similar to: How does .Fortran "dqrls" work?

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

Dear all, I’m not sure if it is O.K. to ask this question here. But where can I find the code for the function ‘C_Cdqrls’ which is called by the R function ‘lsfit‘. Thank you all. Sorry for being naïve if so. -------------------- Ziqiang Zhao 2013-04-23 [[alternative HTML version deleted]]

Hi, I've been running out of memory while using the lm.fit function - have solved the problem and thought there might be interest in incorporating some of the changes. Looked at the source and changed the following lines storage.mode(x) <- "double" storage.mode(y) <- "double" z <- .Fortran("dqrls", qr = x, n = n, p = p, y = y, ny = ny,

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

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

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

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]

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 *

Might it be possible to tighten the definition of "is.qr". I noticed that after I mistakenly typed example(lm) # make lm object named lm.D9 qr.Q(lm.D9) which exhausted the heap memory and produced two warning messages. As an object of class "lm" has a "qr" component, "is.qr" failed to detect that "lm.D9" was not a "qr" object. The

Dear All, I would like to define a function: f(x,y,z) with three arguments x,y,z, such that: given values for x,y, f(x,y,z) is still a function of z and that I am still allowed to find the root in terms of z when x,y are given. For example: f(x,y,z) = x+y + (x^2-z), given x=1,y=3, f(1,3,z)= 1+3+1-z is a function of z, and then I can use R to find the root z=5. Thank you. -Chee [[alternative

Is there a way of getting the argument list of a function from within that function? For example, something like f <- function(x, y = 3) { fargs <- getFunctionArgList() print(fargs) ## Should be `alist(x, y = 3)' } Thanks, -roger

Hi All, I try to do a scatterplot for a bunch of variables. I plot a dependent variable against a bunch of independent variables: -- cut -- graphics::plot( v01_r01 ~ v08_01_up11, data = dataset, xlab = "Dependent", ylab = "Independent #1" ) -- cut -- It is tedious to repeat the statement for all independent variables. Found an alternative, i.e. : -- cut -- mu

Hello, Are there any access functions to the various residual variables that should result from a repeated measures ANOVA ? MyAOVObject$residuals does not exist, and simply printing MyAOVObject gives a very long print of all fields in the result list, many of which I can't see what they are exactly : $error.qr$qraux, for instance. What I would like basically is to inspect those residuals

Full_Name: Alexander Blocker Version: 2.7.1 OS: Ubuntu 8.04 / Windows XP Submission from: (NULL) (76.119.235.225) When running lm(modeleq, weights=wt, data=dataset) with small weights (<1e-10), I have encountered an odd phenomenon with fitted values. Due to numerical precision issues, the fitted values and residuals returned by lm.wfit (from its .Fortran call to dqrls) can differ greatly from

Consider the following missing data problem: y = c(1, 2, 2, 2, 3) a = factor(c(1, 1, 1, 2, 2)) b = factor(c(1, 2, 3, 1, 2)) fit = lm(y ~ a + b) anova(fit) Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) a 1 0.83333 0.83333 1.3637e+33 < 2.2e-16 *** b 2 1.16667 0.58333 9.5461e+32 < 2.2e-16 *** Residuals 1 0.00000 0.00000 ---

Dear Sirs, I am trying to compile R v1.2.1 under Digital Unix 4.0D. The configure-script runs without any problems but during 'make' I receive the following error-message: [......] g77 -mieee -g -O2 -c dtrco.f -o dtrco.o g77 -mieee -g -O2 -c dtrsl.f -o dtrsl.o g77 -mieee -g -O2 -c eigen.f -o eigen.o g77 -mieee -g -O2 -c lminfl.f -o lminfl.o ar cr libappl.a Rsock.o approx.o bakslv.o

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

Dear Friends, I had performed anova test on certain data frame (Health Care Management) and got results [summary(aov)]. I am new to R and also some extent to Statistics. Can somebody help me how should I interpret these figures. I feel difficulty in interpreting values and respective rows and columns. The following is the result to which I request interpretation: > anova.stress$effects

man for `qr` says that the function uses LINPACK's DQRDC, while it in fact uses DQRDC2. ``` The QR decomposition of the matrix as computed by LINPACK or LAPACK. The components in the returned value correspond directly to the values returned by DQRDC/DGEQP3/ZGEQP3 ```