search for: linearmodel

...MODEL VERIFICATION FIRST!!!!! # # Basic Look at the raw data hist(modelSource) plot(density(modelSource,na.rm=TRUE)) plot(sort(modelSource),pch=".") for(i in names(mcReg)) { pairs(modelSource ~ .,mcReg[[i]], main=paste("Model - MIPS vs",i)) } #Build the list to store our results linearModel <- list() residuals <- list() arima_Fit <- list() arima_AO <- list() arima_IO <- list() newcReg <- list() newfReg <- list() newmcReg <- list() newmfReg <- list() newFit <- list() newForecast <- list() # Following won't work until mcReg contains full variety line...

...ng? Thanks a lot. for (i in 92:99){ cat("\n", "=============================================================\n", "YEAR =",i,"\n", "=============================================================\n" ) canos1 <- subset (canos, canos$YEAR==i) LinearModel.1 <- lm(MM_P ~ BLOC + TIL, data=canos1) summary(LinearModel.1) Anova(LinearModel.1, type="II") Anova(LinearModel.1, type="III") } -- ************************************************** Jorge Lampurlan?s Castel Departament d'Enginyeria Agroforestal Escola T?cnica S...

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,...

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 ~ . -ACF-MonthlyST1-MonthlyST2-MonthlyBLA,mcReg) LinearModel1.res <- residuals(linearModel1) LinearModel2.res <- residuals(l...

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 + HLY.MEMORIAL + HLY.J4 + HLY.LABOR + HLY.COLUMBUS + HLY.VETS + HLY.THANKS + HLY.XMAS...

...odel 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 right. > Dataset$Sessn <- as.factor(Dataset$Sessn) > LinearModel.1 <- lm(Response ~ Sessn*Trtmt, data=Dataset) > summary(LinearModel.1) All, good so far, but I have problem understanding "idata=" and "idesign=" functions pertaining to my example. Session is my repeated measure (Sessn 1 and Sessn 2 = two sessions, in reality I have mo...

...odel 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 right. > Dataset$Sessn <- as.factor(Dataset$Sessn) > LinearModel.1 <- lm(Response ~ Sessn*Trtmt, data=Dataset) > summary(LinearModel.1) All, good so far, but I have problem understanding "idata=" and "idesign=" functions pertaining to my example. Session is my repeated measure (Sessn 1 and Sessn 2 = two sessions, in reality I have mo...

...ES 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 checks (many suggested by Tony Christensen). o Documentation for Recode, Compute, linearModel, and generalizedLinearModel dialogs. More extensive documentation for the Commander. o Changes to list*() utility functions to ensure that all objects of a given class are found, and not masked by local variables or objects in the base package. o Many small changes to ensure that obj...

...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) white.test(LinearModel) Hope this helps. Sacha ________________________________ De : Deepak Singh <sdeepakrhelp at gmail.com> ? : r-help at r-project.org Envoy? le : Lundi 4 avril 2016 10h40 Objet : [R] Test for Homoscedesticity in R Without BP Test Respected Sir, I am doing a project on multiple linear...

...tilize the function to carry out the White test as illustrated in: example("CigarettesB", package = "AER") (Of course, the AER package needs to be installed first.) > The White test with R > > install.packages("bstats") > library(bstats) > white.test(LinearModel) That package is no longer on CRAN as it took the code from bptest() without crediting its original authors and released it in a package that conflicted with the original license. Also, the implementation did not check for potential problems with dummy variables or interactions mentioned above...

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

...effectively constant (that is x are not independent for all choices of components of z). When the resulting fit is fed into predict() the result is NA - this is not the same behaviour as models returned by say lm which ignore missing coefficients. I have fixed this by defining my own statsModel (myLinearModel - imaginative) which also ignores such coefficients when predicting. The second problem I have is that I get "Cholesky not positive definite" errors at some nodes. I guess this is because of numerical error and degeneracy in the covariance matrix? Any thoughts on how to avoid having this...

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