search for: crims

Dear all I am trying to fit Variable Coefficients Models on Unbalanced Panel Data. I managed to fit such models on balanced panel data (the example from the "plm" vignette), but I failed to do so on my real, unbalanced panel data. I can reproduce the error on a modified example from the vignette: > require(plm) > data("Hedonic") > Hed <- pvcm(mv ~ crim + zn + indus

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 <-

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

hi I am using Solaris 8 and installing the 3.8 openssh software that I received from www.sunfreeware.com I following the installation instruction see attachment but when I get to ssh-keygen -t rsa1 -f /usr/local/etc/ssh_host_key -N "" I get a PRNG is not seeded could you help me in this matter. Thank you for your cooperation. Spencer Crim

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

? 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 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"

...at* part i've never really gotten over. > Mark Well the Aussie's recently announced an additional travel warning for The Netherlands due to the increased level of petty crime although I feel it was a little extreme. The petty crime problem is very much specific to Amsterdam and foreign crims come into the city specifically to target tourists and their valuables. I've lived out here for 3 years now and enjoy exceptional safety where I live in Haarlem so perhaps an alternative major city such as Haarlem or Den Haag might be an option ? Hmmm... what size was that T shirt ? (c; ***...

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)

This question concerns rpart's facility for user-defined functions that accomplish splitting. I was interested in modifying the code so that in each terminal node, a linear regression is fit to the data. It seems that from the allowable inputs in the user-defined functions, that this may not be possible, since they have the form: function(y, wt, parms) (in the case of the

? 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) predict(fit,data[4,]) plot only reveals part of the tree in contrast to the results on obtains with CART or C5 -------- Original Message -------- Subject: Re: [R] help with rpart From: Sarah

I have been running into difficulties with dispatching on an S4 class defined in the SparseM package, when the method calls are inside a function passed as the f= argument to optimize() in functions in the spdep package. The S4 methods are typically defined as: setMethod("det","matrix.csr", function(x, ...) det(chol(x))^2) that is within setMethod() rather than by name before

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 R-users, Recently, I am facing some problems when converting mlbench data into matrix format. library(mlbench) data(BostonHousing) X<- BostonHousing[,1:13] y<-BostonHousing[,14] I want to convert X and y into matrix form. I am getting these obvious errors... > t(X)%*%y Error in t(X) %*% y : requires numeric/complex matrix/vector arguments > t(as.matrix(X))%*%(as.matrix(y))

...At 16:46 1-12-2003 +0100, you wrote: >Well the Aussie's recently announced an additional travel warning for The >Netherlands due to the increased level of petty crime although I feel it >was a little extreme. The petty crime problem is very much specific to >Amsterdam and foreign crims come into the city specifically to target >tourists and their valuables. > >I've lived out here for 3 years now and enjoy exceptional safety where I >live in Haarlem so perhaps an alternative major city such as Haarlem or >Den Haag might be an option ? How about Enschede ? ;-...

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

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

Hi r-help, I use lavaan:sem() for structural equation modelling with latent variables. Below is a reproducible example (the code requires a working installation of lavaan) where the latent variable criminality is in focus. Besides criminality in general, I am specifically interested one of the manifest variables that make up the latent variable criminality, namely fire.setting. My question is:

All, Recently I ran across a URL documenting published research using R: http://www.cgd.ucar.edu/ccr/ammann/millennium/CODES_MBH.html A note on the site indicates that the code is being revised. The code and data are provided, so that one could reproduce the results without having to buy a proprietary software program. In poking around the R website it is clear that a lot of thought has gone