similar to: Error in x %*% coef(object) : non-conformable arguments

G'morning What does the error message "Error in x %*% coef(object) : non- conformable arguments" indicate when calculating the response values for newdata with a model from bigglm (in package biglm), and how can I debug it? I am attempting to do Monte Carlo simulations, which may explain the loop in the code that follows. After the code I have included the output, which shows that

Dear statistics experts, I'm looking for a way to compare the fit of the following three models: LinModel <- lm(y ~ x) LogModel <- nls(y ~ SSlogis(x, Asym, xmid, scal)) PotModel <- nls(y ~ a * x^n, start=list(a=1, n=1)) I am only interested in whether one of these models has substantial advances in explaining the variance of y. So my original idea was simply to compare the adjusted

Dear List I am new to R and to the list and will try best as I can be clear and concise. My apologies if anything I write contravenes the posting code on this list. I would also like to say I have run through most of the material on the R website before writing this email however, I am stuck. Here is what I want to do and what I have done 1. Read a comma seperated text file into R I have used

Hello there, since I installed CentOS6 few months ago (kept up-to-date using yum), I'm facing very poor performances when writing to USB pendrives. The hardware: a Dell Latitude E6500 laptop (Intel Core Duo P8600 @2.40Ghz), 4Go RAM + 4Go swap, several USB2 pendrives of various brands (less than old, all formatted as vfat). When I perform a copy (with cp or midnight commander, copying big

> I'm trying to use "predict" with a linear mixed-effects logistic > regression model fitted with nlmmPQL from the MASS library. > Unfortunately, I'm getting an error "non-conformable arguments" in > predict.lme, and I would like to understand why. I'd like to briefly describe how I ended up working around this problem. The issue is that predict.lme

>From posts in Sep 2004 and Feb 2005, glm() was raising "Error in x[good, ] * w : non-conformable arrays". I can reproduce this error using: df1 = data.frame(u=1:10, v=rpois(10,10), z=array(1,10,dimnames=list(1:10))) glm(v~u+offset(log(z)), data=df1, family=poisson) -- which seems to be due to the variable z have dimnames. In glm.fit(), the error is

Greetings R-helpers, I am attempting to fit an lme() while specifying a correlation structure, but I'm getting into trouble long before I get to that point. I am receiving the error: Error in y[revOrder] - Fitted : non-conformable arrays It doesn't seem to matter how simple or complex the model I specify is, it always gives this same error message. This makes me suspect something is

I'm trying to use "predict" with a linear mixed-effects logistic regression model fitted with nlmmPQL from the MASS library. Unfortunately, I'm getting an error "non-conformable arguments" in predict.lme, and I would like to understand why. I have used the same call to "predict" with "glm" models without problems. I assume I'm doing

Hi Everyone, I'm running some simulations where eventually I need to table the results. The problem is, that while most simulations I have at least one predicted outcome for each of the six possible categories, sometimes the algorithm assigns all the outcomes and one category is left out. Thus when I try to add the misclassification matrices I get an error. Is there a simple way I can make

I am trying to calculate adjusted means according to a glm model with a Poisson distribution using effect() in the 'effects' package. I've done this many times before with great success, but this time I'm getting an error. My model contains two factors and two covariates/blocks and I would like the adjusted mean and confidence intervals for each factor separately. The model runs

Hello, Consider this function for generalized ridge regression: gre <- function (X,y,D){ n <- dim(X)[1] p <- dim(X)[2] intercept <- rep(1, n) X <- cbind(intercept, X) X2D <- crossprod(X,X)+ D Xy <- crossprod(X,y) bth <- qr.solve(X2D, Xy) } # suppose X is an (nxp) design matrix and y is an (nx1) response vector p <- dim(x)[2] D<- diag(rep(1.5,p)) bt

Hello all, I have generated a principal components regression model using the pcr() function from the PLS package (R version 2.12.0). I am getting a "non-conformable arguments" error when I try to use the predict() function on new data, but only when I try to read in the new data from a separate file. More specifically, when my data looks like this #########training data

Hi, I have a very bizarre problem linking to my Active Directory from my Ubuntu 11.10 system. I have three AD users setup with them belonging to a specific AD group. When I first login a user onto my Ubuntu system, that user is shown to be a member of the group: wbinfo -r <user> And if I show the group members, it also lists my user: wbinfo --gid-info <gid> However - after a

Hi, I got a weird probleme. I have a fileserver running FreeBSD 6.1 and 2 client running Windows XP. Samba version 3.0.23c is installed on FreeBSD server when I'm downloading something from the server to any of my client machine, I got no probleme even with big file, but when I'm uploading from any client machine to the server if the file or folder is bigger than 20mo, after a few

The Wine development release 1.3.33 is now available. What's new in this release (see below for details): - Support for text output in the DIB engine. - Improved support for HTTP proxies. - New version of the Gecko engine. - A number of cursor fixes. - Some fixes in bidirectional text layout. - Various bug fixes. The source is available from the following locations:

Forum, I am attempting to plot weekly horizontal histograms, something very similar to: http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=109 The part of the code used to create this graphic that interest me: # Draw the histograms for (i in 1:length(ages)){ par(new = TRUE) xmin <- -ages[i] + xlim[1] xmax <- xlim[2] - ages[i] ser <- freqs[, i+1] ser <-