search for: lmmodels

Dear community, I'm having this silly problem. I've a linear model. After fixing it, I wanted to know which data had studentized residuals larger than 3, so i tried this: d1 <- cooks.distance(lmmodel) r <- sqrt(abs(rstandard(lmmodel))) rstu <- abs(rstudent(lmmodel)) a <- cbind( mydata, d1, r,rstu) alargerthan3 <- a[rstu >3, ] And suddenly a[rstu >3, ] has

Hi, A following function was kindly provided by GGally?s maintainer, Barret Schloerke. function(data, mapping, ...) { p <- ggplot(data = data, mapping = mapping) + geom_point(color = I("blue")) + geom_smooth(method = "lm", color = I("black"), ...) + theme_blank() + theme(panel.border=element_rect(fill=NA, linetype =

Hello there. I uses the following codes for the purpose of variable selection. > lmModel <- lm(y~.,data.frame(y=y, x=x)) > step <- stepAIC(lmModel, direction="both") > step$anova Stepwise Model Path Analysis of Deviance Table Initial Model: y ~ x.Market.Price + x.Quantity + x.Country + x.Incoterm + x.Channel + x.PaymentTerm Final

Dear Community, I want to identify outliers in my data. I don't know how to use identify command in the plots obtained. I've gone through help files and use mahalanobis example for my purpose: NormalMultivarianteComparefunc <- function(x) { Sx <- cov(x) D2 <- mahalanobis(x, colMeans(x), Sx) plot(density(D2, bw=.5), main="Squared Mahalanobis distances, n=nrow(x),

Dear Help: I am trying to follow Professor Bates' recommendation, quoted by Professor Crawley in The R Book, p629, to determine whether I should model data using the 'plain old' lm function or the mixed model function lmer by using the syntax anova(lmModel,lmerModel). Apparently I've not understood the recommendation or the proper likelihood ratio test in question (or both) for I

Dear helpeRs- I'm using a for loop to create a series of models. I'm trying to assign a name to each model created, using the loop index. The loop gets stuck at the name of the model, giving the error "target of assignment expands to non-language object". The linear model runs without error; only the name is problematic. Here is the current loop syntax. The use of dat

Hello I want to compare the slope (let's say 'b') of a linear model obtained with lm() to a theoretical value (let's say 'th'). To do so, I think I should compute a 't value' using something like : (b - 'th')/standard.deviation(b) and then look at the p-value of this computed t. I don't understand how to do this in a simple way, just using lm()