similar to: Non-negativity constraints for logistic regression

Dear R users, I am currently attempting to fit logistic regression models in R, where the slopes should be restricted to positive values. Although I am aware of the package nnls (which does the trick for linear regression models), I did not find any solution for logistic regression. If there is any package available for this purpose, I would be interested to know them. Alternatively, I realize

Hi, recently I saw but did not pay too much attention to a question that concerned regression with positive coefficients. In Splus, thereis the nnls() function that can be used if I am not wrong, but what about R ? Now I have the same problem: doing a logistic regression under constraint that coefs are non negative. What can I do with R? is there a (weighted) nnls() counterpart available? Thanks

Hi all, I am trying to do an inversion of electromagnetic data with non-negative least squares method (Tikhonov regularisation) and have got it programmed in S-Plus. However I am trying to move all my scripts from S-Plus to R. Is there an equivalent to nnls.fit in R? I think this can be done with pcls? Right? S-Plus script: A, L and data are matrices, lambda is a vector of possible lambda

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.

Hi, is it possible in one of the libraries on linear methods to constrain the coefficients to be positive? Thanks Chris -- Christoph M. Friedrich | mailto:friedrich at computer.org Gesellschaft f?r Modulfermenterbau mbH (GfM mbH) | http://www.tussy.uni-wh.de/~chris Alfred-Herrhausen Str. 44 ; D-58455 Witten, Germany

I would really appreciate it if someone can give suggestions on how to do spectra fitting in R using ordinary least square fitting and non-negativity constraints. The lm() function works well for ordinary least square fitting, but how to specify non-negativity constraints? It wouldn't make sense if the fitting coefficients coming out as negative in absorption spectra deconvolution. Thanks.

Dear All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is

Hi, I was hoping to get some advice on how to derive estimates of slopes from four parameter logistic models fit with SSfpl. I fit the model using: model<-nls(temp~SSfpl(time,a,b,c,d)) summary(model) I am interested in the values of the lower and upper asymptotes (parameters a and b), but also in the gradient of the line at the inflection point (c) which I assume tells me my rate of

Thank you Brian Ripley, Gardar Johannesson, and Marcel Wolbers for your prompt and friendly help! I will share any further learnings as I move through these suggestions. -Bob Abugov Brian Ripley wrote: I just use optim() on the sum of squares with non-negativity constraints. That did not exist in 1999. Gardar Johannesson wrote: You can always just use the quadratic programing library in R

Hi everyone, I'm a bit new here (and new to R), and I was trying to do an OLR, and testing the parallel slope assumption seems be very important. I browsed through past postings, and didn't find much to help me in this area. I was wondering if anyone knew how I could go about doing this. Thank you. -- View this message in context:

Dear all, I want to find the optimal values of a vector, x (with 6 elements) say, satisfying the following conditions: 1. for all x>=0 2. sum(x)=1 3. x[5]<=0.5 and x[6]<=0.5 For the minimisation I'm using nlminb and to satisfy the first 2 conditions the logistic transformation is used with box constraints for condition 3. However, I don't seem to be able to get the values x

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

Hello colleagues, This is a follow up to a question I posed in November regarding an analysis I was working on. Thank you to Dr. Brian Ripley and Dr. John Fox for helping me out during that time. I am conducting logistic regression on data set on psi (ESP) ganzfeld trials. The response variable is binary (correct/incorrect), with a 25% guessing base rate. Dr. Ripley suggested that I

Hi, I was wondering if someone could please help me. I am doing a logistic regression to compare size at maturity between 3 seasons. My model is: fit <- glm(Mature ~ Season * Size - 1, family = binomial, data=dat) where Mature is a binary response, 0 for immature, 1 for mature. There are 3 Seasons. The Season * Size interaction is significant. I would like to compare the size at 50%

lrm does some binning to make the calculations faster. The exact calculation is obtained by running f <- lrm(...) rcorr.cens(predict(f), DA), which results in: C Index Dxy S.D. n missing 0.96814404 0.93628809 0.03808336 32.00000000 0.00000000 uncensored Relevant Pairs Concordant Uncertain 32.00000000

Hello R experts, I have two questions related to the nnls library (http://www.inside-r.org/packages/cran/nnls), and more broadly to linear regression with positive coefficients. Sample code is below the Qs. Q1: Regular regression (with lm) gives me the significance of each variable. How do I get variable significance with nnls? If there's no ready function, any easy way to manually derive

Good day Sir/Ma'am! This is Alyssa Fatmah S. Mastura taking up Master of Science in Statistics at Mindanao State University-Iligan Institute Technology (MSU-IIT), Philippines. I am currently working on my master's thesis titled "Comparing the Three Estimation Methods for the Four Parametric Logistic (4PL) Item Response Model". While I am looking for a package about Markov chain

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the

I know of no existing functions for estimating the parameters of this model using MCMC or MML. Many years ago, I wrote code to estimate this model using marginal maximum likelihood. I wrote this based on the using nlminb and gauss-hermite quadrature points from statmod. I could not find that code to share with you, but I do have code for estimating the 3PL in this way and you could modify the

Dear guRus, I am doing a logistic regression using restricted cubic splines via rcs(). However, the fitted probabilities should be nondecreasing with increasing predictor. Example: predictor <- seq(1,20) y <- c(rep(0,9),rep(1,10),0) model <- glm(y~rcs(predictor,n.knots=3),family="binomial") print(1/(1+exp(-predict(model)))) The last expression should be a nondecreasing