Re: [R] Logistic regression for large data

summary(Base) would show if one of columns of Base was read as character data instead of the expected numeric. That could cause an explosion in the number of dummy variables, hence a huge design matrix. -Bill On Fri, Nov 11, 2022 at 11:30 PM George Brida wrote: > Dear R users, > > I have a

Re: [R] Logistic regression for large data

Hi George, I did not get an attachment. My first step would be to try simplifying things. Do all of these work? fit_1=glm(Base[,2]~Base[,1],family=binomial(link="logit")) fit_1=glm(Base[,2]~Base[,10],family=binomial(link="logit")) fit_1=glm(Base[,2]~Base[,11],family=binomial(link="logit"))

Re: [R] Logistic regression for large data

That’s not a large data set. Something else besides memory limits is going on. You should post output of summary(Base). — David Sent from my iPhone > On Nov 11, 2022, at 11:29 PM, George Brida wrote: > > Dear R users, > > I have a database called Base.csv (attached to this email) which

Re: [R] Logistic Regression with Panel Data

> On Apr 23, 2019, at 8:26 AM, Paul Bernal wrote: > > Dear friends, hope you are all doing great, > > I would like to know if there is any R package that allows fitting of > logistic regression to panel data. > > I installed and loaded package plm, but from what I have read so far, plm >

Re: [R] Logistic regression and robust standard errors

On Fri, 1 Jul 2016, Faradj Koliev wrote: Dear Achim Zeileis,  Many thanks for your quick and informative answer.  I?m sure that the vcovCL should work, however, I experience some problems.  > coeftest(model, vcov=vcovCL(model, cluster=mydata$ID)) First I got this error:  Error in

Re: [R] Logistic regression and robust standard errors

Dear Achim Zeileis, Many thanks for your quick and informative answer. I’m sure that the vcovCL should work, however, I experience some problems. > coeftest(model, vcov=vcovCL(model, cluster=mydata$ID)) First I got this error: Error in vcovCL(model, cluster = mydata$ID) : length of

Re: [R] Logistic regression and robust standard errors

On Fri, 1 Jul 2016, Faradj Koliev wrote: Dear all, I use ?polr? command (library: MASS) to estimate an ordered logistic regression. My model: summary( model<- polr(y ~ x1+x2+x3+x4+x1*x2 ,data=mydata, Hess = TRUE)) But how do I get robust clustered standard errors? I??ve tried

Re: [R] Logistic Regression output baseline (reference) category

> On Mar 25, 2016, at 10:19 PM, Michael Artz wrote: > > Hi, > I have now read an introductory text on regression and I think I do > understand what the intercept is doing. However, my original question is > still unanswered. I understand that the intercept term is the

Re: [R] Logistic Regression output baseline (reference) category

Hi, I have now read an introductory text on regression and I think I do understand what the intercept is doing. However, my original question is still unanswered. I understand that the intercept term is the constant that each other term is measured against. I think baseline is a good word for

Re: [R] Logistic Regression output baseline (reference) category

> On Mar 15, 2016, at 1:27 PM, Michael Artz wrote: > > Hi, > I am trying to use the summary from the glm function as a data source. I > am using the call sink() then > summary(logisticRegModel)$coefficients then sink(). Since it's a matrix you may need to locate a

Re: [R] Logistic Regression output baseline (reference) category

The reference category is aliased with the constant term in the default contr.treatment contrasts. See ?contr.treatment , ?C, ?contrasts If you don't know what this means, you should probably consult a local statistical resource or ask about linear model contrasts at a statistical help website

Re: [R] Logistic Regression

Do you have the sample sizes that the sample proportions were computed from (e.g. 0.5 could be 1 out of 2 or 100 out of 200)? If you do then you can specify the model with the proportions as the y variable and the corresponding sample sizes as the weights argument to glm. If you only have

