his helps,
> John
>
> -
> John Fox, Professor
> McMaster University
> Hamilton, Ontario
> Canada L8S 4M4
> Web: socserv.mcmaster.ca/jfox
>
>
>
>
>> -Original Message-
>> From: R-help [mailto:r-help-boun...@r-pr
is helps,
John
-
John Fox, Professor
McMaster University
Hamilton, Ontario
Canada L8S 4M4
Web: socserv.mcmaster.ca/jfox
> -Original Message-
> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Faradj Koliev
> Sent: July 27, 2016 4:50 AM
> To: r-help@r-project.or
On Wed, 27 Jul 2016, Faradj Koliev wrote:
Dear all,
A quick question: Let?s say I have a full and a restricted model that looks something like this:
Full<- polr(Y ~ X1+X2+X3+X4, data=data, Hess = TRUE, method="logistic?) # ordered logistic regression
Restricted<- polr(Y ~ X1+X2+X3, data=da
Dear all,
A quick question: Let’s say I have a full and a restricted model that looks
something like this:
Full<- polr(Y ~ X1+X2+X3+X4, data=data, Hess = TRUE, method="logistic”) #
ordered logistic regression
Restricted<- polr(Y ~ X1+X2+X3, data=data, Hess = TRUE, method="logistic”) #
or
Amanda Li uchicago.edu> writes:
>
> Hello,
>
> I am so sorry, but I have been struggling with
> the code for the entire day.
>
> I have a very simple dataset that looks like this:
> response=c(45,47,24,35,47,56,29)
> sub=c("A","A","B","B","C","C","C"£©
> time=c(1,2,1,2,1,2,3)
> gdata=cbind(re
Hello,
I am so sorry, but I have been struggling with the code for the entire day.
I have a very simple dataset that looks like this:
response=c(45,47,24,35,47,56,29)
sub=c("A","A","B","B","C","C","C"£©
time=c(1,2,1,2,1,2,3)
gdata=cbind(response,sub,time)
Namely, for three subjects, each has 2 o
> I have run a regression and want to calculate the likelihood
> of obtaining the sample.
> Is there a way in which I can use R to get this likelihood value?
See ?logLik
And see also ?help.search and ??. You would have found the above by typing
??likelihood at the command line in R
S Ellison
Hi all,
I have run a regression and want to calculate the likelihood of obtaining
the sample.
Is there a way in which I can use R to get this likelihood value?
Appreciate your help on this.
The following are the details:
raw_ols1=lm(data$LOSS~data$GDP+data$HPI+data$UE)
summary(raw_ols1)
Call
Dear All,
I fitted two non-nested proportional hazards models using the coxph()
function from package survival. Now, I would like to apply a model
selection test like, e.g., the likelihood ratio test proposed by Vuong.
I found an implementation of Vuong's test in the package 'pscl', but
that
On Nov 10, 2012, at 11:57 AM, bgnumis bgnum wrote:
> Hi All
>
>
> I have to run multiple stimations and to compute Likelihhod ratio.
>
> If I compute ls function with coef and summary I can extract "outputs" that
> I need.
Do you mean "lm"?
>
> I am not able to find something similar to lo
Hi All
I have to run multiple stimations and to compute Likelihhod ratio.
If I compute ls function with coef and summary I can extract "outputs" that
I need.
I am not able to find something similar to log liklihood
Can you pease tell me running a ls function x on y how to extract if
posib
Thanks for pointing that out. Made some modifications and it worked.
--
View this message in context:
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Sent from the R help mailing list archive at Nabble.com.
___
On 19-10-2012, at 04:40, stats12 wrote:
> Dear R users,
>
> I am trying to find the mle that involves integration.
>
> I am using the following code and get an error when I use the nlm function
>
> d<-matrix(c(1,1,0,0,0,0,0,0,2,1,0,0,1,1,0,1,2,2,1,0),nrow=10,ncol=2)
> h<-matrix(runif(20,0,1),
Dear R users,
I am trying to find the mle that involves integration.
