FUN(x1, x2, x3, x4)) since the tmp variable in this case is unnecessary. However, below I will demonstrate that under an odd set of conditions, I can correctly perform as.data.frame(tmp), but not as.data.frame(FUN(x1, x2, x3,

Hi All, I've been successfully using the with function for analyses and the transform function for multiple transformations. Then I thought, why not use "with" for both? I ran into problems & couldn't figure them out from help files or books. So I created a simplified version of what I'm doing: rm( list=ls() ) x1<-c(1,3,3) x2<-c(3,2,1) x3<-c(2,5,2)