Re: [R] Logistic Regression

But beta can only be used to model the open interval between zero and one On Monday, January 25, 2016, Greg Snow <538...@gmail.com> wrote: > Do you have the sample sizes that the sample proportions were computed > from (e.g. 0.5 could be 1 out of 2 or 100 out of 200)? > > If you do then you can

Re: [R] Logistic Regression

Alternatively you might use log(p/1-p) as your dependent variable and use OLS with robust standard errors. Much of your inference would be analogous to a logistic regression John C Frain 3 Aranleigh Park Rathfarnham Dublin 14 Ireland www.tcd.ie/Economics/staff/frainj/home.html

Re: [R] Logistic Regression

with glm(), you might try the quasi binomial family On Saturday, January 23, 2016, pari hesabi wrote: > Hello everybody, > > I am trying to fit a logistic regression model by using glm() function in > R. My response variable is a sample proportion NOT binary

Re: [R] Logistic Regression

> On Jan 23, 2016, at 12:41 PM, pari hesabi wrote: > > Hello everybody, > > I am trying to fit a logistic regression model by using glm() function in R. > My response variable is a sample proportion NOT binary numbers(0,1). So multiply the sample proportions (and

Re: [R] Logistic regression R and Stata grouping variable

You were not completely clear, but it appears that you have data where each subject has results from 8 trials, as a pair of variables is changed. If that is correct, then you want to have a variance that corrects for the repeated measures. In R the glm command handles the simple case but not

Re: [R] logistic regression for data with repeated measures

http://stats.stackexchange.com/questions/62225/conditional-logistic-regression-vs-glmm-in-r might be a good start Ersatzistician and Chutzpahthologist I can answer any question. I don't know is an answer. I don't know yet is a better answer. On Tue, Jul 1, 2014 at

Re: [R] Logistic Regression

You might want to read this vignette: http://cran.r-project.org/web/packages/HSAUR/vignettes/Ch_logistic_regression_glm.pdf On 14 June 2014 19:53, javad bayat j.bayat...@gmail.com wrote: Dear all, I have to use Zelig package for doing logistic regression. How can I use Zelig package for

Re: [R] Logistic Regression with 200K features in R?

it is simply because you can't do a regression with more predictors than observations. Cheers. Am 12.12.2013 09:00, schrieb Romeo Kienzler: Dear List, I'm quite new to R and want to do logistic regression with a 200K feature data set (around 150 training examples). I'm aware that I

Re: [R] Logistic Regression with 200K features in R?

I thought so (with all the limitations due to collinearity and so on), but actually there is a limit for the maximum size of an array which is independent of your memory size and is due to the way arrays are indexed. You can't create an object with more than 2^31-1 = 2147483647 elements.

Re: [R] Logistic Regression with 200K features in R?

On 13-12-12 6:51 AM, Eik Vettorazzi wrote: I thought so (with all the limitations due to collinearity and so on), but actually there is a limit for the maximum size of an array which is independent of your memory size and is due to the way arrays are indexed. You can't create an object with more

Re: [R] Logistic Regression with 200K features in R?

thanks Duncan for this clarification. A double precision matrix with 2e11 elements (as the op wanted) would need about 1.5 TB memory, that's more than a standard (windows 64bit) computer can handle. Cheers. Am 12.12.2013 13:00, schrieb Duncan Murdoch: On 13-12-12 6:51 AM, Eik Vettorazzi wrote:

Re: [R] Logistic Regression with 200K features in R?

On 12/12/2013 7:08 AM, Eik Vettorazzi wrote: thanks Duncan for this clarification. A double precision matrix with 2e11 elements (as the op wanted) would need about 1.5 TB memory, that's more than a standard (windows 64bit) computer can handle. According to Microsoft's Memory Limits web page

Re: [R] Logistic Regression with 200K features in R?

ok, so 200K predictors an 10M observations would work? On 12/12/2013 12:12 PM, Eik Vettorazzi wrote: it is simply because you can't do a regression with more predictors than observations. Cheers. Am 12.12.2013 09:00, schrieb Romeo Kienzler: Dear List, I'm quite new to R and want to do

Re: [R] Logistic Regression with 200K features in R?