I am using the following code and get an error when I use the nlm function
d<-matrix(c(1,1,0,0,0,0,0,0,2,1,0,0,1,1,0,1,2,2,1,0),nrow=10,ncol=2)
h<-matrix(runif(20,0,1),10)
integ<-matrix(c(0),nrow=10, ncol=2)
ll<-function(p){
Hi everyone,
I estimate structural equation models and use maximum likelihood estimation.
However, the estimates differ depeding on the starting values I choose, so I
guess there are multiple local maxima. I'm new to R (and statistics...;),
does anybody maybe know how I solve this best?
Thanks a
On Sun, 12 Jun 2011, Jorge Ivan Velez wrote:
Hi Diviya,
Take a look at the lrtest function in the lmtest package:
install.packages('lmtest)
require(lmtest)
?lrtest
Yes, when you have to nls() fits, say m1 and m2, you can do
lrtest(m1, m2)
However, I don't think that both m1 and m2 can be i
Hi Diviya,
Take a look at the lrtest function in the lmtest package:
install.packages('lmtest)
require(lmtest)
?lrtest
HTH,
Jorge
On Sun, Jun 12, 2011 at 1:16 PM, Diviya Smith <> wrote:
> Hello there,
>
> I want to perform a likelihood ratio test to check if a single exponential
> or a sum of
Hello there,
I want to perform a likelihood ratio test to check if a single exponential
or a sum of 2 exponentials provides the best fit to my data. I am new to R
programming and I am not sure if there is a direct function for doing this
and whats the best way to go about it?
#data
x <- c(1 ,10,
karuna m yahoo.com> writes:
> Can anybody tell me which R package has Lo-Mendell Rubin LR test and
> Bootstrap
> LR test to compare the model fit between k class and k+1 class model
> for Latent class analysis?
I don't know, but
library("sos")
findFn("Lo-Mendell")
findFn("{latent class ana
Dear R-help,
Can anybody tell me which R package has Lo-Mendell Rubin LR test and Bootstrap
LR test to compare the model fit between k class and k+1 class model for Latent
class analysis?
Thanks in advance,
warn regards,Ms.Karunambigai M
PhD Scholar
Dept. of Biostatistics
NIMHANS
Bangalore
India
Dear list,
I have a fitted nlme object from which I want to produce estimates of
the (marginal) likelihood for new data, given the fitted model. I am trying to
cross validate a number of nonlinear mixed effects models, and I would
like to find a way to calculate the marginal likelihood for "virgi
On Mar 25, 2011, at 12:17 PM, Michael Hecker wrote:
Hi,
I have a dataset of 78.903 news articles pertaining to 340 corporate
takeovers.
Mean 231.3871 [articles per takeover]
Std. Dev. 673.6395
I would like to calculate the probability of a certain number of
news articles if I had more tak
Hi,
I have a dataset of 78.903 news articles pertaining to 340 corporate
takeovers.
Mean 231.3871 [articles per takeover]
Std. Dev. 673.6395
I would like to calculate the probability of a certain number of news
articles if I had more takeovers available.
How likely is it to have X articles if
On 2010-04-30 12:42, jh556 wrote:
Some quick googling suggests that they are the same thing. Thanks for the
help!
And note that profile likelihood CIs are produced by default on
glm objects, i.e. R uses MASS's confint.glm for glm objects.
confint.default(your model) let's you compare with Wa
Some quick googling suggests that they are the same thing. Thanks for the
help!
--
View this message in context:
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Sent from the R help mailing list archive at Nabble.com.
___
jh556 wrote:
I'm applying logistic regression to a moderate sized data set for which I
believe Wald based confidence intervals on B coefficients are too
conservative. Some of the literature recommends using confidence intervals
based on the likelihood ratio in such cases, but I'm having diffic
I'm applying logistic regression to a moderate sized data set for which I
believe Wald based confidence intervals on B coefficients are too
conservative. Some of the literature recommends using confidence intervals
based on the likelihood ratio in such cases, but I'm having difficulty
locating a
Hi
I'm using mle2 for optimization of a multinomial likelihood. The model fit
is not very good and I would like to look at the likelihood profiles.
