similar to: delcol() of Splus

Hello, I'd like to propose an extension to the function summary.aov. In Splus (2000, I don't know about other versions), summary.aov allows a parameter ssType to be set to 1 or 3 (defaults to 1) to choose the type of Sums of Squares. I know I can get Type III SS in R with drop1(model), but including the functionality into summary.aov would, in my opinion, - yield a more usable table

Dear all, I met a problem when installing new packages on R, my system is linux fedora 6.0, the following is output. please help me. Thanks. > install.packages('lars') --- Please select a CRAN mirror for use in this session --- Loading Tcl/Tk interface ... done trying URL 'http://www.stathy.com/cran/src/contrib/lars_0.9-5.tar.gz' Content type 'application/x-tar' length

Dear R users, I'm trying to use rpart() to build a classification tree on a big dataset. The number of samples is n=100 and the number of variables is p=10000. At first I stored all the data in a data.frame and got a "stack overflow" error; then I changed the data into a matrix and the problem disappeared. Now the trouble is when I try to use the predict() function, since each

the data is : >table.8.3<-data.frame(expand.grid( marijuana=factor(c("Yes","No"),levels=c("No","Yes")), cigarette=factor(c("Yes","No"),levels=c("No","Yes")), alcohol=factor(c("Yes","No"),levels=c("No","Yes"))), count=c(911,538,44,456,3,43,2,279))

I am using R to teach, and here are a couple of things that I thought would work didn't work. 1. I noticed the utility data(***,package=***) recently and like it very much, but unless I type in the whole word "package" I'll get an error in 1.4.0. For example, data(cats,package=MASS) works fine but data(cats,pac=MASS) doesn't. 2. drop1 doesn't seem to be as smart as

I would like to do a SS type III analysis on a proportional odds logistic regression model. I use drop1(), but dropterm() shows the same behaviour. It works as expected for regular main effects models, however when the model includes an interaction effect it seems to have problems with matching the parameters to the predictor terms. An example: library("MASS"); options(contrasts =

Hi, I was wondering why anova() and drop1() give different tail probabilities for F tests. I guess overdispersion is calculated differently in the following example, but why? Thanks for any advice, Tom For example: > x<-c(2,3,4,5,6) > y<-c(0,1,0,0,1) > b1<-glm(y~x,binomial) > b2<-glm(y~1,binomial) > drop1(b1,test="F") Single term deletions Model: y ~

Dear List, recently tried to reproduce the results of some custom model selection function after updating R, which unfortunately failed. However, I ultimately found the issue to be that testing with pchisq() in drop1() seems to have changed. In the below example, earlier versions (e.g. R 2.4.1) produce a missing P-value for the variable x, while newer versions (e.g. R 2.7.1) produce 0 (2.2e-16).

By changing three lines in drop1 from access based on $ to access based on standard accessor methods (terms() and residuals()), it becomes *much* easier to extend drop1 to work with other model types. The use of $ rather than accessors in this context seems to be an oversight rather than a design decision, but maybe someone knows better ... In particular, if one makes these changes (which I am

Dear R Core Team I've a proposal to improve drop1(). The function should change the contrast from the default ("treatment") to "sum". If you fit a model with an interaction (which ist not signifikant) and you display the main effect with drop1( , scope = .~., test = "F") If you remove the interaction, then everything's okay. There is no way to fit a

Hi, If I used drop1 in a weighted lm fit, it seems to ignore the weights in the AIC calculation of the dropped terms, see the example below. Can this be right? Yan -------------------- library(car) > unweighted.model <- lm(trSex ~ (river+length +depth)^2- length:depth, dno2) > Anova(unweighted.model) Anova Table (Type II tests) Response: trSex Sum Sq Df F value

Hi all, I've spent quite a lot of time searching through the help lists and reading about how best to run perform a 2-way ANOVA with unbalanced data. I realize this has been covered a great deal so I was trying to avoid adding yet another entry to the long list considering the use of different SS, etc. Unfortunately, I have come to the point where I feel I have to wade in and see if someone

This is probably simple, but I just can't see it... I want to calculate the R^2s for a series of linear models where each term is dropped in turn. I can get the RSS from drop1(), and the r.squared from summary() for a given model, but don't know how to use the result of drop1() to get the r.squared for each model with one term dropped. Working example: library(vcd) # for

Hello All, Am having some trouble computing Type III SS in a Cox Regression using either drop1 or Anova from the car package. Am hoping that people will take a look to see if they can tell what's going on. Here is my R code: cox3grp <- subset(survData, Treatment %in% c("DC", "DA", "DO"), c("PTNO", "Treatment", "PFS_CENSORED",

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

hi chaps: a simple suggestion: R tells me who the contributors() are, but this should also tell me where I should mail suggestions to. Is it this mailing list? a repository of suggestions? an individual? this came up because i wanted to suggest two small enhancements: the first is for the summary() method for plain data frames. it would seem to me that the number of "NA"s

A recent question in r-help made me realize that I should add a drop1 method for coxph and survreg. The default does not handle strata() or cluster() properly. However, for coxph the right options for the "test" argument would be likelihood-ratio, score, and Wald; not chisq and F. All of them reference a chi-square distribution. My thought is use these arguments, and add an

Hello all, I have a question on manova in R: I'm using the function "manova()" from the stats package. Is there anything like a stepwise (backward or forward) manova in R (like there is for regression and anova). When I enter: step(Model1, data=Mydata) R returns the message: Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, : no 'drop1'

Yes, it's homework . . . delete now if desired . . . but I think it is an interesting problem. Running R 2.11.1, LaTeX on WinXP, via Sweave. A drop1() object from a glm() produces, as part of its output, a string that looks like this: <none> The trouble I run into is that running latex() on a drop1() object from glm() produces a string that looks like this in the generated .tex

I downloaded and installed dovecot 1.1 nightly build yesterday. Here is a the errors I collected from the log file. dovecot: Aug 15 14:19:29 Error: IMAP(use at doamin): Log synchronization error at seq=7,offset=24 for /data2/mail/we/webmail.us/ji/jing/dovecot.index: Extension introduction for unknown id 2 dovecot: Aug 15 14:19:29 Error: IMAP(use at doamin): Log synchronization error at