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

ght have plenty of physical memory, but also lots of (open files, cookies, applications, other stuff) that eat memory. Regards, Tim -Original Message- From: R-help On Behalf Of George Brida Sent: Friday, November 11, 2022 4:17 PM To: r-help@r-project.org Subject: [R] Logistic regress

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

[R] Logistic regression for large data

Dear R users, I have a database called Base.csv (attached to this email) which contains 13 columns and 8257 rows and whose the first 8 columns are dummy variables which take 1 or 0. The problem is when I wrote the following instructions to do a logistic regression , R runs for hours and hours

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 >

[R] Logistic Regression with Panel Data

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 only allows fitting of linear regression to panel data, not logistic.

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

[R] Logistic regression and robust standard errors

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 coeftest(resA, vcov=vcovHC(resA,

[R] logistic regression with package 'mice'

Dear all, I request your help to solve a problem I've encountered in using 'mice' for multiple imputation. I want to apply a logistic regression model. I need to extract information on the fit of the model. Is there any way to calculate a likelihood ratio or the McFadden-pseudoR2 from the results

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

[R] Logistic Regression output baseline (reference) category

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(). The independent variables are categorical and thus there is always a baseline value for every category that is omitted from the glm

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

[R] logistic regression R and Stata – grouping variable

Hello, I mostly use Stata 13 for my regression analysis. I want to conduct a logistic regression on a proportion/number of success. Because I receive errors in Stata I did not expect nor understand (if there are Stata experts who want to know more about the problems I face and can potentially

[R] Logistic Regression Results per Condition

Hi everyone! I conducted a study for which I conducted logistic regressions (and it works), but now I'd like to have the results per condition, and I failed to discover how to have them. I explain myself: In conduted a study in which participants can realize one behavior (coded 1 if

[R] logistic regression for data with repeated measures

Hi, It seems that I'm quite lost in this wide and powerful R's universe, so I permit myself to ask your help about issues with which I'm struggling. Thank you, I would like to know if the answer’s accuracy (correct = 1; incorrect = 0) varies depending on 2 categorical variables which are the

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

[R] Logistic Regression

Dear all, I have to use Zelig package for doing logistic regression. How can I use Zelig package for logistic regression? I did this code by glm function: glm1 = glm(kod~Curv+Elev+Out.c+Slope+Aspect,data=data, family=binomial) summary(glm1) But the results were not appropriate for my

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

[R] Logistic regression help!

Hi guys, I have a trouble to solve the specificity and senstitivity for a logistic regression model. I really need your help, would you please help me out? :) Thank you!! This is the model I constructed: model=glm(Status ~ Gender.compl+ X2.4.times.per.month+

[R] Logistic regression and FIML for treating missing values

Hello there, Is it possible to do a logistic regression with R while 1) using the full-information maximum likelihood (FIML) for treating missing values, and 2) having a complex sample design (Cluster, Weight, Stratum). I looked at Lavaan, but, if I'm correct, it doesn't do FIML when using

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

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 should use Naive Bayes but I have a more general question about the capability of R handling very high dimensional data. Please consider the

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

[R] Logistic regression over LOOCV

Hello all. I have this code: myLOOCV - function(myformula, data) { Y - all.vars(myformula)[1] Scores- numeric(length(data[,1])) for (i in 1:length(data[,1])) { train - data[-i,] test - data[i,] myModel - lrm(myformula, train) Scores[i] -

[R] Logistic regression

Dear colleagues I have a couple of problems related with binary logistic regression. The first problem is how to compute Pearson and Likelihood chi-squeared tests for grouped data. For the same form of data set how to compute sensitivity, specificity and related measures. When I speak about

[R] Logistic Regression

Please read the attach file. Thank you Endy __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained,

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

[R] Logistic Regression

Dear colleagues I have a couple of problems related with binary logistic regression. The first problem is how to compute Pearson and Likelihood chi-squeared tests for grouped data. For the same form of data set how to compute sensitivity, specificity and related measures. When I speak about

[R] Logistic regression

I have a data set to be analyzed using to binary logistic regression. The data set is iin grouped form. My question is: how I can compute Hosmer-Lemeshow test and measures like sensitivity and specificity? Any suggestion will be greatly appreciated. Thank you Endy [[alternative HTML

Re: [R] Logistic regression

] On Behalf Of Endy BlackEndy [pert...@gmail.com] Sent: 14 April 2013 19:05 To: R-Help Subject: [R] Logistic regression I have a data set to be analyzed using to binary logistic regression. The data set is iin grouped form. My question is: how I can compute Hosmer-Lemeshow test and measures like

[R] Logistic Regression with Offset value

Dear R friends. I´m trying to fit a Logistic Regression using glm( family='binomial'). Here is the model: *model-glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp, offset=(log(1/off)), data=mydata, family='binomial')* mydata has 76820 observations. The response variable f_ocur) is a 0-1.

[R] Logistic Regression with Offset value

Dear R friends. I´m trying to fit a Logistic Regression using glm( family='binomial'). Here is the model: *model-glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp, offset=(log(1/off)), data=mydata, family='binomial')* mydata has 76820 observations. The response variable f_ocur) is a 0-1.

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

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 did not get .50) I am basically testing the performance of classifiers. Here is the code: n - 1000; # Sample size

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

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

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 model is simply a result of the sample size (200,000 cases). I'd rather use

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

[R] logistic Regression with SSlogis but y == 0 ?!

Hello there! I got some data with x and y values. there are some y == 0. This is a problem for the selfstarting regression model SSlogis. The regression works if I use a non selfstarting model. The formula is the same. But this needs very detailed information of the starting list. I dont have

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

[R] logistic regression

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 Flies,main=Number of Living Fruit Flies vs Day,pch=16) alive

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

[R] Logistic regression

Hi does anyone know how to do a logistic regression in R? any help appreciated Carl -- View this message in context: http://r.789695.n4.nabble.com/Logistic-regression-tp4512658p4512658.html Sent from the R help mailing list archive at Nabble.com.

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:

[R] Logistic regression using package sem

Hi! I constructed a model where most of the variables are linearly related to each other. However, the dependent variable is dichotomous, which means I should not try to fit a linear relationship between the other variables, which are continuos, and this variable. I'm wondering how I would have

[R] Logistic Regression Coding Help

Hello. I am beginning to analyze my work and have realized that a simple chi-square analysis will not suffice for my research, with one notable reason is that data are not discrete.  Since my data fit the assumptions of a logistic regression, I am moving forward with this analysis.  With

[R] Logistic regression with non-gaussian random effects

Hi, does anyone know of an implementation of generalized linear models with random effects, where the random effects are non-gaussian? Actually, what I need is to do a logistic regression (or binomial regression) where the linear predictor in addition to fixed effects and gaussian random

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

[R] Logistic Regression

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 https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do

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

[R] Logistic Regression with genetic component

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 artificially fertilize and generate embryos with. I

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

[R] 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:

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

[R] logistic regression by glm

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) + as.factor(2281517), family =

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

[R] logistic Regression using lmer

Hi can we perform logistic regression using lmer function. Please help me with this -- View this message in context: http://r.789695.n4.nabble.com/logistic-Regression-using-lmer-tp4034056p4034056.html Sent from the R help mailing list archive at Nabble.com.

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

[R] Logistic Regression - Variable Selection Methods With Prediction

Hello, I am pretty new to R, I have always used SAS and SAS products. My target variable is binary ('Y' and 'N') and i have about 14 predictor variables. My goal is to compare different variable selection methods like Forward, Backward, All possible subsests. I am using misclassification rate to

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