Displaying 20 results from an estimated 10000 matches similar to: "predict.lme / glmmPQL: "non-conformable arguments""
2006 Jan 30
1
predict.lme / nlmmPQL: "non-conformable arguments"
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
2006 May 30
0
(PR#8905) Recommended package nlme: bug in predict.lme when an independent variable is a polynomial
Many thanks for your very useful comments and suggestions.
Renaud
2006/5/30, Prof Brian Ripley <ripley at stats.ox.ac.uk>:
> On Tue, 30 May 2006, Prof Brian Ripley wrote:
>
> > This is not really a bug. See
> >
> > http://developer.r-project.org/model-fitting-functions.txt
> >
> > for how this is handled in other packages. All model-fitting in R used =
2006 May 27
1
Recommended package nlme: bug in predict.lme when an independent variable is a polynomial (PR#8905)
Full_Name: Renaud Lancelot
Version: Version 2.3.0 (2006-04-24)
OS: MS Windows XP Pro SP2
Submission from: (NULL) (82.239.219.108)
I think there is a bug in predict.lme, when a polynomial generated by poly() is
used as an explanatory variable, and a new data.frame is used for predictions. I
guess this is related to * not * using, for predictions, the coefs used in
constructing the orthogonal
2007 Jan 30
0
lme : Error in y[revOrder] - Fitted : non-conformable arrays
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
2009 Oct 26
1
GLMMPQL and negbinomial: trouble with the X-axis in PREDICT
I'm having some difficulty with graphing outputs of a GLM model I've
been working. I have count data for both my predictor (only 1) and
response variables, and I have pseudoreplication which I've modeled as
a random effect. The odTest() from pscl:: indicated that the negative
binomial distribution fit better than Poisson, and I then proceeded by
estimating theta from glm.nb.
My
2004 Mar 22
0
solved mystery of difference between glmmPQL and lme
I asked a few days ago about the difference in results I saw between the
MASS function glmmPQL (due to Venables and Ripley) and the lme function
from the package nlme (due to Pinheiro and Bates). When the two tools
apply to the same model (gaussian, link=identity, correlation=AR1), I
was getting different results and wondered if there was an argument in
favor of one or the other.
Several
2004 Mar 20
1
contrast lme and glmmPQL and getting additional results...
I have a longitudinal data analysis project. There are 10 observations
on each of 15 units, and I'm estimating this with randomly varying
intercepts along with an AR1 correction for the error terms within
units. There is no correlation across units. Blundering around in R
for a long time, I found that for linear/gaussian models, I can use
either the MASS method glmmPQL (thanks to
2010 Jan 23
1
(nlme, lme, glmmML, or glmmPQL)mixed effect models with large spatial data sets
Hi,
I have a spatial data set with many observations (~50,000) and would like to
keep as much data as possible. There is spatial dependence, so I am
attempting a mixed model in R with a spherical variogram defining the
correlation as a function of distance between points. I have tried nlme,
lme, glmmML, and glmmPQL. In all case the matrix needed (seems to be
(N^2)/2 - N) is too large for my
2005 Jan 05
0
lme, glmmPQL, multiple random effects
Hi all -
R2.0.1, OS X
Perhaps while there is some discussion of lme going on.....
I am trying to execute a glmm using glmmPQL from the MASS libray, using
the example data set from McCullagh and Nelder's (1989, p442) table
14.4 (it happens to be the glmm example for GENSTAT as well). The data
are binary, representing mating success (1,0) for crosses between males
and females from two
2006 Jan 10
0
bug in either glmmPQL or lme/lmer
I know it's conventional to report bugs to the maintainer, but I'm not sure
which package actually contains this bug(s), so I apologize for sending this
to the list at large. I see the bug under both R 2.1.1, and R 2.2.1. (I sent
a related message a while ago, but this one has more detail.)
library(MASS)
library(nlme)
fit.model <- function(il, model.family) {
cs <-
2009 Jul 16
0
how to get means and confidence limits after glmmPQL or lmer
R,
I want to get means and confidence limits on the original scale for
the treatment effect after running a mixed model.
The data are:
response<-c(16,4,5,8,41,45,10,15,11,3,1,64,41,23,18,16,10,22,2,3)
2011 Jun 06
0
lme, stepAIC, predict: scope and visibility
Hello all,
I've run into a problem where I can't run predict.lme on an object simplified via a stepAIC. A similar post has been recorded on this list:
https://stat.ethz.ch/pipermail/r-help/2008-May/162047.html
but in my case, I'm going to great lengths to not repeat that poster's error and still coming up short. Any advice would be much appreciated. It would seem that, after
2012 Jan 09
1
glmmPQL and predict
Is the labeling/naming of levels in the documentation for the
predict.glmmPQL function "backwards"? The documentation states "Level
values increase from outermost to innermost grouping, with level zero
corresponding to the population predictions". Taking the sample in
the documentation:
fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID,
family =
2008 Oct 13
0
gamm() and predict()
Dear All,
I have a query relating to the use of the ?predict? and ?gamm? functions. I am dealing with large (approx. 5000) sets of presence/absence data, which I am trying to model as a function of different of environmental covariates. Ideally my models should include individual and colony as random factors. I have been trying to fit binomial models using the gamm function to achieve this. For
2011 Jan 07
0
Error in x %*% coef(object) : non-conformable arguments
Hello, and thanks in advance!
What does the error message "Error in x %*% coef(object) : non-
conformable arguments" when indicate when predicting 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 somewhat interesting loop which follows. After the code I
have included the output,
2006 Apr 06
1
polynomial predict with lme
Does lme prediction work correctly with poly() terms?
In the following simulated example, the predictions
are wildly off.
Or am I doing something daft?
Milk yield for five cows is measured weekly for 45 weeks.
Yield is simulated as cubic function of weekno + random
cow effect (on intercept) + residual error.
I want to recover an estimate of the fixed curve.
###############
library(nlme)
2011 Apr 18
2
Predicting with a principal component regression model: "non-conformable arguments" error
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
2006 Mar 24
1
predict.glmmPQL Problem
Dear all,
for a cross-validation I have to use predict.glmmPQL() , where the
formula of
the corresponding glmmPQL call is not given explicitly, but constructed
using as.formula.
However, this does not work as expected:
x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2)
mydata<-data.frame(x1,x2,y)
library(MASS)
# works as expected
model1<-glmmPQL(y~x1, ~1 | factor(x2),
2005 Jan 03
0
LME-glmmPQL formulation
Hi all -
R2.0.1 on OSX;MASS library;nlme library
I am trying to emulate the solution to a problem set that has normally
been run in Genstat, using R. The problem that I am having at the
moment is with the following glmm question (using glmmPQL from the MASS
library):
"We have two different forest habitats (first rotation thicket, and
high forest) which we want to survey for the
2003 Jul 14
1
methods help and glmmPQL
Dear All,
I would like to ask you to help me with my memeory. I remember using some
function that would list all the possible methods I could apply to an
object. Say, if I had an object of
class=lme,
it would tell me that that I could do stuff like
qqnorm(myobjct), or VarCorr(myobject). In general, a very complete list.
I though this list of all possible methods would pop out by typing