similar to: Logistic regression with constrained parameter ??

I am trying to automatically construct a distance function from a training set in order to use it to cluster another data set. The variables are nominal. One variable is a "class" variable having two values; it is kept separate from the others. I have a method which constructs a distance matrix for the levels of a nominal variable in the context of the other variables. I want to

Dear R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here is the function, logitregVR <- function(x, y, wt =

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

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

Hello R helpers, I am trying to do a linear OLS regression of y on two variables x1 and x2. I want to constrain the coefficients of x1 and x2 to sum up to 1. and therefore run a constrained OLS. Can anybody help with this? (I have seen some answers to similar questions but it was not clear to me what I need to do) - I have tried the lm function with offset but I must not have used it properly.

Greetings, I am struggling a bit with a non-linear regression. The problem is described below with the known values r and D inidcated. I tried to alter the start values but get always following error message: Error in nlsModel(formula, mf, start, wts): singular gradient matrix at initial parameter estimates Calls: nls -> switch -> nlsModel I might be missing something with regard to the

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 *

Dear R-Users, Being an engineer and not a statistician, my desired course of action may either be impossible or very simple. I am attempting to fit a non-linear model to some measured data. One term in the model contains a square-root, but in the course of regression, this term turns negative and an error occurs. I started using Micrsoft's Excel Solver, and then I turned to NIST's

All, Could anyone recommend a package that allows the user to constrain the coefficients from a multiple regression equation? I tried using the gl1ce function in lasso2, but couldn't get it to work. I created a contrived example to illustrate my starting point. data(cars) fmla <- formula(dist ~ speed) gl1c.E <- gl1ce(fmla, data = cars) gl1c.E gl1c.E <- gl1ce(fmla, data =

Hello, all, I had problems with an extension to a classic optimization problem. The target is to minimize a quadratic form a'Ma with respect to vector b, where vector a=(b',-1)', i.e., a is the expand of b, and M is a symmetric matrix (positive definite if needed). One more constrain on b is b'b=1. I want to solve b given M. I tried but it seems impossible to find an analytic

I'm relatively new to R, but I'm attempting to do a non-linear maximum likelihood estimation (mle) in R, with the added problem that I have a non-linear constraint. The basic problem is linear in the parameters (a_i) and has only one non-linear component, b, with the problem being linear when b = 0 and non-linear otherwise. Furthermore, f(a_i) <= b <= g(a_i) for some (simple) f

I wish to simulate: Ho: Odds Ratio =1 H1 Odds Ratio <> 1 Can any of the R users show me how to generate this data for say 20% from Ho and 80% from H1? Thanks, Jim [[alternative HTML version deleted]]

Dear all, I have something about function outer() that I can't understand. Just see the following example. The two NaNs are due to 0/0, but I can't figure out the cause of the last two errors. I wonder if some one can explain this for me. ___________________________________________________________________ > sx=rbinom(10,1,0.5);ot=rbinom(10,1,0.5);ag <- rbinom(10,100,0.3);ho <-

There's something peculiar that I do not understand here. However, did you realize that the thing you are assigning into parts of `a' is NULL? Check you're my.test.boot.ci.1: It's NULL. Be that as it may, I get: > a <- data.frame(matrix(1:4, nrow=2), X3=NA, X4=NA) > a X1 X2 X3 X4 1 1 3 NA NA 2 2 4 NA NA > a[a$X1 == 1,]$X3 <- NULL > a X1 X2 X3 X4 1 1

Ok i've comment in between de debug logs.   Check my comments and add the needed info.   Van: Ian Coetzee [mailto:samba at iancoetzee.za.net] Verzonden: woensdag 10 april 2019 10:17 Aan: L.P.H. van Belle CC: samba at lists.samba.org Onderwerp: Re: [Samba] chown: changing ownership of 'test': Invalid argument Hi Louis, Thank you. I will add those line and test. Will revert

Hello, all! Well, third time is *not* the charm for me. (I've been through the process 3 times with 3 different DCs). I am trying to set up a member server, using Samba 4.1.14, and washing out when getting to the winbind testing. I've tried ignoring the failure and pressing on, but that didn't get anywhere. In this instance, I have a freshly-installed, configured and functioning

UseRs, I am working on a dataset (see small example below) where individuals were followed on a specific date-time combo and multiple repeated measurements were taken (e.g., height in meters, behavior class in 2 letter code). Observation numbers varied between individual (ranging from 1 observation for each date-time combo to >50) I am trying to summarize the data into 1 row per

Dear R Users, I am looking for some guidance on setting up an optimisation in R with non-linear constraints. Here is my simple problem: - I have a function h(inputs) whose value I would like to maximise - the 'inputs' are subject to lower and upper bounds - however, I have some further constraints: I would like to constrain the values for two other separate function f(inputs) and

Hello, I've searched for an answer to no avail. I am wondering if anyone knows how to constrain certain correlations to be equal. I have family data with 2 twins per family plus up to 2 siblings. I would like to somehow constrain all the sibling correlations (twin-sib and sib-sib) to be the same while allowing the twin-twin correlation to be different. Here is some simulated code:

Dear R-experts, I am running R version 2.7.1 on Windows Vista. I have a small dataset which consists of ?chick ID?, ?year (0, 1)?, ?hatching order [HO, defined as first, second and third-hatched chick]?, and the binary outcome of interest ?death (0, 1)?. So a subset of my dataset looks like this on a txt file: y ID Yr HO 1 1 1 First 0 2 1 First 0 3 1 Second 0 4 1 First 1 5 1 First 0 6 1 Third