Displaying 20 results from an estimated 10000 matches similar to: "glm.fit: fitted probabilities numerically 0 or 1 occurred for a continuous variable?"
2012 Mar 20
2
glm.fit: fitted probabilities numerically 0 or 1 occurred?
Hi all,
I am doing bootstrap with logistic regression by using glm function, and I
get the errors;
glm.fit: fitted probabilities numerically 0 or 1 occurred and
glm.fit: algorithm did not converge
I have read some things about this issue in the mailing list. I can guess
what was the problem. My data contains one or may be two outliers. Does the
error occur due to these extreme values or
2011 Dec 01
1
logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred
Sorry if this is a duplicate: This is a re-post because the pdf's mentioned
below did not go through.
Hello,
I'm new'ish to R, and very new to glm. I've read a lot about my issue:
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
...including:
http://tolstoy.newcastle.edu.au/R/help/05/07/7759.html
2008 Mar 11
2
glm.fit: "fitted probabilities numerically 0 or 1 occurred"
Hi,
could anyone explain to me what this warning message
exactly means and what the consequences are?
Is it due to the fact that there are very extreme
observations / outliers included or what is the reason
for it?
Thanks so much,
Werner
Machen Sie Yahoo! zu Ihrer Startseite. Los geht's:
2008 Aug 31
1
Fitted probabilities in conditional logit regression
Dear R-help,
I'm doing conditional logit regression for a discrete choice model.
I want to know whether there's a way to get the fitted probabilities. In
Stata, "predict" works for clogit, but it seems that in R "predict" does
not.
Thank you very much!
Best wishes.
Sincerely,
Min
--
Min Chen
Graduate Student
Department of Agricultural,
2003 Jun 15
1
Fitted probabilities from glmmPQL?
Hello All,
Specifying 'type = "response"' when using predict() on a
model fit using glm(...,family="binomial") returns fitted
probabilities.
Is it possible to get the same from a model object
fit using glmmPQL() ?
Thanks in advance,
Rob
_____________________________________________________
Rob Keefe Lab: (208) 885-5165
M.S. student
2005 Apr 15
1
Range in probabilities of a fitted lrm model (Y~X)
Dear R-list,
Is there a function or technique by which the probability (or log odds)
range of a logistic model (fit <- lrm(Y~X)) can be derived?
The aim is to obtain min & max of the estimated probabilities of Y.
Could summary.Design() be used for that or is there another method/trick?
Thanks,
Jan
_______________________________________________________________________
ir. Jan
2007 Mar 26
1
fitted probabilities in multinomial logistic regression are identical for each level
I was hoping for some advice regarding possible explanations for the
fitted probability values I obtained for a multinomial logistic
regression. The analysis aims to predict whether Capgras delusions
(present/absent) are associated with group (ABH, SV, homicide; values
= 1,2,3,), controlling for previous violence. What has me puzzled is
that for each combination the fitted probabilities are
2008 May 16
1
SE of difference in fitted probabilities from logistic model.
I am fitting a logistic binomial model of the form
glm(y ~ a*x,family=binomial)
where a is a factor (with 5 levels) and x is a continuous predictor.
To assess how much ``impact'' x has, I want to compare the fitted
success probability
when x = its maximum value with the fitted probability when x = its
mean value.
(The mean and the max are to be taken by level of the factor
2000 Jan 05
0
bug in glm.fit (PR#395)
Dear R-team
There seems to be a bug in glm.fit - I got the following error message:
> > > + Error in names<-.default(*tmp*, value = ynames) : names attribute must be the same length as the vector
In addition: Warning messages:
1: fitted probabilities of 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y,
2: fitted probabilities of 0 or 1 occurred
2004 Nov 11
1
glm.fit warning message
I am feeling my way in the use of GLM's and have come across a warning whilst manually simplifying a model with interaction terms, removing terms one at a time from the maximum model (R1.9.0).
> model<-glm(midpnts~(AET+tempave+tempvar+MDE+sqrtarea)^2+Lat,family=poisson,weights=weightS)
> model2<-update(model,~.-tempave:tempvar)
Warning message:
fitted rates numerically 0
2008 Jan 08
3
GAM, GLM, Logit, infinite or missing values in 'x'
Hi,
I'm running gam (mgcv version 1.3-29) and glm (logit) (stats R 2.61) on
the same models/data, and I got error messages for the gam() model and
warnings for the glm() model.
