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Hi, I have a question to ask. if in a linear regression model, the independent variables are not statistically significant, is it necessary to test these variables in a non-linear model? Since most of non-linear form of a variable can be represented to a linear combination using Taylor's theorem, so I wonder whether the non-linear form is also not statistically significant in such a

Please advice on the package I should use to run a linear regression model (weighted least squared) with linear equality constraint. I initially tried "constrOptim" but it turned out that it only supported inequality linear constraint. Thank you very much in advance. Cheers, Jon -- View this message in context:

Hello, I am looking for a measure of linearity in the relationship between two variables. Specifically, I have two variables for which the relationship is reasonably linear over a certain range of values, and then diverges from linearity at either end of the range, as one or other variable "saturates" at a maximum or minimum value. I want to identify the region of linearity, where

Hi, I'm new to R (though I have spent hours trying to learn how to use it) and also not very knowledgeable about statistics, so I hope you will excuse what may seem like a very basic question. I'm trying to use R to do an ANOVA analysis for some data with an unbalanced design, and while I was trying to figure that out, I got confused about the purpose of the "lm". All

Dear friends, I searched the R site and found a lot of results on general linear model and generalized linear model , and i was confused by them. Here, I only want to get some concise answers on the following questions and i'll study it by your hints: 1. Which function(package) could be used to fit the general linear model ? 2. Which function(package) could be used to fit the generalized

Dear List, how can I obtain the value of r suqared for a non-linear model? For linear models it can be found in the summary() of the model but for non-linear models I just don't know. Please help! Anna

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

I am trying to understand what is the difference between linear and polynomial kernel: linear: u'*v polynomial: (gamma*u'*v + coef0)^degree It would seem that polynomial kernel with gamma = 1; coef0 = 0 and degree = 1 should be identical to linear kernel, however it gives me significantly different results for very simple data set, with linear kernel

Anyone know how to get p-values for the t-values from the coefficients produced in vglm? Attached is the code and output ? see comment added to output to show where I need p-values + print(paste("********** Using VGAM function gamma2 **********")) + modl2<- vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c") + print(coef(modl2,matrix=TRUE))

Hi everyone, i?m a new user of R and i?m trying to translate an linear optimization problem from Matlab into r. The matlab code is as follow: options = optimset('Diagnostics','on'); [x fval exitflag] = linprog(f,A,b,Aeq,beq,lb,ub,[],options); exitflag fval x=round(x); Where: f = Linear objective function vector (vector of 45,rows) A = Matrix for linear inequality

Apologies for the long post. I'm trying to include what I hope are the relevant bits (output of lsusb -v and usbhid-ups) Long term goal: I've got a DIY UPS that I would like to get working with my QNAP NAS (which uses Linux and NUT underneath the hood) Immediate short-term problem: I can't get past running usbhid-ups on Ubuntu 23.10 with an Arduino Leonardo. I have an Arduino Micro

On examining non-linearity of Cox coefficients with penalized splines - I have not been able to dig up a completely clear description of the test performed in R or S-plus. >From the Therneau and Grambsch book (2000 - page 126) I gather that the test reported for "linear" has as its null hypothesis that the spline coefficient is the same at the center of basis. Thus, in the example

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

Dear all, can somebody give me some pointer how I can fit a "linear-by-linear association model" (i.e. loglinear model for the ordinal variables) in R? A brief description can be found here 'https://onlinecourses.science.psu.edu/stat504/node/141'. Thanks for your help

Hi all, I would like to test the homoegeneity of variances between several linear model for some analysis of covariance. It seems that the Bartlett's test is a good test to use but I am having problem using with linear model and I cannot find any examples on the internet. There are some examples for comparisons of variances but not linear models. If I take the hellung data set, which is

Hello, I have done some research about breakpoints (I am not a statistician) and I found out about the breakpoint, strucchange and segmented packages in R allowing to find breakpoints assuming linear model. However, I would like to fit a periodic time series with a non linear (periodic) model, and I was wondering how I could find breakpoints for this model in R. Is it even possible ? My model

Hi, I want to fit a linear model (without any random effect) with method "ml". I tried to use "glm" I found that there is no option for "ml" or "reml" and the default one is "reml". THen I tried to use "lme" but it requires a random effect. How can I fix this problem? Of course, it's not necessary to be "glm" or

Hi R experts, Could you please help me to fit a linear, cubic and quadratic curve in a figure? I was trying to show all these three fitting curves with different colour in one figure. I spent substantial time to figure it out, but I could not. I have given here a example and what I did for linear, but no idea for cubic and quadratic fitting curve > dput(test) structure(list(sp = c(4L, 5L,

the study design of the data I have to analyse is simple. There is 1 control group (CTRL) and 2 different treatment groups (TREAT_1 and TREAT_2). The data also includes 2 covariates COV1 and COV2. I have been asked to check if there is a linear or quadratic treatment effect in the data. I created a dummy data set to explain my situation: df1 <- data.frame( Observation =

Hello, we are currently working on my project that aims at improving the register allocation scheme by identifying if the interference graphs are chordal or not. we are working on the llvm compiler .we are forcing the compiler to use PBQP register allocation scheme by an option of ' ' regalloc=pbqp ' during the execution of prgm. we have been succesfull in accessing the interference