similar to: Ooops, apologies, it was my error re predict with subset

Hello running R 1.7.1 on Windows 2000 I have a model notmar1 <- glm(yprisx~age+harddrug+sex, subset = marcom == 0, family = quasipoisson) and summary(notmar1) gives (as it should) 433 df for the null model but when I run predict(notmar1 <- glm(yprisx~age+harddrug+sex, subset = marcom == 0, family = quasipoisson)) I get preditions for 528 people (the full data set, not the subset)

Hello Using R 2.7.0 on Windows. I am running a linear discriminant analysis as follows <<<< discrim1 <- lda(normvar~ mafmahal+ mrfmahal+ mffmahal+ bafmahal+ brfmahal+ cofmahal+ bmfmahal+ cfmahal+ fractmahal+ antmahal+ absmifmahal+ absifmahal, subset = train) prediction <- predict(discrim1, traintest1[-train,])$class >>> When I do this, I get a warning

Hmmm-- still looks like a bug to me! But as I don't want to hog the airwaves, here's my last summary on this point, with a question: [Bravington] #> Prediction from the original data was just an example, of course; my general #> proposal is that inactive factor levels in the prediction set should be #> dropped. I don't see how this could ever cause inconsistent behaviour

Good afternoon Running R2.10.0 on Windows I have a data frame that includes (among much else) a factor (In_2006) and a continuous variable (math_3_4). I would like to find the 2 cases for In_2006 = 0 that are closest to each case where In_2006 = 1. My data looks like In_2006 math_3_4 0 55.1 1 51.6 1 18.1 1 26.6 1 14.1

I am working with a non-parametic smoothing operation using a Generalized Additive Model. It is a bivariate data set. I know how to do the smooth, and out comes a nice smooth curve. Now I want to find the value of the smoothed curve for several values of x (the abscissa). This can be done (please correct me if I am wrong) by using the predict.gam function. You feed the predict.gam function a

Full_Name: Vaidotas Zemlys Version: 2.9.0 OS: Ubuntu 8.10 Submission from: (NULL) (213.197.173.50) Steps to reproduce the bug: library(nlme) data(Oxboys) qm=lmList(height~age|Subject,data=Oxboys) grid=with(Oxboys,expand.grid(age=seq(min(age),max(age),length=50),Subject=levels(Subject))) res <- predict(qm,grid,se=TRUE) Erreur dans if (pool) { : l'argument est de longueur nulle res <-

I've created an xyplot that I want to add a trend line to using a subset of the data. The xyplot is xyplot(X9444500~WY,data=mynewdata,xlim=c(1900,2020),ylab="TEST", xlab="",ylim=c(100,100000),scales=list(x=list(at=c(1900,1920,1940,1960,1980,2000,2020),axs="r",tck=-1),y=list(log=TRUE,tck=-100,at=c(100,1000,10000,100000))), panel=function(x,y,...) {

Hello I have a dataframe that looks like this MSA CITY HIVEST YEAR YR CAT 1 0200 Albuquerque 0.50 1996 1996 5 2 0520 Atlanta 13.00 1997 1997 5 3 0720 Baltimore 29.10 1994 1994 1 4 0720 Baltimore 13.00 1995 1995 5 5 0720 Baltimore 3.68

Hi, I know this is probably a basic question... But I don't seem to find the answer. I'm fitting a GLM with a Poisson family, and then tried to get a look at the predictions, however the offset does seem to be taken into consideration: model_glm=glm(cases~rhs(data$year,2003)+lhs(data$year,2003), offset=(log(population)), data=data, subset=28:36, family=poisson()) predict

predict.warn() -- a function to display factor levels in new data for linear model prediction that do not exist in the estimating data. Date: 2005-8-24 From: John C. Nash (with thanks to Uwe Ligges for suggestions) nashjc at uottawa.ca Motivation: In computing predictions from a linear model using factors, it is possible to introduce new factor levels. This was encountered on a practical

Thanks for the reproducable example. I can confirm that it fails on my machine using survival 2-37.5, the next soon-to-be-released version, The issue is with NextMethod, and my assumption that the called routine inherited everything from the parent, including the environment chain. A simple test this AM showed me that the assumption is false. It might have been true for Splus. Working this

Dear list, I have encountered a problem with predict.nls (Windows XP, R.2.5.0), but I am not sure if it is a bug... On the nls man page, an example is: DNase1 <- subset(DNase, Run == 1) fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)), data = DNase1, start = list(xmid = 0, scal = 1)) alg = "plinear", trace =

Dear R-helpers, let us assume, that I have the following dataset: a <- rnbinom(200, 1, 0.5) b <- (1:200) c <- (30:229) d <- rep(c("q", "r", "s", "t"), rep(50,4)) data_frame <- data.frame(a,b,c,d) In a first step I run a glm.nb (full code is given at the end of this mail) and want to predict my response variable a. In a second step, I would

In modelling functions some people like to use a weight of 0 to drop an observation instead of using a subset value of FALSE. E.g., weights=c(0,1,1,...) instead of subset=c(FALSE, TRUE, TRUE, ...) to drop the first observation. lm() and summary.lm() appear to treat these in the same way, decrementing the number of degrees of freedom for each dropped observation. However, predict.lm() does

Hi, I use the lda function from the MASS package to classify some samples according to some chemical properties. If I run lda without cross validation all is ok but, if I run lda with cross validation, the R consol say: resLDA <- ldaRedOx <- lda(Activity ~ TRedOx[,1:6], CV=TRUE, data=dfDataRedOx, subset=train) predLDA <- predict(resLDA, newdata=dfDataRedOx[-train,])$class >

I have a data.frame with a series of variables tagged to a binary response ('present'/'absent'). I am trying to use randomForest to predict present/absent in a second dataset. After a lot a fiddling (using two data frames, making sure data types are the same, lots of testing with data that works such as data(iris)) I've settled on combining all my data into one data.frame

Thanks for your reply Mark, but no, using predict on the new data.frame does not help here. ? I had first thought that the probelm was due?the?explanatory variable (age)?and?the offset one (date) being?very similar (highly?correlated, I am trying to tease their effect apart, and hoped offset would help in this since I know the relationship with age already). But this appears not to be the case.

Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of the problem I'm

> From: Ross Darnell > > Liaw, Andy wrote: > >>From: Liaw, Andy > >> > >> > >>>From: Ross Darnell > >>> > >>>A good point but what is the value of storing a large set of > >>>predicted > >>>values when the values of the explanatory variables are lost > >>>(predicted >

Hello everyone. I am doing a logistic gam (package mgcv) on a pretty large dataframe (130.000 cases with 100 variables). Because of that, the gam is fitted on a random subset of 10000. Now when I want to predict the values for the rest of the data, I get the following error: > gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1, +