Displaying 20 results from an estimated 9000 matches similar to: "glm.fit and predict.glm: error ' no terms component'"
2014 Jan 13
1
predict.glm line 28. Please explain
I imitated predict.glm, my thing worked, now I need to revise. It would
help me very much if someone would explain predict.glm line 28, which says
object$na.action <- NULL # kill this for predict.lm calls
I want to know
1) why does it set the object$na.action to NULL
2) what does the comment after mean?
Maybe I need a pass by value lesson too, because I can't see how changing
that
2005 Apr 13
3
A suggestion for predict function(s)
Maybe a useful addition to the predict functions would be to return the
values of the predictor variables. It just (unless there are problems)
requires an extra line. I have inserted an example below.
"predict.glm" <-
function (object, newdata = NULL, type = c("link", "response",
"terms"), se.fit = FALSE,
2007 Oct 02
1
problems with glm
I am having a couple of problems someone may be able to cast some light on.
Question 1:
I am making a logistic model but when i do this:
glm.model = glm(as.factor(form$finished) ~ ., family=binomial,
data=form[1:150000,])
I get this:
Error in model.frame(formula, rownames, variables, varnames, extras,
extranames, :
variable lengths differ (found for 'barrier')
which is
2000 Feb 17
3
se from predict.glm
I am not sure whether it is a design decision or just an oversight.
When I ask for the standard errors of the predictions with
predict(budwm.lgt,se=TRUE)
where budwm.lgt is a logistic fit of the budworm data in MASS, I got
Error in match.arg(type) : ARG should be one of response, terms
If one is to construct a CI for the fitted binomial probability,
wouldn't it be more natural to do
2008 Dec 16
1
Prediction intervals for zero inflated Poisson regression
Dear all,
I'm using zeroinfl() from the pscl-package for zero inflated Poisson
regression. I would like to calculate (aproximate) prediction intervals
for the fitted values. The package itself does not provide them. Can
this be calculated analyticaly? Or do I have to use bootstrap?
What I tried until now is to use bootstrap to estimate these intervals.
Any comments on the code are welcome.
2005 Jul 25
5
passing formula arguments cv.glm
I am trying to write a wrapper for the last example in help(cv.glm) that
deals with leave-one-out-cross-validation (LOOCV) for a logistic model.
This wrapper will be used as part of a bigger program.
Here is my wrapper funtion :
logistic.LOOCV.err <- function( formu=NULL, data=NULL ){
cost.fn <- function(cl, pred) mean( abs(cl-pred) > 0.5 )
glmfit <- glm(
2010 Sep 07
1
Saving fits (glm, nls) without data
Is there any package that assists in saving and reconstituting glm and
nls fits without bringing along the accompanying data? A quick search
on CRAN didn't turn up anything.
If not, how do other people deal with saving the coefficients of model
fits?
For example, I've run a glm fit that has 23 coefficents on data set that
had 193,008 rows, by the time the fit was called. When I save
2005 Apr 14
0
predict.glm(..., type="response") dropping names (and a propsed (PR#7792)
Here's a patch that should make predict.glm(..., type="response") retain the
names. The change passes make check on our Opteron running SLES9. One
simple test is:
names(predict(glm(y ~ x, family=binomial,
data=data.frame(y=c(1, 0, 1, 0), x=c(1, 1, 0, 0))),
newdata=data.frame(x=c(0, 0.5, 1)), type="response"))
which gives
[1]
2002 Feb 27
1
Bug in glm.fit? (PR#1331)
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
2008 Jun 13
1
Writing a new link for a GLM.
Hi,
I wish to write a new link function for a GLM. R's glm routine does
not supply the "loglog" link. I modified the make.link function adding
the code:
}, loglog = {
linkfun <- function(mu) -log(-log(mu))
linkinv <- function(eta) exp(-exp(-eta))
mu.eta <- function(eta) exp(-exp(-eta)-eta)
valideta <- function(eta) all(eta != 0)
2007 Aug 02
1
simulate() and glm fits
Dear All,
I have been trying to simulate data from a fitted glm using the simulate()
function (version details at the bottom). This works for lm() fits and
even for lmer() fits (in lme4). However, for glm() fits its output does
not make sense to me -- am I missing something or is this a bug?
