similar to: There might be something wrong with cv.lm(DAAG)

Dear R-list, I'm not sure what I've found about a function in DAAG package is a bug. When I was using cv.lm(DAAG) , I found there might be something wrong with it. The problem is that we can't use it to deal with a linear model with more than one predictor variable. But the usage documentation hasn't informed us about this. The code illustrates my discovery: > library(DAAG)

Dear all, I've tried a multiple regression, and now I want to try a cross-validation. I obtain this error (it must be sth related to df) that I don't understand, any help would be appreciated. cv.lm(df= dat, lm2.52f, m=3) Error en `[.data.frame`(df, , ynam) : undefined columns selected lm2.52f is my lm object, dat is a dataframe where the variables involved in .lm are I tried CVlm

Dear R-list, I apologize for my many emails but I think I know how to desctribe my problem differently and more clearly. My question is how to compare linear models with intercept and those without intercept using maximizing adjusted R^2 strategy. Now I do it like the following: > library(leaps) > n=20 > x=matrix(rnorm(n*3),ncol=3) > b=c(1,2,0) > intercept=1 >

Dear R users, I'm a newbie for R and want to ask some basic questions. So, after I open the R software, I typed library(DAAG). Then, I get massive warning messages as shown below. Why does it happen? Also, here are few specific questions regarding each message. 1) Loading required package: MASS -> Does this mean that the MASS package is not included in DAAG? 2) Attaching package:

I use R on my Ubuntu 9.04 laptop, which was installed by "aptitude install" way Now i'm learning a book of R which needs "MASS" and "DAAG" installed. So i followed the instructions: >install.packages("MASS") >library("MASS") MASS works fine. But DAAG doesn't. Anyone who could help would be appreciated a lot ! Below are my error

I use R on my Ubuntu 9.04 laptop, which was installed by "aptitude install" way Now i'm learning a book of R which needs "MASS" and "DAAG" installed. So i followed the instructions: >install.packages("MASS") >library("MASS") MASS works fine. But DAAG doesn't. Anyone who could help would be appreciated a lot ! Below are my error

Dear R-list, I am sorry for my shortage of stat knowlege. I want know how to conduct a hypothesis test : Ho:|E(X)|=|E(Y)|<->H1:otherwise. Actually, in my study, X and Y is two observations of bias, where bias=u^hat-u, u is a parameter I concerned. Given X=(u^hat_xi - u) and Y=(u^hat_yi - u), I want to know which bias is smaller, or the absolute expection of which is smaller. Due to limit

Hello all, This shouldn't be difficult, but I am not able to extract a printed value from a function and assign it to an object. In my case, > library(DAAG) > twotPermutation(c(2,3,4),c(3,6,5),plotit=F) [1] 0.298 I would like to assign this result to an object. Thanks, Duarte

Can anyone help explain to me why the two codes below have different result? I thought I can use log(time)~. to replace log(time)~dist+climb+timef.I am using CVlm from DAAG package. I think nihills is preloaded with the package. Thanks in advance. > CVlm(df=nihills, form.lm=formula(log(time)~.),plotit="Observed",m=2)Analysis of Variance Table Response: log(time) Df Sum Sq

Hie I am trying to install Package DAAG this is the error I get > library(DAAG) Loading required package: randomForest Error: package 'randomForest' could not be loaded In addition: Warning message: In library(pkg, character.only = TRUE, logical.return = TRUE, lib.loc = lib.loc) : there is no package called 'randomForest' what should i do thanks The University of Fort Hare

Hi, everybody, 3 questions about R-square: ---------(1)----------- Does R2 always increase as variables are added? ---------(2)----------- Does R2 always greater than 1? ---------(3)----------- How is R2 in summary(lm(y~x-1))$r.squared calculated? It is different from (r.square=sum((y.hat-mean (y))^2)/sum((y-mean(y))^2)) I will illustrate these problems by the following codes:

Hi, all When I am using mle.cv(wle), I find a interesting problem: I can't do leave-one-out cross-validation with mle.cv(wle). I will illustrate the problem as following: > xx=matrix(rnorm(20*3),ncol=3) > bb=c(1,2,0) > yy=xx%*%bb+rnorm(20,0,0.001)+0 > summary(mle.cv(yy~xx,split=nrow(xx)-1,monte.carlo=2*nrow(xx),verbose=T), num.max=1)[[1]] mle.cv: dimension of the split subsample

<< repost because previous attempt was not plain text, sorry! >> Hi Folks, I have a pretty simple problem: after building a multivariate linear model, I need to report my 95% confidence interval for predictions based on future observations. I know that one option is to use predict(interval="prediction") but I'm curious about less parametric ways to get an estimate. I

Philipp Benner reported a Debian bug report against r-cran-rpart aka rpart. In short, the issue has to do with how rpart evaluates a formula and supporting arguments, in particular 'weights'. A simple contrived example is ----------------------------------------------------------------------------- library(rpart) ## using data from help(rpart), set up simple example myformula <-

Dear All, I have a question about formulas and model.matrix(). If one specifies a model via a formula, the corresponding design matrix can be obtained with the model.matrix() function. For example: x1 <- c(1,4,2,3,5) x2 <- c(1,1,2,2,2) myformula <- ~ x1 + factor(x2) model.matrix(myformula) My question is: If an intercept term is in the model (like in the example above), is it always

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata) mytest

Hi, I am passing a data frame and field name to a function. I've figured out how I can create the formula based on the passed in field name, but I'm struggling to create a vector based in that field. for example if I hard code with the actual field name Y = df$Target, everything works fine. but if I use the passed in parameter name, it doesn't give me what I want, Y = df$mytarget

Hi Bert, On 2023-07-08 3:42 p.m., Bert Gunter wrote: > Caution: This email may have originated from outside the organization. Please exercise additional caution with any links and attachments. > > > Thanks John. > > ?boxcox says: > > ************************* > Arguments > > object > > a formula or fitted model object. Currently only lm and aov objects

Thanks John. ?boxcox says: ************************* Arguments object a formula or fitted model object. Currently only lm and aov objects are handled. ************************* I read that as saying that boxcox(lm(z+1 ~ 1),...) should run without error. But it didn't. And perhaps here's why: BoxCoxLambda <- function(z){ b <- MASS:::boxcox.lm(lm(z+1 ~ 1), lambda = seq(-5, 5,

Hi, I am passing a data frame and field name to a function. I've figured out how I can create the formula based on the passed in field name, but I'm struggling to create a vector based in that field. for example if I hard code with the actual field name Y = df$Target, everything works fine. but if I use the passed in parameter name, it doesn't give me what I want, Y =