similar to: predict.tree

Simple question: is it true that polygon (x, y, col=vec, border=vec, ...) will cycle through the vec colors for fills but not for the border colors when sending multiple polygons? thanks, Denis White US EPA, 200 SW 35th St, Corvallis, Oregon, 97333 USA voice: 541.754.4476, email: white.denis at epa.gov web: www.epa.gov/wed/pages/staff/white/

>> In the previous release of r, theire was a index of all functions in R. >> It was was i understand as reference. It was simple to search in this html >> page by function name or by keywords. >> Why, by god, this page is removed in the Veriosn 1.3 of R??? > Take a look at R_HOME/doc/html/function.html .... The browser-based search > engine was not on that page, but

I'm using R 1.4.1 under Windows 2000 and am experiencing difficulty installing a package. I've included an example output using Rcmd build, but the same problem occurs with Rcmd check and Rcmd INSTALL. The error (make[1]: /bin/sh.exe: Command not found) is not a new one - it's referenced in readme.packages among other locations. I've placed a copy of sh.exe in C:\bin, but it

Hello, I'm attempting to classify data using tree(). summary(tree()) indicates that I have a very good classification rate. What I'd like to know is which tokens in the data set are correctly classified and which are not. Is there a method for associating the classification with the token? I've been reading Chambers and Hastie (1992) chapter 9 on tree-based models, but find no

I've tried to predict the values from a new data.frame using the nls.predict function and keep getting the error message: Error in if (sum(wrong) == 1) stop(gettextf("variable '%s' was fitted with class \"%s\" but class \"%s\" was supplied", : missing value where TRUE/FALSE needed I first thought that it was becuase there may have been something

Dear R community, I have recently discovered the package oblique.tree and I must admit that it was a nice surprise for me, since I have actually made my own version of a kind of a classifier which uses the idea of oblique splits (splits by means of hyperplanes). So I am now interested in comparing these two classifiers. But what I do not seem to understand is why the function

Summer job opportunity using Splus/R in Corvallis, Oregon: Great opportunity to gain experience in application of survey designs to natural resources. Graduate level statistician/programmer competent in the development of algorithms using the statistical software SPlus or R to work as a summer hire as part of the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment

Dear R users I have a problem with predict.tree() when I want the fitted values of a tree object to be returned (i.e. argument 'newdata' is missing). Whatever I specify for argument 'type' the fitted model object is returned. Example: library(tree) data(kyphosis) fit.tree <- tree(Kyphosis ~ Age + Number + Start, data=kyphosis) p <- predict(fit.tree,

Dear community, I've a tree which included at first 23 variables. Then I've pruned this tree, and there are only 8 variables involved. I'd like to predict and only introduce in newdata the values of these 8 variables involved. However, as the tree was built with the 23, it asked me for 15 values, even if it doesn't need them. Is there a way to introduce only this 8 values?

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

I am writing a function to assess the out of sample predictive capabilities of a time series regression model. However lm.predict isn't behaving as I expect it to. What I am trying to do is give it a set of explanatory variables and have it give me a single predicted value using the lm fitted model. > model = lm(y~x) > newdata=matrix(1,1,6) > pred =

Maybe a useful addition to the predict functions would be to return the values of the predictor variables. It just (unless there are problems) requires an extra line. I have inserted an example below. "predict.glm" <- function (object, newdata = NULL, type = c("link", "response", "terms"), se.fit = FALSE,

I imitated predict.glm, my thing worked, now I need to revise. It would help me very much if someone would explain predict.glm line 28, which says object$na.action <- NULL # kill this for predict.lm calls I want to know 1) why does it set the object$na.action to NULL 2) what does the comment after mean? Maybe I need a pass by value lesson too, because I can't see how changing that

I'm adding a couple of vignettes to an existing package. When I make a change to the sweave file, and run the check command, c:\conifers\trunk>R CMD check rconifers I get the following message(s) in the 00check.log file: * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... WARNING Package vignette(s) without corresponding PDF:

I run the following code in R 1.6.2 on Windows: xxx <- rnorm(100) yyy <- .5 * rnorm(100) + sqrt(1-.5^2) * rnorm(100) ord <- order(xxx) xxx <- xxx[ord] # for yyy <- yyy[ord] # convenience in reading printout rm(ord) reg.gam <- gam(yyy ~ s(xxx, k=8)) f <- function(x, reg.gam, target.y) { cat("inside f() called by optimize():\n") cat("arg x=", x,

Many thanks for your very useful comments and suggestions. Renaud 2006/5/30, Prof Brian Ripley <ripley at stats.ox.ac.uk>: > On Tue, 30 May 2006, Prof Brian Ripley wrote: > > > This is not really a bug. See > > > > http://developer.r-project.org/model-fitting-functions.txt > > > > for how this is handled in other packages. All model-fitting in R used =

Dear all, I'd like to draw a color key beside a graph drawn with xyplot (lattice library). I am aware of the draw.colorkey function (grid library) but don't know how to handle it. Any hint would be appreciated. Renaud -- Dr Renaud Lancelot, v?t?rinaire CIRAD, D?partement Elevage et M?decine V?t?rinaire (CIRAD-Emvt) Programme Productions Animales

I am struggling to get GAM model predictions from the top models calculated using model.avg in the package "MuMIn". My model looks something like the following: gamp <- gam(log10(y)~s(x1,bs="tp",k=3)+s(x2,bs="tp",k=3)+ s(x3,bs="tp",k=3)+s(x4,bs="tp",k=3)+s(x5,bs="tp",k=3)+ s(x6,bs="tp",k=3)+x7,data=dat,

In one particular situation predict.coxph gives an error message. Namely: stratified data, predict='expected', new data, se=TRUE. I think I found the error but I'll leave that to you to decide. Thanks, Chris ######## CODE library(survival) set.seed(20121221) nn <- 10 # sample size in each group lambda0 <- 0.1 # event rate in group 0 lambda1 <- 0.2 # event rate in group 1

Hi, I split a data set into two partitions (80 and 42), use the first as the training set in glm and the second as testing set in glm predict. But when I call glm.predict, I get the warning message:  Warning message: 'newdata' had 42 rows but variable(s) found have 80 rows  ---------------------  s = sample(1:122)