similar to: recompute values repeatedly, or new file for glm()?

Once again I need to tap into the collective knowledge here. Let's say I have the following columns and data below Y X1 X2 X3 X4 I would like to generate additional new columns and column names (ie the data would be squared - and I'd like the column names to reflect this) like: Y X1 X2 X3 X4 X1^2 X2^2 X3^2 X4^2 I believe I can compute the values correctly with the code below, but I

Hi Folks, This tip is probably lurking somewhere already, but I've just discovered it the hard way, so it is probably worth passing on for the benefit of those who might otherwise hack their way along the same path. Say (for example) you want to do a logistic regression of a binary response Y on variables X1, X2, X3, X4: GLM <- glm(Y ~ X1 + X2 + X3 + X4) Say there are 1000 cases in the

Dette er en melding med flere deler i MIME-format. --=_alternative 004C4E4A00257091_= Content-Type: text/plain; charset="US-ASCII" Yes. so (x1*x2*x3*x4*x5*x6*x7*x8)^2 = (x1+x2+x3+x4+x5+x6+x7+x8)^8 ? and there is a difference in (x1*x2*x3*x4*x5*x6*x7*x8)^2 and (x1*x2*x3*x4*x5*x6*x7*x8) althoug the resulting formulas are the same, or? This fikses my problem, but R still crashes for the

On Wed, 5 Oct 2005 Hallgeir.Grinde at elkem.no wrote: > 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 <- runif(1000) > y <-

On Wed, 5 Oct 2005 Hallgeir.Grinde at elkem.no wrote: > Yes. > so (x1*x2*x3*x4*x5*x6*x7*x8)^2 = (x1+x2+x3+x4+x5+x6+x7+x8)^8 ? Yes in the sense that the simplified formula given by terms() is the same. > and there is a difference in > (x1*x2*x3*x4*x5*x6*x7*x8)^2 > and > (x1*x2*x3*x4*x5*x6*x7*x8) > althoug the resulting formulas are the same, or? The first is reduced to the

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

Dear all, I am an beginner with R and I have a question concerning apply(). My problem is as follows: I have data on four variables (x1,x2,x3,x4) with missing values for x1 and x2. > data[1:9,] x1 x2 x3 x4 1 NA NA 10 1 2 NA NA 8 3 3 NA NA 13 7 4 9 9 9 9 5 11 14 20 10 6 14 14 14 15 7 6 6 6 8 8 4 4 4 19 9 12 12 12 8 ..... I would like to replace the missing values

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 <-

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

Hello guys, i looked over the archive files and found nothing about this kind of error. I have a database of 33 elements described in 8 variables, i'm using the Leave-One-Out iterative process to take one of the elements to be the test element and make a regression with the other 32 and then I try to predict the clas of the element out. I'm using this call as a part of a Leave-One-Out

I posted this one as an R bug (https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=14767), but Prof. Ripley says I'm premature, and I should raise the question here. Here's the behavior I assert is a bug: The output from delete.response on a terms object alters the formula by removing the dependent variable. It removes the response from the "variables" attribute and it changes

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

Dear Friends: I like so much to work with R program. Congratulations for your work. I need R for work with multivariate data. My question is: With the pairs(X) command my output is a pairwise scatterplot symmetric matrix. Like: | X1 |X1 vs X2|X1 vs X3|X1 vs X4| |X2 vs X1| X2 |X2 vs X3|X2 vs X4| |X3 vs X1|X3 vs X2| X3 |X3 vs X4| |X4 vs X1|X4 vs X2|X4 vs X3| X4 | It is

> From: Peter Dalgaard > > Hallgeir.Grinde at elkem.no writes: > > > 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)

Hi, I wonder if anyone can help me with this issue. I am using R2BayesX. It seems that the model can maximally contain 20 interactions. When the number of interaction terms exceed 20, the code stops working. Here is a piece of toy code. rm(list=ls()) library(BayesX) library(R2BayesX) #data generating model f2<-function(x1,x2,x3,x4) { y<-2*sin(pi*x1)*1.5+exp(2*x2)/3+2 * sin(4 * pi * (x3

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 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 <-

I'm pasting below a working R file featuring a function I'd like to polish up. I'm teaching regression this semester and every time I come to something that is very difficult to explain in class, I try to simplify it by writing an R function (eventually into my package "rockchalk"). Students have a difficult time with predict and newdata objects, so right now I'm

Dear list, I am making a linear regression of the following format. lm(Y~X1+X2+log(X3)+log(X4)) Now I would like to check the linear regression above using generalized linear model. Please kindly if the following format is correct and thank you. (If it is wrong, please indicate why.) glm(Y~X1+X2+log(X3)+log(X4), family=poisson(link="log")) Also, please kindly suggest any references