similar to: Reading large datasets and fitting logistic models in R

Hi ! Right now I am learning to use svm functions available in R and trying to use these function with given example. I was stuck with svmlight function which is available in klaR package. Any help would be appreciated regarding this function. 1. I am unable to use svmlight( ) which is available in package: klaR. Although I have downloaded klaR_0.6-3 package from

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"))

When I used svmlight, I got below error: my command is: foo <- svmlight(y~., data= myData) the results: Error in file(con, "r") : unable to open connection In addition: Warning messages: 1: svm_learn not found 2: cannot open file '_model_1.txt' > myData[1:2,] y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 1 1 63 1 0 0 145 233 1 1 0 150 0 2.3 1

Hi R experts, I used biglm to construct a model (which has categorical variables). When I run predict on the model output on a new data (for testing) or on the same data, I get only NA's. I'm able to run predict with some other models constructed with biglm. One reason I suspect is that the model itself has a few undefined terms (NA's). I'm wondering if there's any way to

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'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

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,

I am refering to a function call like this: >data(iris) >x <- svmlight(Species ~ ., data = iris) I tried to see the content of it by typing: > Species ~ . but it gives nothing. How can I see it's content ? - P.Dubois

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, I was trying to run a guest OS with multiple partitions. I added a line like disk=[''phy:hda6,hda6,w''] in the configuration file. The disk was not mounted in the host OS. However, the kernel refuses to boot with an error message which says "kernel not syncing... attempted to kill init... rebooting...". When I remove that line, everything works. Is there

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,

Is there any way to disable the auto-complete feature when we index a named list? E.g: a <- list ('longname'=1, 'anothername'=2) a$l will return 1 and a$a will return 2 the '[[' operator behaves in the same way, the '[' operator does not do autocomplete. Is there any way to disable autocomplete for all the operators? Thanks Pradheep [[alternative HTML version

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

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

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

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:

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

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

Dear R Users, When I used svmlight, I got below error: my command is: foo <- svmlight(y~., data= myData) the results: Error in file(con, "r") : unable to open connection In addition: Warning messages: 1: svm_learn not found 2: cannot open file '_model_1.txt' > myData[1:2,] y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 1 1 63 1 0 0 145 233 1 1 0

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)