similar to: bigglm binomial negative fitted value

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, 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)

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 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,

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,

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,

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

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*

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)

Gracias! El 15 de junio de 2015, 16:54, Freddy Omar López Quintero < freddy.vate01 en gmail.com> escribió: > ?Holap.? > > ran out of iterations and failed to converge > > > ?Prueba aumentando el número de iteraciones, con el argumento maxit: > > ?GLM <- bigglm(In.hospital_death ~ GCS + BUN, data = DatosGLM, family = >> binomial(logit), maxit=1000)? >

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 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 R, tengo las siguientes preguntas: Pregunta 1: Cargar las tablas de los datos de peliculas en R usando `ff`. Cómo se construye una columna nueva que de, para cada cliente y cada evaluación, de el número de días que han pasado desde la primera evaluación del cliente? Qué función se utiliza para verifica el consumo de memoria en las operaciones? Pregunta 2: Cómo se corre un modelo de

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

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

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

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

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