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se's and CI's for fitted lines in multivariate regression analysis

Rui Barradas 
Hello,

If you want confidence intervals for the beta coefficients of the model, 
try the following.

ci_lm <- function(object, level = 0.95){
     sfit <- summary(object)
     beta <- sfit$coefficients[, 1]
     se <- sfit$coefficients[, 2]
     df <- sfit$df[1]
     alpha <- 1 - level
     lower <- beta + qt(alpha/2, df = df)*se
     upper <- beta + qt(1 - alpha/2, df = df)*se
     data.frame(beta, lower, upper)
}

data(OrchardSprays)
model <- lm(decrease ~ rowpos + colpos * treatment, data = OrchardSprays)
ci_lm(model)


On the other hand, if you want to run regressions on each factor level 
separately, use the argument 'subset' of lm().

model2 <- lm(decrease ~ colpos , subset = treatment == 'A', data = 
OrchardSprays)
model2

I believe that you might be looking for this last one.

Rui Barradas
Em 16-10-2012 19:58, Sigrid escreveu:

Thread (9 messages)

Sigrid se's and CI's for fitted lines in multivariate regression analysis Oct 10 Bert Gunter se's and CI's for fitted lines in multivariate regression analysis Oct 10 Sigrid se's and CI's for fitted lines in multivariate regression analysis Oct 10 Rui Barradas se's and CI's for fitted lines in multivariate regression analysis Oct 10 Sigrid se's and CI's for fitted lines in multivariate regression analysis Oct 16 Rui Barradas se's and CI's for fitted lines in multivariate regression analysis Oct 16 Rui Barradas se's and CI's for fitted lines in multivariate regression analysis Oct 17 Sigrid se's and CI's for fitted lines in multivariate regression analysis Oct 20 David Winsemius se's and CI's for fitted lines in multivariate regression analysis Oct 20