Dear Eik, thank you so much for your help! best Regards, Romeo On 12/12/2013 12:51 PM, Eik Vettorazzi wrote: I thought so (with all the limitations due to collinearity and so on), but actually there is a limit for the maximum size of an array which is independent of your memory size and is

Re: [R] Logistic Regression

On Apr 19, 2013, at 11:45 AM, Endy BlackEndy wrote: Please read the attach file. Please re-read : http://www.r-project.org/mail.html#instructions Thank you Endy __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help

Re: [R] Logistic regression

Endy, See the package ResourceSelection for the HL test and the package caret for the sensitivity and specificity measures. Regards, Jose Iparraguirre Chief Economist Age UK, London From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On

Re: [R] Logistic regression/Cut point? predict ??

What do you mean by at x equal zero? On Sun, Oct 21, 2012 at 8:37 AM, Adel Powell powella...@gmail.com wrote: I am new to R and I am trying to do a monte carlo simulation where I generate data and interject error then test various cut points; however, my output was garbage (at x equal zero, I

Re: [R] Logistic regression X^2 test with large sample size (fwd)

On Jul 31, 2012, at 10:35 AM, M Pomati marco.pom...@bristol.ac.uk wrote: Does anyone know of any X^2 tests to compare the fit of logistic models which factor out the sample size? I'm dealing with a very large sample and I fear the significant X^2 test I get when adding a variable to the

Re: [R] Logistic regression X^2 test with large sample size (fwd)

Marc, thank you very much for your help. I've posted in on http://math.stackexchange.com/questions/177252/x2-tests-to-compare-the-fit-of-large-samples-logistic-models and added details. Many thanks Marco --On 31 July 2012 11:50 -0500 Marc Schwartz marc_schwa...@me.com wrote: On Jul 31,

Re: [R] Logistic regression X^2 test with large sample size (fwd)

On Jul 31, 2012, at 10:25 AM, M Pomati wrote: Marc, thank you very much for your help. I've posted in on http://math.stackexchange.com/questions/177252/x2-tests-to-compare-the-fit-of-large-samples-logistic-models and added details. I think you might have gotten a more statistically

Re: [R] logistic regression

On Apr 3, 2012, at 9:25 PM, Melrose2012 wrote: I am trying to plot the logistic regression of a dataset (# of living flies vs days the flies are alive) and then fit a best-fit line to this data. Here is my code: plot(fflies$living~fflies$day,xlab=Number of Days,ylab=Number of Fruit

Re: [R] logistic regression

On Apr 3, 2012, at 9:25 PM, Melrose2012 wrote: I am trying to plot the logistic regression of a dataset (# of living flies vs days the flies are alive) and then fit a best-fit line to this data. Here is my code: plot(fflies$living~fflies$day,xlab=Number of Days,ylab=Number of Fruit

Re: [R] logistic regression

No, this is related to my own data. Yes, 'fflies' the dataset - here I am working with two columns: # of fruit flies alive vs # of days these flies are alive. There is no error, it's just that the best-fit line does not plot nicely on top of my data (see figure attached).

Re: [R] logistic regression

Get help. You do not understand glm's. What do you think the fitted values are? -- Hint: they are *not* an estimate of the number of living fruit flies. -- Bert On Tue, Apr 3, 2012 at 6:25 PM, Melrose2012 melissa.patric...@stonybrook.edu wrote: I am trying to plot the logistic regression of a

Re: [R] logistic regression

Thank you for your reply. I do understand that I am working in log space with the default link function of the binomial being logit. My problem is, I thought that they way I had written the code, when I did the lines command, it should plot the best-fit line (found by 'glm') on top of my

