Displaying 20 results from an estimated 10000 matches similar to: "partial proportional odds model (PPO)"
2007 Jan 07
1
Partial proportional odds logistic regression
R-experts:
I would like to explore the partial proportional odds models of Peterson
and Harrell (Applied Statistics 1990, 39(2): 205-217) for a dataset that
I am analyzing. I have not been able to locate a R package that
implements these models. Is anyone aware of existing R functions,
packages, etc... that might be used to implement the partial
proportional odds models?
Brant Inman
2007 Jul 16
2
Error while fitting Partial Proportional Odds model using vglm
Dear R developers:
I am trying to fit a PPO model using vglm from the library VGAM, and get an
error while executing the code. Here is the data, code, and error:
Data: first row is the column names. a = age, and 1,2,3, 4 and 5 are
condition grades.
a 1 2 3 4 5
1 1 0 0 0 0
2 84 2 7 10 2
3 16 0 6 6 2
4 13 0 3 4 0
5 0 0 0 1 0
Library(VGAM)
2005 Sep 05
1
convergence for proportional odds model
Hey, everyone,
I am using proportional odds model for ordinal responses in dose-response experiments. For some samll data, SAS can successfully provide estimators of the parameters, but the built-in function polr() in R fails. Would you like to tell me how to make some change so I can use polr() to obtain the estimators? Or anyone can give me a hint about the conditions for the existance of MLE
2009 Feb 24
1
polr (MASS): score test for proportional odds model
For the following model,
library(vcd)
arth.polr <- polr(Improved ~ Sex + Treatment + Age, data=Arthritis)
summary(arth.polr)
where Improved is an ordered, 3-level response I'm looking for a
*simple* way to test
the validity of the proportional odds assumption, typically done via a
score test
for equal slopes/effects over the predictors.
I do find a po.test= option in the repolr package
2012 Oct 23
1
Testing proportional odds assumption in R
I want to test whether the proportional odds assumption for an ordered
regression is met.
The UCLA website points out that there is no mathematical way to test the
proportional odds assumption (http://www.ats.ucla.edu/stat//R/dae/ologit.htm),
and use graphical inspection ("We were unable to locate a facility in R to
perform any of the tests commonly used to test the parallel slopes
2007 Aug 02
1
proportional odds model
Hi all!!
I am using a proportinal odds model to study some ordered categorical
data. I am trying to predict one ordered categorical variable taking
into account only another categorical variable.
I am using polr from the R MASS library. It seems to work ok, but I'm
still getting familiar and I don't know how to assess goodness of fit.
I have this output, when using response ~ independent
2013 Jan 24
4
Difference between R and SAS in Corcordance index in ordinal logistic regression
lrm does some binning to make the calculations faster. The exact calculation
is obtained by running
f <- lrm(...)
rcorr.cens(predict(f), DA), which results in:
C Index Dxy S.D. n missing
0.96814404 0.93628809 0.03808336 32.00000000 0.00000000
uncensored Relevant Pairs Concordant Uncertain
32.00000000
2003 May 28
2
Ordinal data - Regression Trees & Proportional Odds
I have a data set w/ an ordinal response taking on one of 10 categories.
I am considering using polr to fit a cumulative logits model. I
previously fit the model in SAS (using proc logistic) which provides a
test for the proportional odds assumption (p < 0.001 for the test). Are
there simple diagnostic plots that can be used to look at the validity
of this assumption and possibly help w/
2011 Jun 28
1
Testing the proportional odds assumption of an ordinal generalized estimating equations (GEE) regression model
Dear list members,
I am estimating an ordinal generalized estimating equations (GEE) regression model on repeated measurements data.
Is there any way (preferably in R) to test the proportional odds assumption of this model?
Thanks in advance.
