similar to: R Codes for fitting Logistic Regression for Multivaraite Data

Hi I want to do logistic regression for multinomial data. How can I do it in R? Thanks a lot Nimal Fernando [[alternative HTML version deleted]]

Sir, I want to fit a multinomial logistic regression in R.I think mlogit() is the function for doing this. mlogit () is in packege globaltest.But, I can not install this package. I use the following: install.packages("globaltest") Can you help me? Regards, Suman Dhara [[alternative HTML version deleted]]

Hello, I have this data: Time AMP 0 0.2000000 10 0.1958350 20 0.2914560 40 0.6763628 60 0.8494534 90 0.9874526 120 1.0477692 where AMP is the concentration of this metabolite with time. If you plot the data, you can see that it could be fitted using a logistic regression. For this purpose, I used this code: AMP.nls <- nls(AMP~SSlogis(Time,Asym, xmid, scal), data

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 Aly

Hi, I am trying to fit a logistic regression using glm, but my explanatory variables are of mixed type: some are numeric, some are ordinal, some are categorical, say If x1 is numeric, x2 is ordinal, x3 is categorical, is the following formula OK? *model <- glm(y~x1+x2+x3, family=binomial(link="logit"), na.action=na.pass)* * * *Thanks,* * * *-Jack* [[alternative HTML version

Hello, I'm trying to fit a logistic curve to data but I'm having a hard time discovering how. Every tutorial I've come across either assumes the logistic curve has 0<y<1 or assumes I have multiple categories of data I simply have two vectors, a and b, of equal length with no missing data, and I suspect they follow a logistic curve. The vectors are a<-c(39609, 39643,

Hi all, Where f(x) is a logistic function, I have data that follow: g(x) = f(x)*.5 + .5 How would you suggest I modify the standard glm(..., family='binomial') function to fit this? Here's an example of a clearly ill-advised attempt to simply use the standard glm(..., family='binomial') approach: ######## # First generate some data ######## #define the scale and location of

I am trying to find out if someone has implemented a (McFadden-type) multivariate binary logistic regresssion package for R? From what I can tell, this is not available for R. Thank you, Lynne Baker [[alternative HTML version deleted]]

Hello, I am trying to fit my Elisa results (absorbance readings) to a standard curve. To create the standard curve model, I performed a 4-parameter logistic fit using the 'drc' package (ExpectedConc~Absorbance). This gave me the following: > FourP A 'drc' model. Call: drm(formula = Response ~ Expected, data = SC, fct = LL.4()) Coefficients: b:(Intercept) c:(Intercept)

Hi all, I am trying to fit a logistic regression for a bivariate response using five independent variables in a stepwise procedure. My outputs look okay but does any one know (or is there any literature on) how the confidence intervals are calculated for the reported odds ratios..? Thanks! [[alternative HTML version deleted]]

Dear R help list, I am trying to do a logistic regression where I have a categorical response variable Y and two numerical predictors X1 and X2. There are quite a lot of missing values for predictor X2. eg., Y X1 X2 red 0.6 0.2 * red 0.5 0.2 * red 0.5 NA red 0.5 NA green 0.2 0.1 * green 0.1 NA green 0.1 NA green 0.05 0.05 * I am wondering can I combine X1 and

Dear friends, I hope this email finds you all well. This is the dataset I am working with: dput(random_mod12_data2) structure(list(Index = c(1L, 5L, 11L, 3L, 2L, 8L, 9L, 4L), x = c(5, 13, 25, 9, 7, 19, 21, 11), n = c(500, 500, 500, 500, 500, 500, 500, 500), r = c(100, 211, 391, 147, 122, 310, 343, 176), ratio = c(0.2, 0.422, 0.782, 0.294, 0.244, 0.62, 0.686, 0.352)), row.names = c(NA, -8L),

Dear R-Helpers, I'm working with immunoassay data and 5PL logistic model. I wanted to experiment with different forms of weighting and parameter selection, which is not possible in instrument software, so I turned to R. I am using R 2.14.2 under Win7 64bit, and the 'nls' library to fit the model - I started with the same model and weighting type (1/y) as in the instrument to see

Dear ALL, I'm trying to figure out what the percentage effects are in a logistic regression. To be more clear, I'm not interested in the effect on y of a 1-unit increase in x, but on the percentage effect on y of a 1% increase in x (in economics this is also often called an "elasticity"). For example, if my independent variables are in logs, the betas can be directly

I would like to know how R computes the probability of an event in a logistic model (P(y=1)) from the score s, linear combination of x and beta. I noticed that there are differences (small, less than e-16) between the fitting values automatically computed in the glm procedure by R, and the values "manually" computed by me applying the reverse formula p=e^s/(1+e^s); moreover I noticed

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 problematic invasive plant species (one by one) by a set of recorded ecological / geographical parameters. i'd be glad if someone would comment on applying this

This news item in a data mining newsletter makes various claims for a technique called "Reduced Error Logistic Regression": http://www.kdnuggets.com/news/2007/n08/12i.html In brief, are these (ambitious) claims justified and if so, has this technique been implemented in R (or does anyone have any plans to do so)? Tim C

Hi all, First, a quick thank you for R; it's amazing. I am trying to fit models for a count dataset following the overdispersed logisitic regression approach outlined in Baggerly et al. (BMC Bioinformatics, 5:144; Annotated R code is given at the end of the paper) but R is returning an error with the data below. Any help in understanding or overcoming this obstacle is appreciated.

Hi all. Are there any R functions around that do quick logistic regression with a Gaussian prior distribution on the coefficients? I just want posterior mode, not MCMC. (I'm using it as a step within an iterative imputation algorithm.) This isn't hard to do: each step of a glm iteration simply linearizes the derivative of the log-likelihood, and, at this point, essentially no

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by