Re: [R] logistic regression

Hi Melissa, I would highly encourage you to read [1]. It would be extremely beneficial for your understanding of the type of models you should use in a situation like yours (count data). Best regards, Jorge.- [1] cran.r-project.org/web/packages/pscl/vignettes/countreg.pdf On Wed, Apr 4,

Re: [R] logistic regression

Melrose2012 melissa.patrician at stonybrook.edu writes: alive - (fflies$living) dead - (fflies$living[1]-alive) glm.fit - glm(cbind(alive,dead)~fflies$day,family=binomial) summary(glm.fit) Your call to glm() is not doing what you think it's doing. What you want to do is probably closer to

Re: [R] Logistic regression

On Wed, Mar 28, 2012 at 11:49 AM, carlb1 carl19...@hotmail.com wrote: Hi does anyone know how to do a logistic regression in R? I do. And doubtless many other folks on R-help do too. any help appreciated You should probably start here:

Re: [R] Logistic Regression

I'm surprised not to see the simple answer: glm models return the MLE estimate. fit - glm(y ~ x1 + x2 + , family='binomial') There is no need for special packages, this is a standard part of R. Terry Therneau begin included message -- On 02/06/2012

Re: [R] Logistic Regression

Hi, On Mon, Feb 6, 2012 at 10:08 AM, Ana rrast...@gmail.com wrote: I am looking for R packages that can make a Logistic Regression model with parameter estimation by Maximum Likelihood Estimation. How are you looking? Did you perhaps try Google

Re: [R] Logistic Regression

On 02/06/2012 03:08 PM, Ana wrote: I am looking for R packages that can make a Logistic Regression model with parameter estimation by Maximum Likelihood Estimation. Many thanks for helping out. __ R-help@r-project.org mailing list

Re: [R] Logistic Regression with genetic component

Danielle Duncan dlduncan2 at alaska.edu writes: Greetings, I have a question that I'd like to get input on. I have a classic toxicology study where I artificially fertilized and exposed embryos to a chemical and counted defects. In addition, I kept track of male-female pairs that I used to

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

On 12/01/2011 08:00 PM, Ben quant wrote: The data I am using is the last file called l_yx.RData at this link (the second file contains the plots from earlier): http://scientia.crescat.net/static/ben/ The logistic regression model you are fitting assumes a linear relationship between x and the

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

On Dec 1, 2011, at 18:54 , Ben quant wrote: Sorry if this is a duplicate: This is a re-post because the pdf's mentioned below did not go through. Still not there. Sometimes it's because your mailer doesn't label them with the appropriate mime-type (e.g. as application/octet-stream, which is

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

Thank you for the feedback, but my data looks fine to me. Please tell me if I'm not understanding. I followed your instructions and here is a sample of the first 500 values : (info on 'd' is below that) d - as.data.frame(l_yx) x = with(d, y[order(x)]) x[1:500] # I have 1's and 0's dispersed

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

On Dec 1, 2011, at 21:32 , Ben quant wrote: Thank you for the feedback, but my data looks fine to me. Please tell me if I'm not understanding. Hum, then maybe it really is a case of a transition region being short relative to the range of your data. Notice that the warning is just that: a

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

Here you go: attach(as.data.frame(l_yx)) range(x[y==1]) [1] -22500.746. range(x[y==0]) [1] -10076.5303653.0228 How do I know what is acceptable? Also, here are the screen shots of my data that I tried to send earlier (two screen shots, two pages):

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

Oops! Please ignore my last post. I mistakenly gave you different data I was testing with. This is the correct data: Here you go: attach(as.data.frame(l_yx)) range(x[y==0]) [1] 0.0 14.66518 range(x[y==1]) [1] 0.0 13.49791 How do I know what is acceptable? Also, here are the

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

I'm not proposing this as a permanent solution, just investigating the warning. I zeroed out the three outliers and received no warning. Can someone tell me why I am getting no warning now? I did this 3 times to get rid of the 3 outliers: mx_dims = arrayInd(which.max(l_yx), dim(l_yx))

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

On Dec 1, 2011, at 23:43 , Ben quant wrote: I'm not proposing this as a permanent solution, just investigating the warning. I zeroed out the three outliers and received no warning. Can someone tell me why I am getting no warning now? It's easier to explain why you got the warning before.

