similar to: GAM for censored data? (survival analysis)

I need to give a quick description of Tobit Regression (TR), including how it differs from ordinary least squares (OLS). I am an ecologist who knows just enough about remote sensing and statistics to be dangerous in both. Now I have found myself doing a remote sensing project where I have used TR: survreg(Surv()). As far as I can tell, no form of Censored Regression has been used in analyzing

Dear r-helpers, I estimated a generalized additive model (GAM) using Hastie's package GAM. Example: gam1 <- gam(vegetation ~ s(slope), family = binomial, data=aufnahmen_0708, trace=TRUE) pred <- predict(gam1, type = "response") vegetation is a categorial, slope a numerical variable. Now I want to assess the accurancy of the model using k-fold cross validation. I found the

dear all, I would like to know whether survdiff and survReg function in the survival package work for left-truncated and right-censored data. If not, what other functions can i use to make comparison between two survival curves with LTRC data. thanks for any help given sing yee

Dear All, Anyone know if there is a package that fits Generalized Linear Models(GAM) to data with ordered dependent variable(response) ? Simon Wood's mgcv has probit, logit,... other links, however, I could not find a way to do GAM *ordered *probit. Yee's VGAM claims to fit ordinal proportional odds model(cumulative logit model) (see: http://www.stat.auckland.ac.nz/~yee/VGAM/) but I

Dear r-helpers, I use function step.gam (package gam, T. Hastie) with several explanatory variables to build a model. Unfortunately, I obviously have too many variables. This message occurs on my 4 core 64bit machine with 8GB RAM in R2.11.1 for Windows (64bit build): Error in array(FALSE, term.lengths) : 'dim' specifies too large an array I read that this message occurs when running out

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

Dear users, I am trying to show the equation (including coefficients from the model estimates) for a gam model but do not understand how to. Slide 7 from one of the authors presentations (gam-theory.pdf URL: http://people.bath.ac.uk/sw283/mgcv/) shows a general equation log{E(yi )} = ?+ ?xi + f (zi ) . What I would like to do is put my model coefficients and present the equation used. I am an

Thank you for looking into this so quickly. As you correctly surmise, I was using the Carbon version of R-1.6.1 on Mac OS 10.2.2 (Jaguar) when I got the "wrong" answers. One other observation: The right censoring seems to work fine. Thanks again, Tim On Thursday, November 14, 2002, at 11:09 AM, Jan de Leeuw wrote: > I take that back. I now get the "correct" result

OK, I really dread to ask that .... much more that I know some discussion about p-values and if they are relevant for regressions were already on the list. I know to get p-val of regression coefficients - this is not a problem. But unfortunately one editor of a journal where i would like to publish some results insists in giving p-values for the squared deviance i get out from different glm and

Dear all I have a fairly funky problem that I think demands some sort of survival analysis. There are two Red List assessments for mammals: 1986 and 2008. Some mammals changed their Red List status between those dates. Those changes can be regarded as "events" and are "interval censored" in the sense that we don't know at what point between 1986 and 2008 each species

In your particular case I don't think that censoring is an issue, at least not for the reason that you discuss. The basic censoring assumption in the Cox model is that subjects who are censored have the same future risk as those who were a. not censored and b. have the same covariates. The real problem with informative censoring are the covaraites that are not in the model; ones that

Hi there, I´ve a question concerning the nnet package in the area of survival analysis: what is the final value, which is computed to fit the model with the following nnet-c all: net <- nnet(cat~x, data=d, size=2, decay=0.1, censored=TRUE, maxit=20, Wts=rep(0,22), Hess=TRUE) where cat is a matrix with a row for each record and

Hello, I'm interested in correcting for and measuring unobserved heterogeneity ("missing variables") using R. In particular, I'm searching for a simple way to measure the amount of unobserved heterogeneity remaining in a series of increasingly complex models (adding additional variables to each new model) on the same data. I have a static database of 400,000 or

I read through Harrington and Fleming (1982) but it is beyond my statistical comprehension. I have survival data for insects that have a very finite expiration date. I'm trying to test for differences in survival distributions between different groups. I understand that the medical field is most often dealing with censored data and that survival analysis, at least in the package survival,

Dear All, I would like to fit some parametric survival models using left truncated, right censored data in R. However I am having problems finding a function to fit parametric survival models which can handle left truncated data. I have tested both the survreg function in package survival: fit1 <- survreg(Surv(start, stop, status) ~ X + Y + Z, data=data1) and the psm function in package

Can anybody give me a neat example of interval censored data analysis codes in R? Given that suvreg(Surv(c(1,1,NA,3),c(2,NA,2,3),type="interval2")~1) works why does survreg(Surv(data[,1],data[,2],type="interval2")~1) not work where data is : T.1 T.2 Status 1 0.0000000 0.62873036 1 2 0.0000000 2.07039068 1 3 0.0000000

The answer to this may be obvious, but I was wondering in the rms package and the survfit(), how you can plot the censored time points as ticks. Take for example, library(survival) library(rms) foo <- data.frame(Time=c(1,2,3,4,5,6,10), Status=c(1,1,0,0,1,1,1)) answer <- survfit(Surv(foo$Time, foo$Status==1) ~1) # this shows the censored time points as ticks at Time = 3 and 4 plot(answer)

On Wed, 13 Nov 2002, Jan de Leeuw wrote: > > No problemo. And, in fact, I get the same results in > the R-1.6.0 Carbon version. I don't. Could there be a G3/G4 issue? -thomas > --- Jan > > On Wednesday, November 13, 2002, at 02:05 PM, tim@timcohn.com wrote: > > > Full_Name: Tim Cohn > > Version: 1.6.1 > > OS: Macintosh OS X > > Submission

R-ecologists: Anyone wanting to create a Krebs package for R can do so using the C-source code avalaible at: ftp://gause.biology.ualberta.ca/pub/jbrzusto/krebs/source.zip Barry J. Cooke Current mailing address: Ph.D. Candidate 3971 NW 23 Circle Environmental Biology and Ecology Gainesville, Florida, USA Department of Biological Sciences 32605

Would anybody be able to advise on which package would offer the best approach for producing a model able to predict the probability of species occupation based upon a range of variables, some of them catagorical (eg. ten soil types where the numbers assigned are not related to any qualitative/quantitative continuum or vegetation type) and others continuous such as field size or vegetation height.