similar to: biglm and epicalc ROC curves

Dear R-help list, I'm attempting to use the ROC routine from the epicalc package after performing a logistic regression analysis. My code is included after the sessionInfo() result. The datafile (GasketMelt1.csv) is attached. I updated both R and the epicalc packages and tried again before sending this request. sessionInfo result: R version 2.9.1 (2009-06-26) i386-pc-mingw32 locale:

Dear R-help, I am resending as I believe I screwed up the e-mail address to R-help earlier. Sorry for my lack of attention to detail, and for any inconvenience. I have also sent the question to the package maintainer, as suggested in the posting guide. Regards, Cliff ---------- Forwarded message ---------- From: Clifford Long <gnolffilc at gmail.com> Date: Sun, Jul 26, 2009 at 8:46

This is to submit a commented example function for use in the data argument to the bigglm(biglm) function, when you want to read the data from a file (instead of a URL), or rescale or modify the data before fitting the model. In the hope that this may be of help to someone out there. make.data <- function (filename, chunksize, ...) { conn<-NULL; function (reset=FALSE) { if

Hi Everyone, I often use the 'safe prediction' feature available through glm(). Now, I'm at a situation where I must use biglm:::bigglm. ## begin example library(splines) library(biglm) ff <- log(Volume)~ns(log(Girth), df=5) fit.glm <- glm(ff, data=trees) fit.biglm <- bigglm(ff, data=trees) predict(fit.glm, newdata=data.frame(Girth=2:5)) ## -1.3161465 -0.2975659

G'morning What does the error message "Error in x %*% coef(object) : non- conformable arguments" indicate when calculating the response values for newdata with a model from bigglm (in package biglm), and how can I debug it? I am attempting to do Monte Carlo simulations, which may explain the loop in the code that follows. After the code I have included the output, which shows that

Dear R-friends, ? I am using the epicalc package and the manual by V. Chongsuvivatwong "Analysis of epidemiological data using R and Epicalc" to get the hang of some basic epidemiological analyses.??? ? After running all the analyses of chapter 7, one is supposed to wrap it up by saving the data writing: ? ? > save(.data, file = "Chapter7.Rdata") ? ...?after writing the

Hola a todos, Estoy teniendo problemas para cambiar el nombre de los factores. Mi matriz original es de la siguiente forma: > alfa local mes amb Fase sp1 sp2 sp3 sp4 L1-P1-C Ago.10 Lentico LFP 22.111664 0 0 0 L3-P3-M Ago.10 Lentico LFP 22.111664 5.527916 5.527916 0 P-1-C Ago.10 Lotico LFP 11.055832 0 0 0 P-3-M Ago.10 Lotico LFP 55.27916 5.527916 11.055832 0

Hi everyone, I'm working with the Epical library, specicatly using the power test in proportions. I think this test is not working like in the book: Biostatistical Analysis (4th Edition): Jerrold H. Zar In the example 23.25. (I attach this Pic) It's not the same answer. Using the follow command don't give the same answer. library(epicalc) power.for.2p(0.75, 0.50, 50, 45, alpha =

Hello colleagues, I have tried to use the package bigmemory, biganalytics and biglm. I want to specify a multivariate regression with a weight. I have imported a large dataset with the library(bigmemory). I load the library (biglm) and specified a regression with a weight. But I get everytime an error message like "object not found" or "`weights' must be a

Hi everyone, I have been looking at the Bigglm (Basically does Generalised Linear Models for big data under the Biglm package) command and I have done some profiling on this code and found that to do a GLM on a 100mb file (9 million rows by 5 columns matrix(most of the numbers were either a 0,1 or 2 randomly generated)) it took about 2 minutes on a linux machine with 8gb of RAM and 4 cores.

Hello there, I just upgraded to R 2.15 (from R 2.12) on a Windows XP machine and noticed some puzzling behaviour (that in my opinion did not exist in R 2.12). It is possible now to call objects without spelling out the full object name. R now seems to use that (unique) object which shares the same beginning of the called object, even though the originally called object might not even exist.

Dear All, I would like to publish a function for 'heckit' estimations together with two examples from Greene's and Wooldridge's econometric textbooks. These examples use the dataset of Mroz (1987) that is also available in John Fox' "car" package. However, not all variables that are used in my examples are available in the "car" package. Therefore, I

If a package defined an S3 generic and an S4 generic for the same function (so as to add methods for S4 classes to the existing code), how do I set up the namespace to have them exported? With import(stats) exportMethods(bigglm) importClassesFrom(DBI) useDynLib(biglm) export(biglm) export(bigglm) in NAMESPACE, the S3 generic is not exported. > methods("bigglm") [1] bigglm.RODBC*

Hi, Until now, I thought that the results of glm() and bigglm() would coincide. Probably a naive assumption? Anyways, I've been using bigglm() on some datasets I have available. One of the sets has >15M observations. I have 3 continuous predictors (A, B, C) and a binary outcome (Y). And tried the following: m1 <- bigglm(Y~A+B+C, family=binomial(), data=dataset1, chunksize=10e6)

On Nov 16, 2009, at 1:43 PM, Dale Johannesen wrote: > > On Nov 14, 2009, at 3:16 PMPST, Howard Hinnant wrote: > >> In many places there is code that looks like: >> >> MBBI = next(MBBI); >> >> In C++0X there is a std::next that is likely to be in scope when these >> calls are made. And due to ADL the above call becomes ambiguous: >>

Dear all, I have a large data set and would like to fit a logistic regression model using the bigglm function. I need to include an offset in the model but when I do this the bigglm function seems to ignore it. For example, running the two models below produces the same model and the offset is ignored bigglm(y~x,offset=z,data=Test,family=binomial(link = "logit"))

Hi Sir, Thanks for making package available to us. I am facing few problems if you can give some hints: Problem-1: The model summary and residual deviance matched (in the mail below) but I didn't understand why AIC is still different. > AIC(m1) [1] 532965 > AIC(m1big_longer) [1] 101442.9 Problem-2: chunksize argument is there in bigglm but not in biglm, consequently,

Dear all, I am using the bigglm package to fit a few GLM's to a large dataset (3 million rows, 6 columns). While trying to fit a Poisson GLM I noticed that the coefficient estimates were very different from what I obtained when estimating the model on a smaller dataset using glm(), I wrote a very basic toy example to compare the results of bigglm() against a glm() call. Consider the

I would like to perform a multinomial logistic regression on a large data set, but do not know how. I've only thought of a few possibilities and write to seek advice and guidance on them or deepening or expanding my search. On smaller data sets, I have successfully loaded the data and issued commands such as: length(levels(factor(data$response))) [1] 6 # implies polychotomy library(nnet)

Howard Hinnant wrote: > On Nov 16, 2009, at 1:43 PM, Dale Johannesen wrote: > > >> "next" is a popular name; if it breaks llvm, I'd expect this standards change to break a lot of existing code. Do you really want to do that? >> > > I'm happy to open an LWG issue for you on this subject. Here are directions on submitting an issue: > >