Re: [R] logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

Thank you so much for your help. The data I am using is the last file called l_yx.RData at this link (the second file contains the plots from earlier): http://scientia.crescat.net/static/ben/ Seems like the warning went away with pmin(x,1) but now the OR is over 15k. If I multiple my x's by

Re: [R] logistic regression by glm

On 20.11.2011 12:46, tujchl wrote: HI I use glm in R to do logistic regression. and treat both response and predictor as factor In my first try: *** Call: glm(formula = as.factor(diagnostic) ~ as.factor(7161521) +

Re: [R] logistic regression by glm

On 20.11.2011 16:58, 屠鞠传礼 wrote: Thank you Ligges :) one more question: my response value diagnostic have 4 levels (0, 1, 2 and 3), so I use it like this: as.factor(diagnostic) ~ as.factor(7161521) +as.factor(2281517) Is it all right? Uhh. 4 levels? Than I doubt logistic regression is the

Re: [R] logistic regression by glm

On 20.11.2011 17:27, 屠鞠传礼 wrote: I worried it too, Do you have idear that what tools I can use? Depends on your aims - what you want to do with the fitted model. A multinomial model, some kind of discriminant analysis (lda, qda), tree based methods, svm and so son come to mind. You

Re: [R] logistic regression by glm

I worried it too, Do you have idear that what tools I can use? ÔÚ 2011-11-21 00:13:26£¬Uwe Ligges lig...@statistik.tu-dortmund.de дµÀ£º On 20.11.2011 16:58, ÍÀ¾Ï´«Àñ wrote: Thank you Ligges :) one more question: my response value diagnostic have 4 levels (0, 1, 2 and 3), so I use it

Re: [R] logistic regression by glm

Thank you Ligges :) one more question: my response value diagnostic have 4 levels (0, 1, 2 and 3), so I use it like this: as.factor(diagnostic) ~ as.factor(7161521) +as.factor(2281517) Is it all right? ÔÚ 2011-11-20 23:45:23£¬Uwe Ligges lig...@statistik.tu-dortmund.de дµÀ£º On 20.11.2011

Re: [R] logistic regression by glm

Thank you very much :) I search on net and find sometimes response value in logistic model can have more than 2 values, and the way of this kinds of regression is called Ordinal Logistic Regression. and even we can caculate it by the same way I mean glm in R. here are some references: 1.

Re: [R] logistic regression by glm

On Nov 20, 2011, at 7:26 PM, 屠鞠传礼 wrote: Thank you very much :) I search on net and find sometimes response value in logistic model can have more than 2 values, and the way of this kinds of regression is called Ordinal Logistic Regression. and even we can caculate it by the same way I

Re: [R] logistic Regression using lmer

arunkumar akpbond007 at gmail.com writes: Hi can we perform logistic regression using lmer function. Please help me with this Yes. library(lme4) glmer([reponse]~[fixed effects (covariates)]+(1|[grouping variable]), data=[data frame], family=binomial) Further questions

Re: [R] Logistic Regression - Variable Selection Methods With Prediction

Can I atleast get help with what pacakge to use for logistic regression with all possible models and do prediction. I know i can use regsubsets but i am not sure if it has any prediction functions to go with it. Thanks On Oct 25, 6:54 pm, RAJ dheerajathr...@gmail.com wrote: Hello, I am pretty

Re: [R] Logistic Regression - Variable Selection Methods With Prediction

Try the glm package Steve Friedman Ph. D. Ecologist / Spatial Statistical Analyst Everglades and Dry Tortugas National Park 950 N Krome Ave (3rd Floor) Homestead, Florida 33034 steve_fried...@nps.gov Office (305) 224 - 4282 Fax (305) 224 - 4147

