similar to: Fitted values with small weights in lm.wfit (PR#11979)

Hi, I am trying to create an R interface for Dynare++ [1], a k-order solver for rational expectation models. I would like some advice on how to glue the C++ code to R. In C++, it works the following way: - a DynareModelEq object is initialized with the formulas, parses them and performs k-order symbolic expansions - the user calls a member of this object multiple times with parameter

Dear R developers, Taking advantage of the yesterday discussion about the use of Depends/Import/Suggests/Enhances, I would like to add a related question. Let's assume, in the DESCRIPTION file of my package foo0, I have: Depends: foo1 Imports: foo2 while in the NAMESPACE file of my package I have importFrom("foo2", f2) and within my package I use the following two external

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 *

R 0.63.3 (any platform) library(lqs) example(lqs) gives Warning in (y - z$residuals) + offset : longer object length is not a multiple of shorter object length repeatedly. The problem is in lm.wfit, which removes cases with zero weights, but does not adjust the offset. The fix is z$fitted.values <- (y - z$residuals) z$weights <- w if (zero.weights) {

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 *

Dear R users, I recently stumbled upon problems of reproducibility while running GAM analyses in different R and gam package versions. In the example below, a small dataset is created in which the y and x1 variables are 100% correlated. The intents of this example were primarily for regression testing and, secondarily, to evaluate how the gam algorithm behaves under extreme/limit conditions. I

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 <- >

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: Could anyone help me to clarify this: are the weights normalized inside lm function (package:stats) before applied to the error term? For example: >lm (cost ~ material, weights=quatity, data=receipt) will lm normalize quatity such that sum(quatity) = 1? I traced to lm.wfit and then the weights get transferred into a precompiled FORTRAN module so I can't figure out. Thanks!

Dear Group, I am having trouble with using rlm on multivariate data sets. When I call rlm I get Error in lm.wfit(x, y, w, method = "qr") : incompatible dimensions lm on the same data sets seem to work well (see code example). Am I doing something wrong? I have already browsed through the forums and google but could not find any related discussions. I use Windows XP and R

Full_Name: P?l Trosvik Version: 2.0.1 OS: Windows XP Submission from: (NULL) (129.240.91.220) Hi Upon entering the command: help.search("linear") .I get the following error report: Error in rbind(...) : number of columns of matrices must match (see arg 8) I get the same error message whichever search term I enter between the quotation marks. This function was working fine just a few

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]

Hola! I seem to remember i used to have the same name of argument and default value in argument list to functions, but (rw1.5.1) this seems not to work: > x <- 3 > test <- function(x=x) x*x > test(7) [1] 49 > test() Error in test() : recursive default argument reference here is a code fragment from lm() using the same syntax: else { x <- model.matrix(mt, mf,

Could someone advise me about how to react to the message: * checking R code for possible problems ... NOTE slm: no visible binding for global variable 'response' from R CMD check SparseM with * using R version 2.6.0 Under development (unstable) (2007-09-03 r42749) The offending code looks like this: "slm" <- function (formula, data, weights, na.action, method =

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 trying to fit a curve to some 1 year failure-time data, so that I can extrapolate and predict failure rates up to 3 years. The data is in the general form: Treatment Time Status Treatment A 28 0 Treatment B 28 0 Treatment B 28 0 Treatment A 28

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 ---

Hi, In using the betareg package, I encounter the following error message: Error in lm.wfit(x, linkfun(y), weights, offset = offset) : NA/NaN/Inf in foreign function call (arg 4) Any help will be most appreciated. Thanks in advance. Alex

I'm trying to use stepAIC on sparse matrices, and I need some help. The documentation for slm.fit suggests: slm.fit and slm.wfit call slm.fit.csr to do Cholesky decomposition and then backsolve to obtain the least squares estimated coefficients. These functions can be called directly if the user is willing to specify the design matrix in matrix.csr form. This is often advantageous in large

Full_Name: Roger peng Version: 1.4.0 (2001-12-13) OS: GNU/Linux (Redhat 7.1) Submission from: (NULL) (128.97.86.126) R compiles fine and runs, but when I call the library() function with no arguments I get the error: > library() Error in FUN(X[[2]], ...) : subscript out of bounds make check also fails when running code in base-Ex.R (I'm assuming it fails where library() function is