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Hi, I'm currently using biglm package to compute GLM outputs on a very large dataset. But no function computes standard erros of predictions. I look in what is done in R, namely in the function predict.glm.R in stats package. In this function, we call predict.lm to compute the standard errors (line 51). The code of predict.lm (in lm.R) is very hard to understand. I wonder if there is any

New versions of leaps and biglm are percolating through CRAN. The new version of biglm fixes a bug in sandwich standard errors with weights, and adds predict(), deviance() and AIC() methods [based on code from Christophe Dutang]. The new version of leaps adds a regsubsets() method for biglm objects, so that the subset selection algorithms can be run efficiently on large data sets. -thomas

New versions of leaps and biglm are percolating through CRAN. The new version of biglm fixes a bug in sandwich standard errors with weights, and adds predict(), deviance() and AIC() methods [based on code from Christophe Dutang]. The new version of leaps adds a regsubsets() method for biglm objects, so that the subset selection algorithms can be run efficiently on large data sets. -thomas

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

I am trying to figure out why 'biglm' can handle large data set... According to the R document - "biglm creates a linear model object that uses only p^2 memory for p variables. It can be updated with more data using update. This allows linear regression on data sets larger than memory." After reading the source code below? I still could not figure out how 'update'

Dear all, I am working on a large dataset and need to use biglm() to perform OLS regressions. I have detected significant ARCH effects which I try to account for using the Newey-West correction. So far, I have worked with NeweyWest() in the sandwich package. NeweyWest() however seems to be unable to handle an object of class "biglm". Looking into the code, I figured out that

Hello colleagues, I have tried to use the package 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 a error message like ?object not found? or ?`weights' must be a formula? or "error in eval(expr, envir, enclos)". 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*

Thanks for your email. I try to change the name in lowercase but it conflicts with a C implementation also named halton. So I rename the C function halton2() and sobol2() while the Fortran function are HALTON() and SOBOL() (I also try lower case in the Fortran code). Unfortunately, it does not help since I get init.c:97:25: error: use of undeclared identifier 'halton_'; did you mean

Hi all - I'm trying to find a way to create dummy variables from factors in a regression. I have been using biglm along the lines of ff <- log(Price) ~ factor(Colour):factor(Store) + factor(DummyVar):factor(Colour):factor(Store) lm1 <- biglm(ff, data=my.dataset) but because there are lots of colours (>100) and lots of stores (>250), I run it to memory problems. Now, not every

I've been working with a rather large data set (~10M rows), and while biglm works beautifully for generating coefficients, it does not report an r-squared. It does report RSS. Any idea on how one could coax an R-squared out of biglm? Thanks in advance for any help with this! Bryan Lim Lecturer Department of Finance University of Melbourne [[alternative HTML version deleted]]

Have you tried using tools:::package_native_routine_registration_skeleton()? If you don't like its output, you can easily edit its results and still avoid most pitfalls. Cheers, Jari Oksanen ________________________________________ From: R-devel <r-devel-bounces at r-project.org> on behalf of Berend Hasselman <bhh at xs4all.nl> Sent: 10 May 2017 09:48 To: Christophe Dutang Cc:

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 all, I tried to a nice legend with small boxes filled with the colors used for the plots. But it does nor work, boxes are always filled with black. An example is here plot(1:4,1:4) lines(1:4,4:1, col="blue") legend("top",leg=c("a","b"),col=c("black","blue"), fill=TRUE) How could I specify the colors? the argument col.box is the

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

Hi, I would like to draw arrows in a classic 2D plot. Which package should I use? is there R base functions that do job? On google, I could not find any useful discussion about this topic, except a link to the function 'grid.arrows' of the grid package. My problem is I would like to draw arrows at the edge of circles drawn by the 'symbols' function. Maybe there is already a

Hello. I had been struggling with running linear regression using lm() primarily because my data has a few categorical variables with at least a thousand levels. I tried the biglm() function and it worked. My problem now is that i don't know how to get the R2 results. Could someone help? Thanks, sean

Hi all, I'm currently working on the fitdistrplus package (that basically fit distributions). There is something I do not understand about the generic function summary. In the current version on CRAN, there is no NAMESPACE saying S3method(summary, fitdist) . However if we use summary on an object send by fitdist function it works fine... According to R-lang, we have " The most

Dear list, I?m trying to register Fortran routines in randtoolbox (in srt/init.c file), see https://r-forge.r-project.org/scm/viewvc.php/pkg/randtoolbox/src/init.c?view=markup&root=rmetrics. Reading https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Registering-native-routines and looking at what is done in stats package, I first thought that the following code will do the job:

Dear list, The task view on optimization does not reference a package for non linear constrained optimization problems. Stefan Theussl told me to look at the Rsolnp package, but unfortunately it is not very clear what method is R ported. (The authors ported the matlab code of Yinyu Ye http://www.stanford.edu/~yyye/ <http://www.stanford.edu/%7Eyyye/>) Currently I'm looking for an