Re: [R] Logistic Regression - Variable Selection Methods With Prediction

Hi, On Wed, Oct 26, 2011 at 12:35 PM, RAJ dheerajathr...@gmail.com wrote: Can I atleast get help with what pacakge to use for logistic regression with all possible models and do prediction. I know i can use regsubsets but i am not sure if it has any prediction functions to go with it. Maybe

Re: [R] Logistic Regression - Variable Selection Methods With Prediction

Check glmulti package for all subset selection. Weidong Gu On Wed, Oct 26, 2011 at 12:35 PM, RAJ dheerajathr...@gmail.com wrote: Can I atleast get help with what pacakge to use for logistic regression with all possible models and do prediction. I know i can use regsubsets but i am not sure if

Re: [R] Logistic Regression - Variable Selection Methods With Prediction

You mean the glm() _function_ in the stats package. ?glm (just to avoid confusion) -- Bert On Wed, Oct 26, 2011 at 10:31 AM, steve_fried...@nps.gov wrote: Try the glm package Steve Friedman Ph. D. Ecologist / Spatial Statistical Analyst Everglades and Dry Tortugas National Park 950 N

Re: [R] Logistic Regression - Variable Selection Methods With Prediction

The reason that you are not likely getting replies is that what you propose to do is considered a poor way of building models. You need to get out of the SAS Mindset. I would suggest you obtain a copy of Frank Harrell's book: http://www.amazon.com/exec/obidos/ASIN/0387952322/ and then

Re: [R] logistic regression: default computed probability

On Sep 21, 2011, at 10:25 AM, n wrote: Hello all, Suppose in a logistic regression model, the binary outcome is coded as 0 or 1. In SAS, the default probability computed is for Y = 0 (smaller of the two values) . However, in SPSS the probability computed is for Y = 1 (greater of the two

Re: [R] Logistic regression with 2 factors and 3 covariates

On 13.06.2011 19:37, Alal wrote: Hello I'm trying to write a model for my data, but Im not sure it's statistically correct. I have one variable (2 levels: A or B). To explain it, I've got 2 factors and 3 continuous variables. I need to do a logistic regression, but... First: can I actually

Re: [R] Logistic Regression

Which statistical principles are you invoking on which to base such analyses? Frank Sergio Della Franca wrote: Dear R-Helpers, I want to perform a logistic regression on my dataset (y). I used the following code: logistic-glm(formula=interest_variable~.,family = binomial(link =

Re: [R] Logistic Regression

First a word of caution: Forward, backward, and stepwise regression analyses are not well received among statisticians. There are many reasons for this. Some of the reasons include: (1) The p value computed at each step is computed ignoring all the previous steps. This can lead to incorrect

Re: [R] Logistic Regression

Date: Tue, 7 Jun 2011 01:38:32 -0700 From: farah.farid@student.aku.edu To: r-help@r-project.org Subject: [R] Logistic Regression I am working on my thesis in which i have couple of independent variables that are categorical in nature and the

Re: [R] Logistic Regression

The 10% change idea was never a good one and has not been backed up by simulations. It is quite arbitrary and results in optimistic standard errors of remaining variables. In fact a paper presented at the Joint Statistical Meetings about 3 years ago (I'm sorry I've forgotten the names of the

Re: [R] Logistic Regression

IMHO, you evidence considerable confusion and misunderstanding of statistical methods. I would say that most of what you describe is nonsense. Of course, maybe I'm just the one who's confused, but I would strongly suggest you consult with a local statistician. This list is unlikely to be able to

Re: [R] logistic regression lrm() output

Why is a one unit change in x an interesting range for the purpose of estimating an odds ratio? The default in summary() is the inter-quartile-range odds ratio as clearly stated in the rms documentation. Frank array chip wrote: Hi, I am trying to run a simple logistic regression using lrm()

