similar to: logit newbie question...

Moved to R-help because there's no obvious financial content. Michael On Sat, Apr 6, 2013 at 10:56 AM, Stat Tistician <statisticiangermany at gmail.com> wrote: > Hi, > I want to implement the EM algorithm manually, with my own loops and so. > Afterwards, I want to compare it to the normalmixEM output of mixtools > package. > > Since the notation is very advanced, I

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.

Hello, I have written a small function ('JostD' based upon a recent molecular ecology paper) to calculate genetic distance between populations (columns in my data set). As I have it now I have to tell it which 2 columns to use (X, Y). I would like it to automatically calculate 'JostD' for all combinations of columns, perhaps returning a matrix of distances. Thanks for any help

I have used the following code to obtain a max likelihood estimator for a logit regression. The final command invokes ‘optim’ to obtain the parameter estimates. The code works OK but I want to use the ‘mle’ function in the ‘stats4’ package instead of directly calling ‘optim’. Can someone please figure out the command to do this? Thank you in advance. Martin # mlelo.r - maximum

In vorbis/lib/mdct.h the following are defined: for integer: #define TRIGBITS 14 #define cPI3_8 6270 #define cPI2_8 11585 #define cPI1_8 15137 #define FLOAT_CONV(x) ((int)((x)*(1<<TRIGBITS)+.5)) for floats: #define cPI3_8 .38268343236508977175F #define cPI2_8 .70710678118654752441F #define cPI1_8 .92387953251128675613F #define FLOAT_CONV(x) = x Could someone explain where these values

Hi All, Sorry to bother you. I'm trying to estimate a set of discrete choice data in R with mixed logit models where one coefficient is random and normally distributed. I've searched on the R help archive and don't see much information very specific to what I'm doing, so I write the code myself, which involves simulated maximum likelihood. But it doesn't work, as I compare

Dear R listers, I am learning the MLE utility optim() in R to program ordered logit models just as an exercise. See below I have three independent variables, x1, x2, and x3. Y is coded as ordinal from 1 to 4. Y is not yet a factor variable here. The ordered logit model satisfies the parallel regression assumption. The following codes can run through, but results were totally different from what I

Hell all. I¡¯m trying to estimate mixed logit model using MSLE. In order to see that mixed logit model works better than simple logit model ( the logit model with fixed coefficient) I simulated a dataset with random coefficients and tried to fit the data with both mixed logit and simple logit model. Because my mixed logit model contains analytically intractable integrations, I applied

I know this has been dealt with before on this list, but the previous messages lacked detail, and I haven't figured it out yet. The model is: \x_{ij} = \mu + \alpha_i + \beta_j \alpha is a random effect (subjects), and \beta is a fixed effect (condition). I have a link function: p_{ij} = .5 + .5( 1 / (1 + exp{ -x_{ij} } ) ) Which is simply a logistic transformed to be between .5 and 1.

Hello, Could anybody provide me with codes (procedure) how to obtain Conditional Logit (McFadden) and Mixed Logit (say, assuming normal distribution) estimates in R? Thanks, David U. -- View this message in context: http://www.nabble.com/Conditional-Logit-and-Mixed-Logit-tf4489238.html#a12802959 Sent from the R help mailing list archive at Nabble.com.

I'm running a multinomial logit in R using the Zelig packages. According to str(trade962a), my dependent variable is a factor with three levels. When I run the multinomial logit I get an error message. However, when I run 'model=logit' it works fine. any ideas on whats wrong? ## MULTINOMIAL LOGIT anes96two <- zelig(trade962a ~ age962 + education962 + personal962 + economy962 +

Hi I am carrying out some logit regressions and want to (a) make sure I'm taking the right approach and (b) work out how to carry out some additional analysis. So, to carry out a logit regression where the dependent variable is a factor db, I use something like: res1_l <- glm(formula = db ~ y1 + + y5, family = binomial(link = "logit")) summary(res1_l) ...which is, I hope

Dear Chales, Thanks for your quick reply. I have Raspberry with last Kernel [Linux pi1 3.18.6+ #753 PREEMPT Sun Feb 8 14:47:22 GMT 2015 armv6l GNU/Linux] If <upsdrvctl>, <upsd> and <upsmon> are stopped <nutdrv_atcl_usb> Works well without any error as "Device or resource busy". If I run <upsc> command never receive any value from my ups <upsc ups at

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,

Hello, I'd like to fit a logit function to my data. The data is distributed like a logit (like in this plot on wikipedia http://en.wikipedia.org/wiki/File:Logit.png) but the values on the x-axis are not between 0 and 1. I don't think using a glm is the solution because I simply want to infer the parameters of the logit function (offset, compression, slope...), so I can apply it to all

Dear all, I am using glm function in order to estimate a logit model i.e. glm(Y ~ data[,2] + data[,3], family = binomial(link = "logit")). I also created a function that estimates logit model and I would like it to compare it with the glm function. So, does anyone know what optimizer or optimization method glm uses in order to derive the result? Thank you Dimitris -- View this

Hi all, I'm using a logistic regression model (created with 'glm') with 3 variables to separate true positives from errors in a data set. All in all it seems to perform quite well, but for some reason the logit values seem to be much lower that they should be. What I mean is that in order to get ~90% sensitivity and ~90% precision I have to set my logit cutoff at around -1 or 0. From

I am new to R but I have managed to use mlogit to run multivariate logit models successfully. My data violates the Independence of Irrelevant Alternatives assumption and now I would like to run a mixed logit model. It is a "wide" data set with 9 independent (individual) variables and three choices (variable Y). The database is in a cvs file called CAU. This is the code I have run

Good morning, I'd like to estimate a simple multinomial logit model in R (not a McFadden conditional logit). For instance, I'd like to estimate the probability of someone having one of eight titles in a company with the independent variables being the company characteristics. A binary logit is well documented. What about the multinomial? Thanks, Walt Paczkowski

Hello, I have a bit of a tricky puzzle with trying to implement a logit model as described in a paper. The particular paper is on horseracing and they explain a model that is a logit trained "per race", yet somehow the coefficients are combined across all the training races to come up with a final set of coefficients. My understanding is that they maximize log likelihood across the