similar to: lm.fit peak memory usage

Hi, all. I want to write some functions like glm() so i studied it. In glm.fit(), it calls a fortran subroutine named "dqrfit" to compute least squares solutions to the system x * b = y To learn how "dqrfit" works, I just follow how glm() calls "dqrfit" by my own example, my codes are given below: > qr <- >

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

Hi All: I'm new to R and have a few questions about getting R to run efficiently with large datasets. I'm running R on Windows XP with 1Gb ram (so about 600mb-700mb after the usual windows overhead). I have a dataset that has 4 million observations and about 20 variables. I want to run probit regressions on this data, but can't do this with more than about 500,000 observations before

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

G'day all, I had a look at the GLM code of R (1.4.1) and I believe that there are problems with the function "glm.fit" that may bite in rare circumstances. Note, I have no data set with which I ran into trouble. This report is solely based on having a look at the code. Below I append a listing of the glm.fit function as produced by my system. I have added line numbers so that I

While using the file functions have found a few more issues (on both Unix and Windows) ie R documentation: In the "close" description (base package) we see that the possible values for the mode 'open' the "r+" and "r+b" values are repeated, and are incorrect the second time. The second set actually corresponds to the "w+" / "w+b", see

Hi all, I have one array of predictors, one observation per row, and one array of responses, also arranged one observation per row. I arrange these into a data.frame and call lm() with a pasted-together formula. I would like to call lm() with a number of responses in excess of 100, but for some reason, 39 seems to be a limit. Why do I get an "invalid variable names" error from

I did a linear correlation of data using glm.fit and stored the output in the object "f": f <- glm.fit(x, y, w) I am intereseted in estimating the quality of the correlation. I am used to do it using pearson correlation coefficient "r" or "r^2". Can I extract this coefficient from the output of glm.fit? Is there another number in the output of glm.fit that

I wonder if someone could explain how the following R idiom works (it's used in glm.fit). start <- coef start[fit$pivot] <- coef coef is a vector of coefficients, set by .Fortran("dqrls", ...). fit$pivot is a vector of integer indexes (indicating how dqrls permuted the columns of x). If coef has n elements, fit$pivot is a permutation of seq(1,5). start[fit$pivot]

Below is a SIMEX object that was generated with the "simex" function from the "simex" package applied to a logistic regression fit. From this mountain of information I would like to extract all of the values summarized in this line: .. ..$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ... Can someone suggest how to go about doing this? I can extract the

Here is a modified version of qraux.Rd, an edited version of R-2.14.0/src/library/base/man/qraux.Rd This gives some details and an example for the case of pivoting. In this case, it is not true that X = QR; rather X[, pivot] = QR. It may save some other people bugs and time to have this information. Tim Hesterberg -------------------------------------------------- % File

Have searched through the archives but have been unable to find any related issues - hopefully I'm not bringing up an old topic. Am using RW1041 on a Windows NT on a machine with 1Gb of memory. Have a function doit() that reads in a chunk of data using readBin, performs a regression, saves out coeffs and then returns. When using Rgui with the default memory limit of 256Mb I'm able to

To the purpose of fitting a 2nd order polynomial (a + b*x + c*x^2) to the chunk of signal falling in a 17 consecutive samples window I wrote the following very crude script. Since I have no previous experience of using Least Square Fit with R I would appreciate your supervision and suggestion. I guess the returned coefficients of the oolynomial are: a = -1.3191398 b = 0.1233055 c = 0.9297401

Hi, Is there a recommended method using R header files for opening files from within a DLL? I can remember reading something on this topic, but have been unable to find it in any of the recent FAQs or in the help archive. I'm using the C open/lseek/read/close functions with the usual C header files ie #include <stdio.h> #include <math.h> #include <string.h>

The following snippet suggests that there is either a bug in qr(,LAPACK=T), or some bug in my understanding. Note that the detected rank is correct (= 2) using the default LINPACK qr, but incorrect (=3) using LAPACK. This is running on Linux Redhat 9.0, using the lapack library that comes with the Redhat distribution. I'm running R 1.7.1 compiled from the source. If the bug is in my

Hi everyone, I am looking to use R as a MATLAB replacement for linear algebra. I've done a fairly good job for finding replacements for most of the functions I'm interested in, I John Fox wrote a program for implementing the reduced row echelon form of a matrix (by doing the Gauss-Jordan elimination). I modified it a bit: rref <- function(A,

Hi all I'm getting a NaN returned on using dffits, as explained below. To me, there seems no obvious (or non-obvious reason for that matter) reason why a NaN appears. Before I start digging further, can anyone see why dffits might be failing? Is there a problem with the data? Consider: # Load data dep <-

I suggest adding a 'pivot' argument to qr.R, to obtain columns in the same order as the original x, so that a <- qr(x) qr.Q(a) %*% qr.R(a, pivot=TRUE) returns x. -------------------------------------------------- # File src/library/base/R/qr.R qr.R <- function(qr, complete = FALSE, pivot = FALSE) { # Args: # qr: a QR decomposition, produced by qr() # complete:

Hi, Seems that on RWin 1.7.0 and 1.6.2 isSeekable returns F on binary files, while seek() works as expected on the same connection - see example below: > con = file(nm, "rb") > isSeekable(con) [1] FALSE > readBin(con, double(), 10) [1] 7.263824e-317 5.968155e-317 2.340685e-317 2.734062e-312 4.088386e-312 4.670335e-317 [7] 6.097545e-317 3.396341e-312 6.615484e-317

Hello. I am trying to invert a matrix, and I am finding that I can get different answers depending on whether I set LAPACK true or false using "qr". I had understood that LAPACK is, in general more robust and faster than LINPACK, so I am confused as to why I am getting what seems to be invalid answers. The matrix is ostensibly the Hessian for a function I am optimizing. I want to get