However I'm optimizing many parameters (~40) so using the function profile()
takes forever and is not practical. How can I calculate approximate
Davnah Urbach dartmouth.edu> writes:
> Thanks for this answer but does that mean that working
> with the deviances is better? Or how else could I
> evaluate the importance of my random terms?
You should probably (a) search the archives of the
r-sig-mixed-models mailing list
and (b) ask this
>
Thanks for this answer but does that mean that working with the deviances is
better? Or how else could I evaluate the importance of my random terms?
Many thanks,
Davnah
> On Mar 14, 2010, at 8:12 PM, hadley wickham wrote:
>
>>> Based on a discussion found on the R mailing list but dating
> Based on a discussion found on the R mailing list but dating back to 2008, I
> have compared the log-likelihoods of the glm model and of the glmer model as
> follows:
>
> lrt <- function (obj1, obj2){
> L0 <- logLik(obj1)
> L1 <- logLik(obj2)
> L01 <- as.vector(- 2 * (L0 - L1))
> df <- attr(L1,
I am currently running a generalized linear mixed effect model using glmer and
I want to estimate how much of the variance is explained by my random factor.
summary(glmer(cbind(female,male)~date+(1|dam),family=binomial,data= liz3"))
Generalized linear mixed model fit by the Laplace approximatio
Dear all,
I have the following problem:
I have been using the routine "optim" in order to maximize a joint
likelihood (basically a mixture with modeled weights) with quantitative
variables..so far so good.
Now I need to plug into the model a categorical variable (namely, age
classes).
Obviousl
Hi all,
Does any one know how to write the likelihood function for Poisson distribution
in R when P(x=0).
For normal case, it an be written as follows,
n * log(lambda) - lambda * n * mean(dat)
Any help is highly appreciated
Ashta
Jim Silverton wrote:
Is there any package available in R to do the following hypothesis tests?
Testing the means of two Poissons (equivalent to the difference of two
proportions)
Testing the equality of two proportions from binomials
Testing the equality of proprtions of two negative binomials
(
Is there any package available in R to do the following hypothesis tests?
Testing the means of two Poissons (equivalent to the difference of two
proportions)
Testing the equality of two proportions from binomials
Testing the equality of proprtions of two negative binomials
(both conditional and un
Thanks, I had a look at mlogit. It seems it does fit a multinomial logit
regression but - just as nnet or VGAM are doing it - it has a function that
tells you the fitted value, not the value that you have with a set of
parameters (which might not be the optimal ones). Or am I wrong on this?
Rong
Thanks for the book suggestion. I'll check it out tomorrow when the library
opens up.
Yes, it is a multilevel model, but its likelihood function is the sum of the
likelihood functions for the individual levels (i.e. a simple multinomial
logits) and some other terms (the priors). It is, essential
You may refer to mlogit for the ordinary multinomial regression. As
fas as I know, there are no functions for multilevel multinomial
regression.
Ronggui
2009/8/2 nikolay12 :
>
> Hi,
>
> I would like to apply the L-BFGS optimization algorithm to compute the MLE
> of a multilevel multinomial Logist
> School of Medicine
> Johns Hopkins University
>
> Ph. (410) 502-2619
> email: [1]rvarad...@jhmi.edu
>
>
> - Original Message -
> From: nikolay12 <[2]nikola...@gmail.com>
> Date: Sunday, August 2, 20
>
>
> - Original Message -
> From: nikolay12
> Date: Sunday, August 2, 2009 3:04 am
> Subject: [R] Likelihood Function for Multinomial Logistic Regression and
> its partial derivatives
> To: r-help@r-project.org
>
>
>> Hi,
>>
>> I
Sunday, August 2, 2009 3:04 am
Subject: [R] Likelihood Function for Multinomial Logistic Regression and its
partial derivatives
To: r-help@r-project.org
> Hi,
>
> I would like to apply the L-BFGS optimization algorithm to compute
> the MLE
> of a multilevel multinomial Lo
Hi,
I would like to apply the L-BFGS optimization algorithm to compute the MLE
of a multilevel multinomial Logistic Regression.