R-help suggested that the glm() warning messages are due to the model
perfectly predicting binary output. Perhaps the model overfits the data? I
inspected my data and it was not immediately obvious to me (though I
2005 Jan 28
3
GLM fitting
DeaR R-useRs,
I'm trying to fit a logist model with these data:
> dati
y x
1 1 37
2 1 35
3 1 33
4 1 40
5 1 45
6 1 41
7 1 42
8 0 20
9 0 21
10 0 25
11 0 27
12 0 29
13 0 18
I use glm(), having this output:
> g<-glm(y~x,family=binomial,data=dati)
Warning messages:
1: Algorithm did not converge in: glm.fit(x = X, y =
Y, weights = weights, start = start, etastart =
2005 Jul 02
2
Is it possible to use glm() with 30 observations?
I have a very simple problem. When using glm to fit
binary logistic regression model, sometimes I receive
the following warning:
Warning messages:
1: fitted probabilities numerically 0 or 1 occurred
in: glm.fit(x = X, y = Y, weights = weights, start =
start, etastart = etastart,
2: fitted probabilities numerically 0 or 1 occurred
in: glm.fit(x = X, y = Y, weights = weights, start =
start,
2005 Aug 05
1
question regarding logit regression using glm
I got the following warning messages when I did a
binomial logit regression using glm():
Warning messages:
1: Algorithm did not converge in: glm.fit(x = X, y =
Y, weights = weights, start = start, etastart =
etastart,
2: fitted probabilities numerically 0 or 1 occurred
in: glm.fit(x = X, y = Y, weights = weights, start =
start, etastart = etastart,
Can some one share your thoughts on how to
2005 Nov 08
1
Interpretation of output from glm
I am fitting a logistic model to binary data. The response variable is a
factor (0 or 1) and all predictors are continuous variables. The main
predictor is LT (I expect a logistic relation between LT and the
probability of being mature) and the other are variables I expect to modify
this relation.
I want to test if all predictors contribute significantly for the fit or not
I fit the full
2006 Jan 31
1
warnings in glm (logistic regression)
Hello R users
I ran more than 100 logistic regression analyses. Some of the analyses gave
me this kind warning below.
###########################################################
Warning messages:
1: algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights,
start = start, etastart = etastart, ...
2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y =
Y,
2009 Feb 14
1
glm problem
Hello,
I have a weird problem here. What I want to do is that I need to draw 1000
samples from a matrix, and use glm on them.
when I used this command, it runs without the problem
> qdata.glm = glm(X258 ~ ., family = binomial, data =
q2data[sample(dim(q2data)[1], 1000), ])
but if I drew the sample first and run glm() on that sample, it gave a
warning.
> qdata.sample =
2011 Dec 22
1
Error message with glm
I'm working on a logistic regression in R with the car package but keep
getting the following error message.
It's only and warning and not an error, but I'm just not sure how to
resolve the issues.
glm.fit: algorithm did not converge
glm.fit: fitted probabilities numerically 0 or 1 occurred
d1 = data.frame(mwin=c(mwin), mbid=c(mbid))
m1 = zelig(mwin ~ mbid, data=d1,
2006 Feb 17
2
Something changed and glm(..., family=binomial) doesn't work now
I ran logistic regression models last week using glm
(...,family=binomial) and got a set of results. Since then I have
loaded the Epi package for ROC analysis. Now when I run those same
models I get completely different results, with most being:
Warning message:
fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y
= Y, weights = weights, start = start, etastart = etastart,
2017 Jul 25
0
Using nls.lm to fit a non-continuous dates range
Dear R users,
Can I fit nls.lm to a non-continuous date data. looked at previous
examples but still not able to fit the model to my data. There are 25
rows of observations as below;
df <- data.frame(Date=as.Date(rownames(df),'%m/%d/%Y'),Y=df$height)
df$days <- as.numeric(df$Date - df[1,]$Date)
head(df)
Date Y days
1 2009-12-01 0.2631250 0
2 2010-01-08 0.4436012