similar to: Fitting a model with an offset in bigglm

Hi i am having a problem with the 'integrate' function the function i want to integrate has the form sum(vector^x) i have defined the function with a for loop first - integrandtotest <- function(x) {a<-rep(0,len=2) for (i in 1:2) {a[i]<-t[i]^x} sum(a) } the results gives errors ########### Error in integrate(integrandtotest, lower = 0.1, upper =

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

Hi, since bigglm doesn't have update, I was wondering how to achieve something like (similar to the example in ff package manual using biglm): first <- TRUE ffrowapply ({ if (first) { first <- FALSE fit <- bigglm(eqn, as.data.frame(bigdata[i1:i2,,drop=FALSE]), chunksize = 10000, family = binomial()) } else { fit <- update(fit,

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

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

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*

I am using an example posted in this help forum to work with a file. the head of the file looks like: 988887 2007-03-05 2007-06-01 90 3 5.450 205500.00 999.00 999.000 0.000 0 0 988887 2007-03-06 2007-06-01 90 3 5.450 205500.00 999.00 999.000 0.000 1 0 988887 2007-03-07 2007-06-01 90 3 5.450 205500.00 999.00 999.000 -0.100 2 0 988887 2007-03-08 2007-06-01 90 3 5.450 205500.00 999.00 999.000 -0.100

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)

Hi, there Since glm cannot handle factors very well. I try to use bigglm like this: logit_model <- bigglm(responser~var1+var2+var3, data, chunksize=1000, family=binomial(), weights=~trial, sandwich=FALSE) fitted <- predict(logit_model, data) only var2 is factor, var1 and var3 are numeric. I expect fitted should be a vector of value falls in (0,1) However, I get something like this:

Hi all, I'm somewhat of a novice in terms of programming, so I thought I'd come here to seek some help with an issue I'm having. I'm trying to model a glm using bigglm, but in spite of my best efforts, I cannot get it to work! Here is the particular line of code that is giving me trouble: >mod = bigglm(Pres/wt ~ Xdes, data=dat, family=poisson(), weights = ~wt, maxit=100,

Dear all, I am trying to calculate the proximity matrix for a data set with 16 variables and 6804 observations using random forests. I have a Pentium 4, 3.00GHz processor with 1 GB of RAM. When I use the command randomForest(data.scale,proximity=T) I get the warning message Error: cannot allocate vector of size 361675 kb Is this because I have reached the limit of what my computer is

Hi R-experts, Does anyone have experience using R for handling large scale data (millions of rows, hundreds or thousands of features)? What is the largest size of data that anyone has used with glm? Also, is there a library to read data in sparse data format (like SVMlight format)? Thanks Pradheep [[alternative HTML version deleted]]

Hola, estoy intentando hacer una regresión logística entre la primera columna de mi data.table (In.hospital_death) y otras dos (GSV y BUN) , me da el error de abajo, he intentado eliminar las filas con valor NA por si esta función no lo admite, pero sigue dando el mismo error. ¿Alguien sabe porqué ocurre? (probé previamente a usar la función glm pero obtenía out of memory) library(XLConnect)

Hello folks, I am very excited to have discovered R and have been exploring its capabilities. R's regression models are of great interest to me as my company is in the business of running thousands of linear regressions on large datasets. I am using biglm to run linear regressions on datasets that are as large as several GB's. I have been pleasantly surprised that biglm runs the

Greetings useRs, Can anyone provide an example how to use ff to feed a very large data frame to glm? The data.frame cannot be loaded in R using conventional read.csv as it is too big. glm(...,data=ff.file) ?? Thank you Stephen B

I'd like to fit linear models on very large datasets. My data frames are about 2000000 rows x 200 columns of doubles and I am using an 64 bit build of R. I've googled about this extensively and went over the "R Data Import/Export" guide. My primary issue is although my data represented in ascii form is 4Gb in size (therefore much smaller considered in binary), R consumes about

We are wanting to use R instead of/in addition to our existing stats package because of it's huge assortment of stat functions. But, we routinely need to fit GLM models to files that are approximately 2-4GB (as SQL tables, un-indexed, w/tinyint-sized fields except for the response & weight variables). Is this feasible, does anybody know, given sufficient hardware, using R? It appears to

Hello list, I am trying to avoid "Rifying" some of my SAS code to generate ROC plots, and the logistic.display() and lroc() functions in the epicalc package do what I want. However, I must generate my logistic model with bigglm because I have 1) limited hardware, 2) ~2.5 million rows, and 4 categorical and 2 continuous independent variables. When I attempt to invoke epicalc's

Hello, I searched the internet but i didn't find the answer for the next problem: I want to do a glm on a csv file consisting of 25 columns and 4 mln rows. Not all the columns are relevant. My problem is to read the data into R. Manipulate the data and then do a glm. I've tried with: dd<-scan("myfile.csv",colClasses=classes) dat<-as.data.frame(dd) My question is: what