similar to: Constraining linear regression model

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

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:

Hello All: I have a binomial model with one covariate, x1, treated as a factor with 3 levels. The other covariate is measured x2 <- 1:30. The response, y, is the proportion of successes out of 20 trials. glm(cbind(y, 20 - y) ~ x1 * x2, family = binomial) Now, I would like to constrain the cofficients on 2 levels of the factor, x1, to be identical and test the difference between these

http://r.789695.n4.nabble.com/file/n3031344/RSV.Rdata RSV.Rdata I want to split my dataset to training set and test set using kennard-stone(KS) algorithm, it is lucky there is R packages soil.spec to implement it. but when I used it to my dataset, it does not work, who can help me, how reasons is it, below, it is my code, and my data in the attachment.

Does anyone know if there is a way to constrain two virtual registers to be allocated to the same physical register? Tia. Reed

On 06/10/2014 05:51 PM, Pete Cooper wrote: > Hi Reed > > You can do this on the instruction itself by telling it 2 operands > must be the same register. For example, from X86: > > let Constraints = "$src1 = $dst" in > defm INSERTPS : SS41I_insertf32<0x21, "insertps">; > > Thanks, Hi Pete, Sorry. I should have been more specific. I'm

I have a WLS regression with 1 dependent variable and 3 independent variables. I wish to constrain the predicted values (the fitted values) so that they are greater than zero (i.e. they are positive). I do not know how to impose this constraint in R. Please respond if you have any suggestions. There are some previous postings about constraining the coefficients, but this won't accomplish

Hello, I have been hunting a memory-leak warning in vring_add_indirect: unreferenced object 0xffff88003d467e20 (size 32): comm "softirq", pid 0, jiffies 4295197765 (age 6.364s) hex dump (first 32 bytes): 28 19 bf 3d 00 00 00 00 0c 00 00 00 01 00 01 00 (..=............ 02 dc 51 3c 00 00 00 00 56 00 00 00 00 00 00 00 ..Q<....V....... backtrace:

Hello, I have been hunting a memory-leak warning in vring_add_indirect: unreferenced object 0xffff88003d467e20 (size 32): comm "softirq", pid 0, jiffies 4295197765 (age 6.364s) hex dump (first 32 bytes): 28 19 bf 3d 00 00 00 00 0c 00 00 00 01 00 01 00 (..=............ 02 dc 51 3c 00 00 00 00 56 00 00 00 00 00 00 00 ..Q<....V....... backtrace:

Good afternoon Running R2.10.0 on Windows I have a data frame that includes (among much else) a factor (In_2006) and a continuous variable (math_3_4). I would like to find the 2 cases for In_2006 = 0 that are closest to each case where In_2006 = 1. My data looks like In_2006 math_3_4 0 55.1 1 51.6 1 18.1 1 26.6 1 14.1

Dear R users, When I use aggregate with table as FUN, I get what I would call a strange behaviour if it involves numerical vectors and one "level" of it is not present for every "levels" of the "by" variable: --------------------------- > df <- data.frame(A=c(1,1,1,1,0,0,0,0),B=c(1,0,1,0,0,0,1,0),C=c(1,0,1,0,0,1,1,1)) >

Hello, I'm working on an structural VAR using the var command to estimate and the svar command on the resultant object (package: vars). I want to constrain coefficients to equal one another, but that value to be estimated. So for the A matrix, I want A[2,1]=A[1,2] to be my constraints. Can this be done with this package? If so, how? If not, is there another package that it might be done with

Proposal That a new mailing list be established that pertains exclusively to R documentation. The purpose of the list would be to discuss weak sections of the documentation and establish fixes for those weak spots. Pro If it works, there would be better documentation. It would be an excellent opportunity for newish and/or less technical people to contribute to R. In some respects such people

Hello Running R2.9.2 on Windows XP I am puzzled by the performance of LME in situations where there are missing data. As I understand it, one of the strengths of this sort of model is how well it deals with missing data, yet lme requires nonmissing data. Thus, m1.mod1 <- lme(fixed = math_1 ~ I(year-2007.5)*TFC_, data = long, random =

Hi, We could use qqplot to see how two distributions are different from each other. To show better how they are different (departs from the straight line), how is it possible to plot the straight line that goes through them? I am looking for some thing like qqline for qqnorm. I thought of abline but how to determine the slope and intercept? Best wishes, Carol

Good afternoon Using R 2.9.2 on a machine running Windows XP I have a longitudinal data set, with data on schools and their test scores over a four year period. I have centered year, and run the following m1.mod1 <- lme(fixed = math_1 ~ I(year-2007.5)*TFC_, data = long, random = ~I(year-2007.5)|schoolnum, na.action = "na.omit") where

Don't use aggregate's simplify=TRUE when FUN() produces return values of various dimensions. In your case, the shape of table(subset)'s return value depends on the number of levels in the factor 'subset'. If you make B a factor before splitting it by C, each split will have the same number of levels (2). If you split it and then let table convert each split to a factor, one

Hi All, I would like to use Kennard-Stone algorithm for splitting a dataset. > mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/binary.csv ")) > library("soil.spec") > ken.sto(mydata,per.n=0.3) Error in ken.sto(mydata, per.n = 0.3) : subscript out of bounds I found that other people run into this problem as well:

Hello! I would like to use constrain to sum coeficients of a factor to 0 instead of classical corner contraint i.e. I would like to fit a model like lm(y ~ 1 + effectA + effectB) and say get parameters intercept effectA_1 effectA_2 effectB_1 effectB_2 effectB_3 where effectA_1 represents deviation of level A_1 from intercept and sum(effectA_1, effectA_2) = 0 and the same for factor B. Is

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 *