The likelihood formula for this model has as one of the summands the formula
for computing the likelihood of an ordinary (single-level) multinomial logit
regression. S
I have a problem related to measuring likelihood between
-an observed presence absence dataset (containing 0 or 1)
-a predicted simulation matrix of the same dimensions (containing values from 0
to 1)
This must be a common problem but I am struggling to find the answer in the
literature.
Withi
ys).
Ravi.
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
- Original Message -
From: joris meys
Date: Tuesday, March 17, 2009 7:37 pm
Subject: [R] Likel
Dear all,
I want to get the likelihood (or AIC or BIC) of a ridge regression model
using lm.ridge from the MASS library. Yet, I can't really find it. As
lm.ridge does not return a standard fit object, it doesn't work with
functions like e.g. BIC (nlme package). Is there a way around it? I would
ca
For the third one:
?anova.glm
test=Chisq will be LRT.
For the first two, you can have the answer from ordinary stat book.
On Wed, Nov 5, 2008 at 1:11 PM, Maithili Shiva <[EMAIL PROTECTED]> wrote:
> Hi!
>
> I am working on the Logistic Regression using R. My R script is as follows
>
>
> ONS <-
Hi!
I am working on the Logistic Regression using R. My R script is as follows
ONS <- read.csv("ONS.csv",header = TRUE)
ONS.glm <- glm(Y ~ Age1+Age2+Sex+Education+Profession+TimeInJob+
TimeInCurrentJob+OwnResidence+Dependents+ApplIncome+FamilyInco+IIR+FOIR+YearsAtBank+SavingsAccount+CurrentAcc
Hi!
I am working on the Logistic Regression using R. My R script is as follows
ONS <- read.csv("ONS.csv",header = TRUE)
ONS.glm <- glm(Y ~ Age1+Age2+Sex+Education+Profession+TimeInJob+
TimeInCurrentJob+OwnResidence+Dependents+ApplIncome+FamilyInco+IIR+FOIR+YearsAtBank+SavingsAccount+CurrentAc
Christophe LOOTS ifremer.fr> writes:
>
> Thank you so much for your help.
>
> The function "dbinom" seems to work very well.
>
> However, I'm a bit lost with the "dnorm" function.
>
> Apparently, I have to compute the mean "mu" and the standard deviation
> "sd" but what does it mean exactly?
Thank you so much for your help.
The function "dbinom" seems to work very well.
However, I'm a bit lost with the "dnorm" function.
Apparently, I have to compute the mean "mu" and the standard deviation
"sd" but what does it mean exactly? I only have a vector of predicted
response and a vector
This particular case with a random intercept model can be handled by
glmmML, by bootstrapping the p-value.
Best, Göran
On Thu, Jul 17, 2008 at 1:29 PM, Douglas Bates <[EMAIL PROTECTED]> wrote:
> On Thu, Jul 17, 2008 at 2:50 AM, Rune Haubo <[EMAIL PROTECTED]> wrote:
>> 2008/7/16 Dimitris Rizopoulo
On Thu, Jul 17, 2008 at 2:50 AM, Rune Haubo <[EMAIL PROTECTED]> wrote:
> 2008/7/16 Dimitris Rizopoulos <[EMAIL PROTECTED]>:
>> well, for computing the p-value you need to use pchisq() and dchisq() (check
>> ?dchisq for more info). For model fits with a logLik method you can directly
>> use the foll
2008/7/16 Dimitris Rizopoulos <[EMAIL PROTECTED]>:
> well, for computing the p-value you need to use pchisq() and dchisq() (check
> ?dchisq for more info). For model fits with a logLik method you can directly
> use the following simple function:
>
> lrt <- function (obj1, obj2) {
>L0 <- logLik(
well, for computing the p-value you need to use pchisq() and dchisq()
(check ?dchisq for more info). For model fits with a logLik method you
can directly use the following simple function:
lrt <- function (obj1, obj2) {
L0 <- logLik(obj1)
L1 <- logLik(obj2)
L01 <- as.vector(- 2 *
Dear list,
I am fitting a logistic multi-level regression model and need to test the
difference between the ordinary logistic regression from a glm() fit and the
mixed effects fit from glmer(), basically I want to do a likelihood ratio test
between the two fits.
