similar to: followup: Re: Issue with predict() for glm models

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Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

Dear All, I am not going to discuss in detail the implementation of a model, but I am rather puzzled because, even if I manage to train a model, then I do not succeed in applying it to some test data. I am sure I am making some trivial mistake, but so far I have been banging my head against the floor. When I run the following code ###############################################

Hi, I would like to simulate data with a binary outcome and a set of predictors that are correlated. I want to be able to fix the number of event (Y=1) vs. non-event (Y=0). Thus, I fix this and then simulate the predictors. I have 2 questions: 1. When the predictors are continuous, I can use mvrnorm(). However, if I have continuous, ordinal and binary predictors, I'm not sure how to simulate

1) dmatrix1<-function(n,p,rho,sigma,k){ muvec1=zeros(1,p) truep<-as.matrix(c(3,1.5,0,0,2,0,0,0)) A=eye(p) for(i in 1:p){ for(j in 1:p){ A[i,j]=rho^(abs(i-j)) X=mvrnorm(n,muvec1,A) y=X%*%truep+as.matrix(rnorm(n,0,sigma)) Y=X[1:k,] w=y[1:k] Z=X[(k+1):n,] z=y[(k+1):n]}} return(list(X,Y,w,Z,z)) } I made this code which performs

I want to use the grplasso package on a data set where I want to fit a linear model.? My interest is in identifying significant?beta coefficients.? The documentation is a bit cryptic so I'd appreciate some help. ? I know this is a strategy for large numbers of variables but consider a simple case for pedagogical puposes.? Say I have?two 3 category predictors (2 dummies each), a binary

All, For my understanding, I wanted to see if I can get glmnet predictions using both the predict function and also by multiplying coefficients by the variable matrix. This is not worked out. Could anyone suggest where I am going wrong? I understand that I may not have the mean/intercept correct, but the scaling is also off, which suggests a bigger mistake. Thanks for your help. Juliet Hannah

Dear all, I tried a simple naive Bayes classification on an artificial dataset, but I have troubles getting the predict function to work with the type="class" specification. With type= "raw", it works perfectly, but with type="class" I get following error : Error in as.vector(x, mode) : invalid 'mode' argument Data : mixture.train is a training set with 100

I have a classification tree model similar to the following (slightly simplified here): > treemod<-tree(y~x) where y is a factor and x is a matrix of numeric predictors. They have dimensions: > length(y) [1] 1163 > dim(x) [1] 1163 75 I?ve evaluated the tree model and am happy with the fit. I also have a matrix of cases that I want to use the tree model to classify. Call it

Dear list, I am trying to use the 'mvrnorm' function from the MASS package for simulating multivariate Gaussian data with given covariance matrix. The diagonal elements of my covariance matrix should be the same, i.e., all variables have the same marginal variance. Also all correlations between all pair of variables should be identical, but could be any value in [-1,1]. The problem I am

Hi, You'd use an IRBuilder, like you did to create your LoadInsts. IRBuilder<> IRB(BB); IRB.CreateGEP(myreg1, myreg2); I assume because you asked this question, something went wrong when using the above method. What was it? :) Cheers, James ________________________________________ From: llvmdev-bounces at cs.uiuc.edu [llvmdev-bounces at cs.uiuc.edu] On Behalf Of Eduardo [erocha.ssa

Dear R-users: How does one calculate in R the odds ratios for a CATEGORICAL predictor variable that has 4 levels. I see r-help inquiries regarding odds ratios for what looked like a continuous predictor variable. I was wondering how to get the pairwise odds ratios for comparisons of levels of a categorical predictor variable. I can't seem to get the correct output using: >

Dear users, I'll appreciate your help with this (hopefully) simple problem. I have a model object which was fitted to inputs X1, X2, X3. Now, I'd like to use this object to make predictions on a new data set where only X1 and X2 are available (just use the estimated coefficients for these variables in making predictions and ignoring the coefficient on X3). Here's my attempt but, of

Hi all, How can I create an llvm::GetElementPtrInst in which the pointer and the index are in registers previously loaded with llvm::LoadInst ? I mean, the generated instruction will be like this: %1 = getelementptr i8* %myreg1, i32 %myreg2 here, %myreg1 and %myreg2 are previously created by load instructions (llvm::LoadInst). Please, let me know if there is an example of something similar.

I'm trying to do LASSO in R with the package glmpath. However, I'm not sure if I am using the accompanying prediction function *predict.glmpath()* correctly. Suppose I fit some regularized binomial regression model like so: library(glmpath);load(heart.data);attach(heart.data); fit <- glmpath(x, y, family=binomial) Then I can use predict.glmpath() to estimate the value of the

I'd like to compare two models which were fitted using gls, however I'm having trouble interpreting the results of gls. If any of you could offer me some advice, I'd greatly appreciate it. Short explanation of models: These two models have the same fixed-effects structure (two independent, linear effects), and differ only in that the second model includes a corExp structure for

Hi James, Thanks for your quick reply. > I assume because you asked this question, something went wrong when using the above method. What was it? :) No, I am not using this method. I was trying to create a llvm::GetElementPtrInst . I didn't create IRBuilder. I am writing a ModulePass that insert new instruction to an existing Module. Besides, how can you get a reference to myreg1 ?

I want to generate multiple multivariate normal samples with different mean vectors and common covariance matrix. I can do this with a loop, but can't quite figure out how to do it with apply and friends. In the example below, I want values to have 3 columns: group, x, y # number of groups, and group means x <- jitter(seq(2,10,by=2)) y <- x + rnorm(length(x), 0, .5) means <-

Dear list, I tried to apply the logistic regression to different response variables from a dataframe and would like to store the results using the function logistic.display from the "epicalc" package in a list, but got an error message "Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1". All the response variables have value of 0 or 1. It worked

¡Hola a todos! Estoy intentando muestrear de una normal multivariante donde hay dos grupos de variables que deben tener una relación "manipulable" entre sí pero ignoro cómo hacerlo. Les cuento, he intentado lo siguiente: # covarianzas del primer grupo de variables: Sigma_U <- matrix(c(.25, .2, .2, .25), ncol=2) # covarianzas del segundo grupo de variables: Sigma_W <- diag(2) #