Andreas
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2005 Apr 27
0
Fitting a kind of Proportional Odds Modell using nlme, polr, lrm or ordgee
Hello,
I'm trying to fit a special kind of proportional odds model from:
Whitehead et al. (2001). Meta-analysis of ordinal outcome using
individual patient data. Statistics in medicine 20: 2243-2260. (model 2)
The data are as follows:
library(nlme)
library(geepack)
library(Design)
library(MASS)
options(contrasts=c("contr.SAS","contr.poly"))
counts <-
2008 Jun 30
2
difference between MASS::polr() and Design::lrm()
Dear all,
It appears that MASS::polr() and Design::lrm() return the same point
estimates but different st.errs when fitting proportional odds models,
grade<-c(4,4,2,4,3,2,3,1,3,3,2,2,3,3,2,4,2,4,5,2,1,4,1,2,5,3,4,2,2,1)
score<-c(525,533,545,582,581,576,572,609,559,543,576,525,574,582,574,471,595,
557,557,584,599,517,649,584,463,591,488,563,553,549)
library(MASS)
library(Design)
2007 Mar 23
4
Effect display of proportional odds model
Dear useRs,
I very much like the effect display of the proportional odds model on
page 29 (Figure 8) of the following paper by John Fox:
http://socserv.mcmaster.ca/jfox/Papers/logit-effect-displays.pdf
It really gives a very concise overview of the model. I would like to
use it to illustrate the proportional odds mixed models we fit here for
a project on Diabetes but I can't seem to reproduce
2011 May 15
5
Question on approximations of full logistic regression model
Hi,
I am trying to construct a logistic regression model from my data (104
patients and 25 events). I build a full model consisting of five
predictors with the use of penalization by rms package (lrm, pentrace
etc) because of events per variable issue. Then, I tried to approximate
the full model by step-down technique predicting L from all of the
componet variables using ordinary least squares
2011 Nov 12
2
Odds ratios from lrm plot
The code
library(Design)
f <- lrm(y~x1+x2+x1*x2, data=data)
plot(f)
produces a plot of log odds vs x2 with 0.95 confidence intervals. How do I
get a plot of odds ratios vs x2 instead?
Thanks
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2008 Jan 05
1
Likelihood ratio test for proportional odds logistic regression
Hi,
I want to do a global likelihood ratio test for the proportional odds
logistic regression model and am unsure how to go about it. I am using
the polr() function in library(MASS).
1. Is the p-value from the likelihood ratio test obtained by
anova(fit1,fit2), where fit1 is the polr model with only the intercept
and fit2 is the full polr model (refer to example below)? So in the
case of the
2005 Mar 16
1
Fitting mixed proportional odds model in R?
Is there a way to fit mixed proportional odds models in R?
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2012 Jun 20
2
Odds Ratios in rms package
Hi,
I'm using the rms package to do regression analysis using the lrm
function. Retrieving odds ratios is possible using summary.rms. However,
I could not find any information on how exactly the odds ratios for
continuous variables are calculated. It doesn't appear to be the odds
ratio at 1 unit increase, because the output of summary.rms did not
match the coefficient's value.
E.g.
2011 Nov 03
0
L1 penalization for proportional odds logistic regression
Dear community,
I am currently attempting to perform a (L1) penalized ordinal logistic
regression with proportional odds. For the moment I only found R packages
allowing to perform forward or backward continuation ratio model with
several penalizations.
Does anyone have a clue of what R package I could use ? I am not even quite
sure that penalized logistic regression with proportional odds has
2007 May 10
1
Follow-up about ordinal logit with mixtures: how about 'continuation ratio' strategy?
This is a follow up to the message I posted 3 days ago about how to
estimate mixed ordinal logit models. I hope you don't mind that I am
just pasting in the code and comments from an R file for your
feedback. Actual estimates are at the end of the post.
### Subject: mixed ordinal logit via "augmented" data setup.
### I've been interested in estimating an ordinal logit model
2007 May 08
3
ordered logistic regression with random effects. Howto?
I'd like to estimate an ordinal logistic regression with a random
effect for a grouping variable. I do not find a pre-packaged
algorithm for this. I've found methods glmmML (package: glmmML) and
lmer (package: lme4) both work fine with dichotomous dependent
variables. I'd like a model similar to polr (package: MASS) or lrm
(package: Design) that allows random effects.
I was