similar to: logistic regression with constrained coefficients?

On Fri, 30 Oct 2020, Pages, Herve wrote: > > On 10/29/20 23:08, Pages, Herve wrote: > ... >> >> I can think of 2 ways to move forward: >> >> 1. Keep I()'s current implementation but suppress the warning. We'll >> make the necessary adjustments to DataFrame() to repair columns supplied >> as I(<S4>) objects. Note that we would still be in

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 want to fit a logistic regression model under a specified null hypothesis, say Ho:Beta_k=1 Is there a way to constrain a parameter in glm.fit ?? Thanks in advance! --------------------------------- [[alternative HTML version deleted]]

Hi, I have a question about the "sext..to" instruction. In the document, I found two examples: %x = sext i8 -1 to i16 It means: i8 -1 = 1111 1111 --> 1111 1111 1111 1111 = i16 how can it determinate, that the i16 value %x positive is (65535)? And the second example: %y = sext i1 true to i32 1 --> 1111 1111 1111 1111 1111 1111 1111 1111 In this example, %y is -1 I'm not sure

Dear Masters, first I'd like to wish u all a great 2011 and happy holydays by now, second (here it come the boring stuff) I have a question to which I hope u would answer: I run a logistic regression by glm(), on the following data type (y1=1,x1=x1); (y2=0,x2=x2);......(yn=0,xn=xn), where the response (y) is abinary outcome on 0,1 amd x is any explanatory variable (continuous or not)

Hi Folks, I've just come across a kind of problem which leads me to wonder how to approach it in R. Basically, each a set of items is subjected to a series of "impacts" until it eventually "fails". The "force" of each impact would depend on covariates X,Y say; but as a result of preceding impacts an item would be expected to have a "cumulative

I am fittting a linner regression with constrained parameters, saying, all parameters are non-negative and sum up to 1. I have searched historical R-help and found that this can be done by solve.QP from the quadprog package. I need to assess the significance of the coefficient estimates, but there is no standard error of the coefficient estimates in the output. So I can not compute the p-value.

Dear list, In my previous email, the model I'd like to estimate is rea=a*st+b*mod+error, where a+b=1 and a,b>0. My apologies for the misunderstanding. Thanks for all your help, Jorge On Wed, Jul 23, 2008 at 3:35 PM, Jorge Ivan Velez <jorgeivanvelez@gmail.com> wrote: > > Dear list, > > I have a data set which looks like myDF (see below) and I'd like to >

Hi everyone, how can I calculate standardized logistic regression coefficients using R? I used "glm" resp. "lrm" from the design-package in order to calculate logistic regression coefficients but I'm wondering if there's a possibility to get standardized logistic regression coefficients? Thanks in advance! Alexander Roth

Dear all, I am looking for ways to compute standardized logistic regression coefficients. I found papers describing at least 6 different ways to standardize logistic regression coefficients. I also found a very old (Thu May 12 21:50:36 CEST 2005) suggestion by Frank E Harrell (one of the colleagues who frequently contribute on this list) saying... Design doesn't implement those because they

> I'm not sure about it, when sext to results a positve/negative value? sext does signed-extension, zext does unsigned-extension. This means that zext always extends by zero bits, while with sext the additional bits are all copies of the top bit of the original value. So with sext, if it was negative in the original type when considered as a signed value, then it will be negative in the

Hi there, I just don't find the solution on the following problem. :( Suppose I have a dataframe with two predictor variables (x1,x2) and one depend binary variable (y). How is it possible to define a glm object (family="binomial") with a user defined logistic function like p(y) = exp(a + c1*x1 + c2*x2) where c1,c2 are the coefficents which I define. So I would like to do no

Hello Everyone, I have what I think is a complex text parsing task. I've provided some sample data below. There's a relatively simple version of the coding that needs to be done and a more complex version. If someone could help me out with either version, I'd greatly appreciate it. Here are my sample data. haveData <- structure(list(profile_key = structure(c(1L, 1L, 2L, 2L, 2L,

Hi, When I include a categorical variable (RACE with 3 levels - "white", "black" and "other") in my logistic regression model, the correlation matrix of the coefficients gets messed up. I get something like: ----------------------------------------- Correlation of Coefficients: ( A L RACEb AGE , 1 LWT , 1 RACEblack 1

Hi, I'm trying to fit a proportional ordinal logistic model using function polr() (package MASS). Is there a way to fix certain betas in the regression (e.g. function arima() allows this by defining fixed ) Maybe there is another function than polr() which allows that? Thanks Kazys [[alternative HTML version deleted]]

Hello All, Can anyone tell help me understand why the function below doesn't work and how I can fix it? Below are some sample data, some code that works on individual rows of the data, and my attempt to translate that code into a function. My hope is to get the function working and then to apply it to the larger data frame using ddply() from the plyr package or possibly some other approach.

Hello all, I am trying to optimize my logistic regression model by using bootstrap. I was previously using SAS for this kind of tasks, but I am now switching to R. My data frame consists of 5 columns and has 109 rows. Each row is a single record composed of the following values: Subject_name, numeric1, numeric2, numeric3 and outcome (yes or no). All three numerics are used to predict

I think the argument supporting the use of bootstrap to determine coefficients, as opposed to just running linear regression on the whole dataset, is the comparison of Rsq and prediction errors between these two approaches - page 1502. There's a substantial difference in favor of the bootstrap approach. -- Michal J. Figurski Gustaf Rydevik wrote: > The url for the mentioned paper is

Dear all, I have a data frame of 6 columns and ~60000 rows which I hope to perform the following calculation on. For each row, I wish to determine whether there are a greater number of positive or negative numbers. Then, if there are more positive numbers in the row, count how many occur - but if there are more negative numbers in the row, count them instead and insert a minus symbol before

I have several hundred times series data sets and I am trying to use auto.arima(), in the forecasting package, to take a first stab at fitting an ARIMA model to each set. Ideally I would like to loop through each time series use auto.arima() to fit a model and output the R^2 of the model and the parameters. I know how to fit each model but I am having trouble generating the necessary output. I