Re: [R] logistic regression with glm: cooks distance and dfbetas are different compared to SPSS output

On 29.04.2011 18:29, Biedermann, Jürgen wrote: Hi there, I have the problem, that I'm not able to reproduce the SPSS residual statistics (dfbeta and cook's distance) with a simple binary logistic regression model obtained in R via the glm-function. I tried the following: fit - glm(y ~ x1 +

Re: [R] logistic regression: wls and unbalanced samples

On Apr 27, 2011, at 00:22 , Andre Guimaraes wrote: Greetings from Rio de Janeiro, Brazil. I am looking for advice / references on binary logistic regression with weighted least squares (using lrm weights), on the following context: 1) unbalanced sample (n0=1, n1=700); 2) sampling

Re: [R] logistic regression: wls and unbalanced samples

On Wed, 27 Apr 2011, peter dalgaard wrote: On Apr 27, 2011, at 00:22 , Andre Guimaraes wrote: Greetings from Rio de Janeiro, Brazil. I am looking for advice / references on binary logistic regression with weighted least squares (using lrm weights), on the following context: 1) unbalanced

Re: [R] logistic regression: wls and unbalanced samples

Many thanks for your messages. I will take a look at the survey package. I was concerned with the issues raised by Cramer (1999) in Predictive performance of the binary logit model in unbalanced samples. In this particular case, misclassification costs are much higher for the smaller group

Re: [R] Logistic Regression Fitting with EM-Algorithm

Dear Ted, sorry for being unclear. Let me try again. I indeed have no knowledge about the value of the response variable for any object. Instead, I have a data frames of explanatory variables for each object. For example, x1 x2 x3 1 4.409974 2.348745 1.9845313 2 3.809249

Re: [R] Logistic Regression Fitting with EM-Algorithm

In view of your further explanation, Robin, the best I can offer is the following. [1] Theoretical frame. *IF* variables (X1,X2,X3) are distributed according to a mixture of two multivariate normal distributions, i.e. as two groups, each with a multivariate normal distribution, *AND* the members

Re: [R] Logistic Regression Fitting with EM-Algorithm

On 03-Jan-11 14:02:21, Robin Aly wrote: Hi all, is there any package which can do an EM algorithm fitting of logistic regression coefficients given only the explanatory variables? I tried to realize this using the Design package, but I didn't find a way. Thanks a lot Kind regards Robin

Re: [R] logistic regression with response 0,1

Hi: I think you created a problem for yourself in the way you generated your data. y-rbinom(2000,1,.7) euro - rnorm(2000, m = 300 * y + 50 * (1 - y), s = 20 * y + 12 * (1 - y)) # Create a 2000 x 2 matrix of probabilities prmat - cbind(0.8 * y + 0.2 * (1 - y), 0.2 * y + 0.8 * (1 - y)) # sample

Re: [R] logistic regression or not?

array chip arrayprofile at yahoo.com writes: [snip] I can think of analyzing this data using glm() with the attached dataset: test-read.table('test.txt',sep='\t') fit-glm(cbind(positive,total-positive)~treatment,test,family=binomial) summary(fit) anova(fit, test='Chisq') First, is this

Re: [R] logistic regression or not?

A possible caveat here. Traditionally, logistic regression was performed on the logit-transformed proportions, with the standard errors based on the residuals for the resulting linear fit. This accommodates overdispersion naturally, but without telling you that you have any. glm with a binomial

Re: [R] logistic regression or not?

On Dec 21, 2010, at 14:22 , S Ellison wrote: A possible caveat here. Traditionally, logistic regression was performed on the logit-transformed proportions, with the standard errors based on the residuals for the resulting linear fit. This accommodates overdispersion naturally, but without

Re: [R] logistic regression or not?

...and before you believe in overdispersion, make sure you have a credible explanation for it. All too often, what you really have is a model that doesn't fit your data properly. Well put. A possible fortune? S Ellison ***

Re: [R] logistic regression or not?

