Displaying 20 results from an estimated 8000 matches similar to: "Interaction Terms versus Interaction Effects in logistic regression"
2010 Apr 28
1
Interaction terms in logistic regression using glm
I recently became aware of the article by Ai and Norton (2003) about
how interaction terms are problematic in nonlinear regression (such as
logistic regression). They offer a correct way of estimating
interaction effects and their standard errors.
My question is: Does the glm() function take these corrections into
account when estimating interaction terms for a logistic regression
2004 Nov 11
1
polr probit versus stata oprobit
Dear All,
I have been struggling to understand why for the housing data in MASS
library R and stata give coef. estimates that are really different. I also
tried to come up with many many examples myself (see below, of course I
did not have the set.seed command included) and all of my
`random' examples seem to give verry similar output. For the housing data,
I have changed the data into numeric
2007 Nov 15
3
not R question : alternative to logistic regression
I was just curious if anyone knew of an alternative model to logistic regression where the probabilities seems pretty linear to the predictor rather than having that S shape that probit and logit assume.
Maybe there is there some kind of other GLM that could accomplish that. Any textbook references or suggestions
are appreciated. I have most of the texts but if someone knows of a text that talks
2012 Apr 07
0
Resumen de R-help-es, Vol 38, Envío 13
2012/4/7 <r-help-es-request@r-project.org>
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2012 Mar 21
0
multivariate ordinal probit regression vglm()
Hello, all.
I'm investigating the rate at which skeletal joint surfaces pass
through a series of ordered stages (changes in morphology). Current
statistical methods in this type of research use various logit or
probit regression techniques (e.g., proportional odds logit/probit,
forward/backward continuation ratio, or restricted/unrestricted
cumulative probit). Data typically include the
2008 Apr 09
0
Endogenous variables in ordinal logistic (or probit) regression
A student brought this question to me and I can't find any articles or
examples that are directly on point.
Suppose there are 2 ordinal logistic regression models, and one wants
to set them into a simultaneous equation framework. Y1 might be a 4
category scale about how much the respondent likes the American Flag
and Y2 might be how much the respondent likes the Republican Party in
America.
2013 Feb 06
2
calculating odds ratio in logistic regression with interaction terms
Dear all,
How can i obtain odds ratio in logistic regression when the model
contains interaction terms in R?
how can i obtain OR for a special case?
Thanks in advance for any help.
Amin
2003 Oct 14
0
Updated package: Boolean v1.03
Version 1.03 of the R package boolean has been uploaded to CRAN and is
now available.
boolean implements partial-observability logit and probit models for
testing Boolean hypotheses. It permits researchers to model the
probability of the occurrence of a given outcome as a complex function
of the probabilities that other outcomes will occur (or other
conditions will be fulfilled). For
2003 Oct 14
0
Updated package: Boolean v1.03
Version 1.03 of the R package boolean has been uploaded to CRAN and is
now available.
boolean implements partial-observability logit and probit models for
testing Boolean hypotheses. It permits researchers to model the
probability of the occurrence of a given outcome as a complex function
of the probabilities that other outcomes will occur (or other
conditions will be fulfilled). For
2008 Jan 03
1
GLM results different from GAM results without smoothing terms
Hi, I am fitting two models, a generalized linear model and a generalized
additive model, to the same data. The R-Help tells that "A generalized
additive model (GAM) is a generalized linear model (GLM) in which the linear
predictor is given by a user specified sum of smooth functions of the
covariates plus a conventional parametric component of the linear
predictor." I am fitting the GAM
2005 Nov 21
2
Multinomial Nested Logit package in R?
Dear R-Help,
I'm hoping to find a Multinomial Nested Logit package in R. It would
be great to find something analogous to "PROC MDC" in SAS:
> The MDC (Multinomial Discrete Choice) procedure analyzes models
> where the
> choice set consists of multiple alternatives. This procedure
> supports conditional logit,
> mixed logit, heteroscedastic extreme value,
2018 Mar 19
4
Struggling to compute marginal effects !
Dear Oscar,
and any other R-project person,
Can you please help me to figure out the meaning of the following error
message in red ?
Error in eval(predvars, data, env) :
numeric 'envir' arg not of length one
I computed ordered logit models using 'polr' in R (I just followed the
guidance a handout I found on princeton.edu about logit, probit and
multinomial logit models) . The
2010 Oct 31
1
Questions about Probit Analysis
Dear All,
I have some questions about probit regressions.
I saw a nice introduction at
http://bit.ly/bU9xL5
and I mainly have two questions.
(1) The first is almost about data manipulation. Consider the following
snippet
##################################################
mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/binary.csv"))
names(mydata) <-
2004 Jun 12
2
ordered probit or logit / recursive regression
> I make a study in health econometrics and have a categorical
> dependent variable (take value 1-5). I would like to fit an ordered
> probit or ordered logit but i didn't find a command or package who
> make that. Does anyone know if it's exists ?
R is very fancy. You won't get mundane things like ordered probit off
the shelf. (I will be very happy if someone will show
2004 Nov 30
1
(no subject)
Hello,
I am trying to estimate a choice model with varying choice set for each
individual.
I would like to fit different kinds of model (logit ,nested logit,
probit...).
So far I have found that package *mnp* allows me to estimate a probit model
with varying choice set.
But for estimation of a logit model, I have only found function *multinom*
of package *nnet* which does not seem to allow for
2020 Apr 13
0
Poor family objects error messages
Hello,
The following code:
> binomial(identity)
Generates an error message:
Error in binomial(identity) :
link "identity" not available for binomial family; available links are ?logit?, ?probit?, ?cloglog?, ?cauchit?, ?log?
While :
> binomial("identity")
Yields an identity-binomial object that works as expected with stats::glm
The error in the first example mislead
2004 Jun 30
1
interval regression
Hi,
does anyone have a quick answer to the question of how to carry out
interval regression in R. I have found "ordered logit" and "ordered
probit" as well as multinomial logit etc. The thing is, though, that I
want to apply logit/probit to interval-coded data and I know the cell
limits which are used to turn the quantitative response into an ordered
factor. Hence, it does
2009 May 07
0
GAM ordered probit
Dear All,
Anyone know if there is a package that fits Generalized Linear Models(GAM)
to data with ordered dependent variable(response) ?
Simon Wood's mgcv has probit, logit,... other links, however, I could not
find a way to do GAM *ordered *probit. Yee's VGAM claims to fit ordinal
proportional odds model(cumulative logit model) (see:
http://www.stat.auckland.ac.nz/~yee/VGAM/) but I
2005 Oct 12
2
linear mixed effect model with ordered logit/probit link?
Hello,
I'm working on the multiple categorical data (5-points scale) using linear
mixed effect model and wondering if anyone knows about or works on the
linear mixed effect model with ordered logit or probit link.
I found that the "lmer" function in R is very flexible and supports
various models, but not ordered logit/probit models. I may conduct my
analysis by turning my DVs
2016 Apr 14
0
help with OR confidence interval using probit link
Howdy everyone
I?m trying to get Odds ratio and OR confidence intervals using a probit model, but I'm not getting.
Do you think you can help me?
I?m new with R L
naive = summary(glm(pcr.data[,7]~boldBeta_individual+pcr.data$age,family=binomial(link=probit)))
naive_answer = c(naive$coefficients[,1:3]) #naive estimates for