The data are like this:
My outc
Christophe LOOTS ifremer.fr> writes:
>
> Hi,
>
> I've two fitted models, one binomial model with presence-absence data
> that predicts probability of presence and one gaussian model (normal or
> log-normal abundances).
>
> I would like to evaluate these models not on their capability of
> a
Hi,
I've two fitted models, one binomial model with presence-absence data
that predicts probability of presence and one gaussian model (normal or
log-normal abundances).
I would like to evaluate these models not on their capability of
adjustment but on their capability of prediction by calcu
R-helpers:
I have a question regarding the crr function of the cmprsk package for
performing competing risks regression. Specifically, I was wondering
if the standard likelihood ratio test for a categorical covariate
applies. For example:
# Make up a fake
Dear List,
Probably i am missing something important in optimize:
llk.1st <- function(alpha){
x <- c(20.0, 23.9, 20.9, 23.8, 25.0, 24.0, 21.7, 23.8, 22.8, 23.1, 23.1, 23.5,
23.0, 23.0)
n <- length(x)
llk1 <- -n*log(gamma(alpha)) - n*alpha*log(sum(x)/(n*alpha)) + (alpha -
1)*(sum(log(x))) - (su
: r-help@r-project.org
Subject: [R] Likelihood optimization numerically
Dear List,
I am not sure how should i optimize a log-likelihood numerically:
Here is a Text book example from Statistical Inference by George
Casella, 2nd
Edition Casella and Berger, Roger L. Berger (2002, pp. 355, ex. 7.4 #
Dear List,
I am not sure how should i optimize a log-likelihood numerically:
Here is a Text book example from Statistical Inference by George Casella, 2nd
Edition Casella and Berger, Roger L. Berger (2002, pp. 355, ex. 7.4 # 7.2.b):
data = x = c(20.0, 23.9, 20.9, 23.8, 25.0, 24.0, 21.7, 23.8, 22
Dear David,
no, distrTEst won't help. It has a different intention.
We are currently working on a new package "distrMod"
(cf. https://r-forge.r-project.org/projects/distrmod/)
which sometime might have such a functionality.
Best,
Matthias
David Bickel wrote:
> Is there any automatic mechanism f
David Bickel wrote:
> Is there any automatic mechanism for extracting a likelihood or test
> statistic distribution (PDF or CDF) from an object of class "htest" or
> from another object of a general class encoding a hypothesis test
> result?
>
> I would like to have a function that takes "x", an ob
You could create an S3 generic that does it. That is not initially
any less work than the if statement but if you add new distribution
no existing code need be modified. Just add a new method for each
distribution to be supported:
getDistr <- function(x) {
.Class <- names(x$value$statist
Is there any automatic mechanism for extracting a likelihood or test
statistic distribution (PDF or CDF) from an object of class "htest" or
from another object of a general class encoding a hypothesis test
result?
I would like to have a function that takes "x", an object of class
"htest", as its o
On Sat, 5 Jan 2008, xinyi lin wrote:
> 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
> a
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 o
On Fri, 21 Sep 2007, Wensui Liu wrote:
> chris,
> as long as you know the log likelihood functions and the # of
> parameters in both models, a pencil and a piece of paper should be
> enough to calculate LR test.
True enough for the LR statistic.
Or follow the instructions in the _posting guide_
chris,
as long as you know the log likelihood functions and the # of
parameters in both models, a pencil and a piece of paper should be
enough to calculate LR test.
On 9/21/07, Chris Elsaesser <[EMAIL PROTECTED]> wrote:
> I would like to try a likelihood ratio test in place of waldtest.
> Ideally
Chris Elsaesser wrote:
> I would like to try a likelihood ratio test in place of waldtest.
> Ideally I'd like to provide two glm models, the second a submodel of the
> first, in the style of lrt
> (http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt
> takes farimsa objects]
>
> Does
I would like to try a likelihood ratio test in place of waldtest.
Ideally I'd like to provide two glm models, the second a submodel of the
first, in the style of lrt
(http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt
takes farimsa objects]
Does anyone know of such a likelihood rat
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