search for: linearmodels

I am working on a script that takes numeric performance indicators and runs them against a series of regressors (dummy regressors, yes\no stuff via 0 and 1, e.g. Was is Christmas this week 0=no, 1=yes). The script is as follows (Written as a function): -- Begin Script -- doEnv <- function(HOUR,ENVNAME,REPORTNAME) { library(RODBC) library(forecast) library("geneplotter")

Hello, With this program I try to repeat analysis for different years. The results of the analysis are not printed when in the loop, except for the year sequence. What is wrong? Thanks a lot. for (i in 92:99){ cat("\n", "=============================================================\n", "YEAR =",i,"\n",

Dear List, Consider the following example x=c(1,2,3,4,5) y=c(2,4,6,8,10) linearmodel=lm(y~x) To predict a y-value if you know the corresponding x value is very easy with the command predict. predict(linearmodel, newdata=(x=1.5)) The other way around, to predict an x-value with a corresponding y-value, doesn't work unfortunately. Is there another function that can do that, or do I need to

to clean up some code I would like to make a list of arbitrary length to store?various objects for use in a loop sample code: ############ BEGIN SAMPLE ############## # You can see the need for a loop already linearModel1=lm(modelSource ~ .,mcReg) linearModel2=step(linearModel1) linearModel3=lm(modelSource ~ .-1,mcReg) linearModel4=step(linearModel3) #custom linearModel5=lm(modelSource ~ .

Why are my coefficients getting appended with a 1? It borks a match I do later against the original list that doesn't have the random 1 added to the end. > linearModel[[1]] Call: lm(formula = modelSource ~ +UNITBUILD + UNITDB + ITBUILD + ITDB + UATBUILD + UATDB + HOGANCODE + RCF + ReleaseST1 + ReleaseST2 + ReleaseBLA + Small.Bank.Acquisitions + HLY.NewYear + HLY.MLK + HLY.PRES +

Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems that I am not getting something

[corrected dataset below] Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems

Dear list members, I've uploaded a new version of the Rcmdr package to CRAN. There are many additions and (I hope) improvements, as indicated in the relevant portion of the CHANGES file, reproduced below. As usual, comments, suggestions, and bug reports are appreciated. John ------------------------------------------- Version 0.8-4 o Minor bug fixes and additional input-error

Hi Deepak, In econometrics there is another test very often used : the white test. The white test is based on the comparison of the estimated variances of residuals when the model is estimated by OLS under the assumption of homoscedasticity and when the model is estimated by OLS under the assumption of heteroscedastic. The White test with R install.packages("bstats") library(bstats)

On Mon, 4 Apr 2016, varin sacha via R-help wrote: > Hi Deepak, > > In econometrics there is another test very often used : the white test. > The white test is based on the comparison of the estimated variances of > residuals when the model is estimated by OLS under the assumption of > homoscedasticity and when the model is estimated by OLS under the > assumption of

Respected Sir, I am doing a project on multiple linear model fitting and in that project I have to test Homoscedesticity of errors I have google for the same and found bptest for the same but in R version 3.2.4 bp test is not available. So please suggest me a test on homoscedesticity ASAP as we have to submit our report on 7-04-2016. P.S. : I have plotted residuals against fitted values and it is

Apologies for re-posting, my original message seems to have been overlooked by the moderators. ---------- Forwarded message ---------- From: Ed <icelus2k5 at gmail.com> Date: 11 October 2012 19:03 Subject: party for prediction To: R-help at r-project.org Hi there I'm experiencing some problems using the party package (specifically mob) for prediction. I have a real scalar y I want to

Dear all, I was reading last night the lm and the Formula manual page, and 'I have to admit that I had tough time to understand their syntax. Is there a simpler guide for the dummies like me to start with? I would like to thank you in advance for your help Regards Alex [[alternative HTML version deleted]]

Hi All, I have millions of regression lines to fit. So I am looking for the most efficient approach in R. Details: I have a large desing matrix X. The dimension is n by p. Each time when fitting the model, select rows from this matrix X and form a new design matrix, called X_current. There is another binary matrix M, with dim m by n, and each row is a 1*n vector. It helps to determin X_current.

Hi, I posted the message below last week, but no answers, so I'm giving it another attempt in case somebody who would be able to help might have missed it and it has now dropped off the end of the list of mails. I am fairly new to R and still trying to figure out how it all works, and I have run into a few issues. I apologize in advance if my questions are a bit basic, I'm also no