Consider the following count data, modelled as gaussian, poisson and
binomial responses:
counts
2007 Feb 23
1
Bootstrapping stepAIC() with glm.nb()
Dear all,
I would like to Boostrap the stepAIC() procedure from package MASS for
variety of model objects, i.e.,
fn <- function(object, data, B = 2){
n <- nrow(data)
res <- vector(mode = "list", length = B)
index <- sample(n, n * B, replace = TRUE)
dim(index) <- c(n, B)
for (i in 1:B) {
up.obj <- update(object, data = data[index[, i], ])
2009 Jan 16
1
specifying model terms when using predict
I've recently encountered an issue when trying to use the predict.glm
function.
I've gotten into the habit of using the dataframe$variablename method of
specifying terms in my model statements. I thought this unambiguous
notation would be acceptable in all situations but it seems models
written this way are not accepted by the predict function. Perhaps
others have encountered this
2012 Apr 02
7
Calculating NOEL using R and logistic regression - Toxicology
Hello, I used the glm function in R to fit a dose-response relationship and
then have been using dose.p to calculate the LC50, however I would like to
calculate the NOEL (no observed effect level), ie the lowest dose above
which responses start occurring. Does anyone know how to do this?
[[alternative HTML version deleted]]
2012 Jul 12
3
Add row into a Matrix witout headers from Function
Hi,
Here i have a matrix like this,
OLDMatrix <-
X1 X2 X3
----- ------ ------
22 24 23
25 27 27
10 13 15
the thing is,
im running two function(SUM,COUNT) to get output in another matrix called
NEWMatrix
NEWMatrix <- c("SUM",colSums(OLDMatrix ))
NEWMatrix <- c("COUNT",colSums(!is.na(OLDMatrix
2005 Jun 14
1
New Family object for GLM models...
Dear R-Users,
I wish to create a new family object based on the Binomial family. The only difference will be with the link function. Thus instead if using the 'logit(u)' link function, i plan to use '-log(i-u)'.
So far, i have tried to write the function following that of the Binomial and Negative Binomial families.
The major problem i have here is with the definition of the
2008 Jun 15
1
randomForest, 'No forest component...' error while calling Predict()
Dear R-users,
While making a prediction using the randomForest function (package
randomForest) I'm getting the following error message:
"Error in predict.randomForest(model, newdata = CV) : No forest component
in the object"
Here's my complete code. For reproducing this task, please find my 2 data
sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).
2009 Jan 23
4
glm binomial loglog (NOT cloglog) link
I would like to do an R glm() with
family = binomial(link="loglog")
Right now, the cloglog link exists, which is nice when the data have a
heavy tail to the left. I have the opposite case and the loglog link
is what I need. Can someone suggest how to add the loglog link onto
glm()? It would be lovely to have it there by default, and it
certainly makes sense to have the two opposite
2008 Apr 04
2
predict.glm & newdata
Hi all -
I'm stumped by the following
mdl <- glm(resp ~ . , data = df, family=binomial, offset = ofst) WORKS
yhat <- predict(mdl) WORKS
yhat <- predict(mdl,newdata = df) FAILS
Error in drop(X[, piv, drop = FALSE] %*% beta[piv]) :
subscript out of bounds
I've tried without offset, quoting binomial. The offset variable ofst IS in df.
Previous postings indicate possible
2004 Mar 01
1
glm logistic model, prediction intervals on impact af age 60 compared to age 30
Dear R-list.
I have done a logistic glm using Age as explanatory variable for some
allergic event.
#the model
model2d<-glm(formula=AEorSAEInfecBac~Age,family=binomial("logit"),data=emrisk)
#predictions for age 30 and 60
preds<-predict(model2d,data.frame(Age=c(30,60)),se.fit=TRUE)
# prediction interval