similar to: A suggestion for predict function(s)

I have fitted a logistic regression model > failed.lr2$call lrm(formula = failed ~ Age + task2 + Age:task2, data = time.long, na.action = na.omit) using the Design package functions and would like to generate a nomogram from this model. the datadist information is generated and stored in > ddist time.long$Age time.long$task2 Low:effect 45

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 am using the Windows menu functions below which will work on the first pass, but if I repeat the same script I cannot get the WinMenuAddItem to work. This is a problem if I change the menu structure and reread the source code I am forced to quit and restart Rgui. "try.menu" <- function(){ OS <- .Platform$OS.type GUI <- .Platform$GUI if (!(OS == "windows" &

Is it possible to produce a histogram directly using the hist() function with the common borders removed? It can be done by plotting the histogram object using type 's'teps. my.hist <- hist(x,plot=FALSE) plot(my.hist$breaks,c(0,my.hist$counts),type='s') I would appreciate help Ross Darnell -- University of Queensland, Brisbane QLD 4067 AUSTRALIA Email: <r.darnell at

Hello, everybody. I recognize I'm asking you to deal with a not-very-important problem. But its important to me :) I've noticed a little inconsistency in the print.function() output. I traced the matter to deparse.c, for which I attach a patch that addresses 3 separate things. There's one important thing that I'm willing to stand behind and 2 litter things that I think would be

Dear R community: it is possible to replace NA?s in a data frame with zeroes? what should I do? Thanks in advance Juan Pablo _________________________________________________________________ MSN Photos es la manera m?s sencilla de compartir e imprimir sus fotos: http://photos.latam.msn.com/Support/WorldWide.aspx -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

I would like to predict a matrix containing missing values according to a fitted linear model. The predicted values must have the same length as the number of observations in newdata, where missing predicted values (due to missing explanatory values) are replaced by NA. How can I achieve this? I tried the following example: > x <- matrix(rnorm(100), ncol=10) > beta <- rep(1, 10) >

I would like to define the order of the levels of a factor. The relevel function would work but since I have 20 levels I would prefer to declare the order explicitly. Using a smaller example levels(oldfactor) "b1" "b2" "r1" "r2" nufactor <- order(oldfactor,order=c("b1","r1","b2","r2")) # my fabricated function

x <- NA na.act <- na.action(na.exclude(x)) y <- rep(0,0) naresid(na.act,y) ... currently produces the result... numeric(0) ... whereas the documentation might lead you to expect NA The behaviour is caused by the line if (length(x) == 0L) return(x) in `stats:::naresid.exclude'. Removing this line results in the behaviour I'd expected in the above example (and in a

I would like to create a page of two graphs (2 rows by 1 col) and then draw vertical lines (segments?) on both graphs from the minimum values to the corresponding maximum value. So I have tried # > y <- rnorm(3000) > par(mfrow=c(2,1)) > plot(y,type="l") > plot(cumsum(y),type="l") > segments(1000,min(cumsum(y)),1000,max(cumsum(y))) > par(mfg=c(1,1)) >

I'm wondering whether anyone has any insight into why the 'simulate' methods for the built-in glm() families (binomial, Poisson, Gamma ...) extract the prior weights using object$prior.weights rather than weights(object,"prior") ? At first I thought this was so that things work correctly when e.g. subset= and na.action=na.exclude are used. However, the current versions of

Hello all, I wish to compute site scores using PCA (prcomp) on a matrix with missing values, for example: Drain Slope OrgL a 4 1 NA b 2.5 39 6 c 6 8 45 d 3 9 12 e 3 16 4 ... Where a,b... are sites. The command > pca<-prcomp(~ Drain + Slope + OrgL, data = t, center = TRUE, scale = TRUE, na.action=na.exclude) works great, and from

Dear R-Helpers, I have a large data matrix, which contains missing data. The matrix looks something like this: 1) X X X X X X NA NA NA 2) NA NA NA NA X X X X X 3) NA NA X X X X NA NA NA 4) X X X X X X X X X 5) X X NA NA X NA NA NA NA and so on. Notice that the first row starts with complete data but ends with missing. The second row starts with missing, but the rest is

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

Hello, I have tried reading the documentation and googling for the answer but reviewing the online matches I end up more confused than before. My problem is apparently simple. I fit a glm model (2^k experiment), and then I would like to predict the response variable (Throughput) for unseen factor levels. When I try to predict I get the following error: > throughput.pred <-

I am having a couple of problems someone may be able to cast some light on. Question 1: I am making a logistic model but when i do this: glm.model = glm(as.factor(form$finished) ~ ., family=binomial, data=form[1:150000,]) I get this: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for 'barrier') which is

The following is evidence of what is surely an undesirable feature. The issue is the handling, in calls to model.frame(), of an explanatory variable that has been derived as an unclassed factor. (Ross Darnell drew this to my attention.) ## Data are slightly modified from p.191 of MASS > worms <- data.frame(sex=gl(2,6), Dose=factor(rep(2^(0:5),2)), +

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.

Hi all, As you can see from below, the result is strange... I would imagined that the bb result should be much higher and close to 1, any way to improve the fit? Any other classification methods? Thank you! data=data.frame(y=rep(c(0, 1), times=100), x=1:200) aa=glm(y~x, data=data, family=binomial(link="logit")) newdata=data.frame(x=6, y=100) bb=predict(aa, newdata=newdata,

Dear all, To modelize the abundance of fish (4 classes) with a set of environmental variables, I used the polr and predict.polr functions. I would like to know how to bring the cumulated probabilities back to a discrete ordinal scale. For the moment I used the predict.polr function with the argument "class". Is there an other way? polrf <- polrf <- polr_mod(formula =