similar to: Re: [R] Step function (PR#753)

On Wed, 29 Nov 2000, Matt Pocernich wrote: > I am having problem using the step function for a linear regression model. I've created an initial model containing only the intercept. Then using the step function, I've selected three variables to be considered for the model. > > > > x0.lm<- lm(MEDV~1, data = x) > > > > anova(x0.lm) > Analysis of

I am having problem using the step function for a linear regression model. I've created an initial model containing only the intercept. Then using the step function, I've selected three variables to be considered for the model. > x0.lm<- lm(MEDV~1, data = x) > > anova(x0.lm) Analysis of Variance Table Response: MEDV Df Sum Sq Mean Sq F value Pr(>F)

Dear all, I'm having some problems with using the step() function inside another function. I think it is an environment problem but I do not know how to overcome it. Any suggestions are appreciated. I've prepared a simple example to illustrate my problem: > library(MASS) > data(Boston) > my.fun <- function(dataset) { + l <- lm(medv ~ .,data=dataset) + final.l <-

Dear All, I have data which contains 14 variables. And I have to regress one of variables on each variable (simple 13 linear regressions) I try to make a loop and store only R-squared colnames(boston) [1] "CRIM" "ZN" "INDUS" "CHAS" "NOX" "RM" "AGE" [8] "DIS" "RAD"

Cubist is a rule-based machine learning model for regression. Parts of the Cubist model are described in: Quinlan. Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence (1992) pp. 343-348 Quinlan. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning

Cubist is a rule-based machine learning model for regression. Parts of the Cubist model are described in: Quinlan. Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence (1992) pp. 343-348 Quinlan. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning

According to the man page for formula, "a formula object has an associated environment". However, update.default doesn't use this environment, which creates problems like the following: make.model <- function(x) { lm(medv~.,x) } library(MASS) data(Boston) fit = make.model(Boston) fit = update(fit,".~.-crim") # Object "x" not found Here is a

FWIW, I wrote a little function to extract variable importance as defined in the CART book a while ago. It's rather limited: Only works for regression problem, and you need to set maxsurrogate=0 and maxcompete=0. It may (or may not) help you: varimp.rpart <- function(x) { dev <- x$frame[, c("var", "dev")] dev <- dev[dev$var != "<leaf>",

Dear List Members, I have encountered two problems when using the step function to select models. To better illustrate the problems, an R image (step.add1.test.RData) which includes the objects needed to run the code (step.add1.test.R) can be found at www.biostat.wisc.edu/~pwang/r-help/<http://www.biostat.wisc.edu/%7Epwang/r-help/> lm.data.frame have factor variables with 3 levels. The

Dear List Members, I have encountered two problems when using the step function to select models. To better illustrate the problems, attached is an R image which includes the objects needed to run the code attached. lm.data.frame have factor variables with 3 levels. The following run shows the first problem. AICs (* and **) are different. I noticed that the Df for rs13482096:rs13483699 is 4,

? data=read.table("http://statcourse.com/research/boston.csv", , sep=",", header = TRUE) ? library(rpart) ? fit=rpart (MV~ CRIM+ZN+INDUS+CHAS+NOX+RM+AGE+DIS+RAD+TAX+ PT+B+LSTAT) Please: Show me the tree. Mark -------- Original Message -------- Subject: Re: [R] help with rpart From: "Stephen Milborrow" <[1]milbo at sonic.net>

Dear R'ers, when I e.g. install the integrate2.2-2 package under R1.1.1 (debian2.2) atlas is linked into the library: Installing package `integrate' ... libs gcc -shared -lc -shared -o /usr/lib/R/library/integrate/libs/integrate.so adapt.o adapt_callback.o bsrl.o flfm.o nxprt.o symrl.o wht.o -L/usr/local/lib -lf77blas -lcblas -latlas -lg2c -lm -L/usr/lib/gcc-lib/i386-linux/2.95.2

dear R experts: ?I am writing my own little newey-west standard error function, with heteroskedasticity and arbitrary x period autocorrelation corrections. ?including my function in this post here may help others searching for something similar. it is working quite well, except on occasion, it complains that Error in solve.default(crossprod(x.na.omitted, x.na.omitted)) : system is

thank you, achim. I will try chol2inv. sandwich is a very nice package, but let me make some short suggestions. I am not a good econometrician, so I do not know what prewhitening is, and the vignette did not explain it. "?coeftest" did not work after I loaded the library. automatic bandwidth selection can be a good thing, but is not always. as to my own little function, I like the

I'm using the package geepack to fit GEE models. Does anyone know of methods for add1 and drop1 for a 'geeglm' model object, or perhaps a method for extractAIC based on the QIC of Pan 2001? I see there has been some mention of this on R-help a few years ago (RSiteSearch("QIC")). The package does provide an anova method for its model objects, and update() seems to work:

>?step .... 'step' uses 'add1' and 'drop1' repeatedly; it will work for any method for which they work, and that is determined by having a valid method for 'extractAIC'. When the additive constant can be chosen so that AIC is equal to Mallows' Cp, this is done and the tables are labelled appropriately. so my question is :what constant

Hello, I'm currently dealing with a rather strange problem when using the function "stepAIC" ("MASS" package). The setting is the following: From model learning data sets ("learndata"), I want to be able to build prediction functions (in order to save them in a file for further use). This is done by the function "pred.function" (see below). Therein,

> >  Dear moderators, Please help me encode the program instructed by follows. Thank u! Apply the methods introduced in Sections 4.2.1 and 4.2.2, say the > rank-based variable selection and BIC criterions, to the Boston housing > data. >  The Boston housing data contains 506 observations, and is publicly available in the R package mlbench (dataset “BostonHousing”).  The

David, Thanks. The reason add1.lm (and drop1.lm) do not support offsets is that lm did not when they were written, and the person who added offsets to lm did not change them. (I do wish they had not added an offset arg and just used the formula as in S's glm.) That is easy to add. For the other point, some care is needed if 'x' is supplied and the upper scope reduces the number

I am using R 2.1.1 under Mac OS 10.3.9. Two related problems (see notes 1. and 2. below) are illustrated by results of the following: y <- rnorm(10) x <- z <- 1:10 is.na(x[9]) <- TRUE lm0 <- lm(y ~ 1) lm1 <- lm(y ~ 1, weights = rep(1, 10)) add1(lm0, scope = ~ x) ## works ok add1(lm1, scope = ~ x) ## error lm2 <- lm(y ~ 1, offset = 1:10) add1(lm0, scope = ~ z) ##