: glm(log(percentage/(1-percentage))~treatment,data=test) Thanks John   From: Ben Bolker bbol...@gmail.com To: r-h...@stat.math.ethz.ch Sent: Tue, December 21, 2010 5:08:34 AM Subject: Re: [R] logistic regression or not? array chip arrayprofile at yahoo.com

Re: [R] logistic regression or not?

. *From:* Ben Bolker bbol...@gmail.com *To:* r-h...@stat.math.ethz.ch *Sent:* Tue, December 21, 2010 5:08:34 AM *Subject:* Re: [R] logistic regression or not? array chip arrayprofile at yahoo.com http://yahoo.com/ writes: [snip] I can think of analyzing

Re: [R] logistic regression or not?

Ben, thanks again. John From: Ben Bolker bbol...@gmail.com Cc: r-h...@stat.math.ethz.ch; S Ellison s.elli...@lgc.co.uk; peter dalgaard pda...@gmail.com Sent: Tue, December 21, 2010 9:26:29 AM Subject: Re: [R] logistic regression or not? On 10-12-21 12:20 PM

Re: [R] Logistic regression with factorial effect

You would be better off posting to R-sig-mixed-models or R-sig-ecology -- Bert On Thu, Nov 18, 2010 at 9:32 AM, Billy.Requena billy.requ...@gmail.com wrote: Hello, I’d like to evaluate the temporal effect on the relationship between a continuous variable (e.g. size) and the probability of

Re: [R] logistic regression tree

dear all, thank you everyone for the profound answers and the needful references! achim, thank you for the very kind offer!! sorrily i'm not around vienna in the near feature, otherwise i'd be glad to coming back to your invitation. yours, kay - Kay Cichini

Re: [R] logistic regression tree

On 08/22/2010 01:51 PM, Kay Cichini wrote: achim, thank you for the very kind offer!! sorrily i'm not around vienna in the near feature, otherwise i'd be glad to coming back to your invitation. Not that it's any of my business, but I don't think you need to go THAT far to visit Achim these

Re: [R] logistic regression tree

hello gavin achim, thanks for responding. by logistic regression tree i meant a regression tree for a binary response variable. but as you say i could also use a classification tree - in my case with only two outcomes. i'm not aware if there are substantial differences to expect for the two

Re: [R] logistic regression tree

On Fri, 20 Aug 2010, Kay Cichini wrote: hello gavin achim, thanks for responding. by logistic regression tree i meant a regression tree for a binary response variable. but as you say i could also use a classification tree - in my case with only two outcomes. i'm not aware if there are

Re: [R] logistic regression tree

It would be good to tell us of the frequency of observations in each category of Y, and the number of continuous X's. Recursive partitioning will require perhaps 50,000 observations in the less frequent Y category for its structure and predicted values to validate, depending on X and the

Re: [R] logistic regression tree

hello, my data-collection is not yet finished, but i though have started investigating possible analysis methods. below i give a very close simulation of my future data-set, however there might be more nominal explanatory variables - there will be no continous at all (maybe some ordered

Re: [R] logistic regression tree

On Fri, 2010-08-20 at 14:46 -0700, Kay Cichini wrote: hello, my data-collection is not yet finished, but i though have started investigating possible analysis methods. below i give a very close simulation of my future data-set, however there might be more nominal explanatory variables -

Re: [R] logistic regression tree

On Fri, 20 Aug 2010, Kay Cichini wrote: hello, my data-collection is not yet finished, but i though have started investigating possible analysis methods. below i give a very close simulation of my future data-set, however there might be more nominal explanatory variables - there will be no

Re: [R] logistic regression tree

On Thu, 2010-08-19 at 13:42 -0700, Kay Cichini wrote: hello everyone, i sampled 100 stands at 20 restoration sites and presence of 3 different invasive plant species. i came across logistic regression trees and wonder if this is suited for my purpose - predicting presence of these

  1   2   >