similar to: question about how summary.lm works

R developers, I have a first attempt to make an aov function. Eventually I want to build in Error() structure, but first I am trying to get this presentable for balanced data with only a single stratum, just using residual error. I am following R. M. Heiberger's Computation for the Analysis of Designed Experiments, Wiley (1989) I a using a wrapper (aov.bal) to call the

Hi - I am doing a monte carlo experiment that requires to do a linear regression of a matrix of vectors of dependent variables on a fixed set of covariates (one regression per vector). I am wondering if anyone has any idea of how to speed up the computations in R. The code follows: #regression function #Linear regression code qreg <- function(y,x) { X=cbind(1,x) m<-lm.fit(y=y,x=X)

thank you, achim. I will try chol2inv. sandwich is a very nice package, but let me make some short suggestions. I am not a good econometrician, so I do not know what prewhitening is, and the vignette did not explain it. "?coeftest" did not work after I loaded the library. automatic bandwidth selection can be a good thing, but is not always. as to my own little function, I like the

I'm running a huge number of regressions in a loop, so I tried lm.fit for a speedup. However, I would like to be able to calculate the t-stats for the coefficients. Does anyone have some functions for calculating the regression summary stats of an lm.fit object? Thanks, Whit

The solve function in r-patched has been changed so that it applies a tolerance when using Lapack routines to calculate the inverse of a matrix or to solve a system of linear equations. A tolerance has always been used with the Linpack routines but not with the Lapack routines in versions 1.7.x and 1.8.0. (You can use the optional argument tol = 0 to override this check for computational

Hi, I'm trying to use Frank Harrell's Design:::ols function to do regression of y (numeric) on the interaction of two factors (x1 and x2), but Lapack throws an error: > library(Design) ... > load(url("http://www.csse.unimelb.edu.au/~gabraham/x")) > ols(y ~ x1 * x2, data=x) Error in chol2inv(fit$qr$qr) : 'size' cannot exceed nrow(x) = 20 > traceback()

I installed R in (what I believe is) the standard way by adding the following line to /etc/apt/sources.list deb http://cran.us.r-project.org/bin/linux/ubuntu precise/ All was well, until I recently upgraded from 2.15.1 to 2.15.2 by running sudo apt-get update sudo apt-get upgrade Now, when I try to do simple things, I get an error. e.g. R> PP.test(rnorm(1000)) Error in

Dear BioConductor and R fellow users I apologize in advance for double posting, but I am not sure which list would actually be best fit for this message. I am experiencing a weird error with my R installation on Ubuntu 10.04.4 (LTS) 64bit: When I run R on the terminal everything goes smoothly: $R R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation

dear R experts: ?I am writing my own little newey-west standard error function, with heteroskedasticity and arbitrary x period autocorrelation corrections. ?including my function in this post here may help others searching for something similar. it is working quite well, except on occasion, it complains that Error in solve.default(crossprod(x.na.omitted, x.na.omitted)) : system is

Hello, I was wondering if someone knows the formula used by the function lm to compute the t-values. I am trying to implement a linear regression myself. Assuming that I have K variables, and N observations, the formula I am using is: For the k-th variable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation.

Good morning to all, I am encountering a blocking issue when using the function 'breackpoints' from package 'strucchange'. *Context:* I use a data frame, 248 observations of 5 variables, no NA. I compute a linear model, as y~x1+...+x4 x4 is a dummy variable (0 or 1). I want to check this model for structural changes. *Process & issues:* *First, I used function Fstats.* It

I usually ran different statistical analysis in R with routines that use lapack like gam() lm(), etc but after several updates of libraries the following error appears: library(mgcv) This is mgcv 1.7-22. For overview type 'help("mgcv-package")'. model <- with(chlaR,gam(ClorMAX ~ s(DegDay_NM))) Error en eigen(St, symmetric = TRUE) : lapack routines cannot be loaded Adem?s:

In summary.glm I'm trying to get a better feel for the z output. The following lines can be found in the function 1 if (p > 0) { 2 p1 <- 1:p 3 Qr <- object$qr 4 coef.p <- object$coefficients[Qr$pivot[p1]] 5 covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) 6 dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))

Dear R users, I came up with some simple functions to give me the standard betas and tolerance values from a predefined lm() model. I have been trying to insert the results from these functions into the coefficients matrix in a modified summary.lm function that I'm calling summary2 (I'd never edit the summary.lm function directly!!). I managed to get the results inserted into the

Hi, I'm having problems using ACML with R. I made two changes in config.site by setting LDFLAGS="-L/opt/acml3.1.0/gnu64/lib" BLAS_LIBS="-lacml" ./config and make go through but when I try to use the lm() function, I get the error message Error in chol2inv(Qr$qr[p1, p1, drop = FALSE]) : lapack routines cannot be loaded In addition: Warning message: unable to load

--Multipart_Mon_Nov_24_14:51:09_1997-1 Content-Type: text/plain; charset=US-ASCII >>>>> "PaulG" == Paul Gilbert <pgilbert@bank-banque-canada.ca> writes: MM> The code is basically in V&R 1 and 2; V&R2 on p.167. I have it as a MM> C function that I used to dyn.load into S-plus in order MM> to prove that S-plus was using it.

I encountered the following problem in R-2.4.0 for Windows binary downloaded from CRAN (data from R-help post by Ethan Johnsons). I was also using the contributed binary Rblas.dll for Intel P4 chip. The problem doesn't occur with the default Rblas.dll. x=c(3.05176E-05, 0.000457764, 0.003204346, 0.0138855, 0.04165649, 0.09164429, 0.1527405, 0.1963806, 0.1963806, 0.1527405, 0.09164429,

i am trying to use matinv from the Design package to compute the generalized inverse of the normal equations of a 3x3 design via the sweep operator. That is, for the linear model y = ? + x1 + x2 + x1*x2 where x1, x2 are 3-level factors and dummy coding is being used the matrix to be inverted is X'X = 9 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 3 3 0 0 1 1 1 1 0 0 1 0 0 1 0 0 3 0 3 0 1 1 1 0 1 0 0 1

Hi, I am running simulations that does multiple comparisons to control. For each simulation, I need to model 7 nls functions. I loop over 7 to do the nls using try if try fails, I break out of that loop, and go to next simulation. I get warnings on nls failures, but the simulation continues to run, except when the internal call (internal to nls) of the chol2inv fails.

Here's my little discussion example for a quadratic regression: http://pj.freefaculty.org/R/WorkingExamples/regression-quadratic-1.R Students press me to know the benefits of poly() over the more obvious regression formulas. I think I understand the theory on why poly() should be more numerically stable, but I'm having trouble writing down an example that proves the benefit of this. I