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

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:

Okay, I've now tried to the predict function and get the SE, although 
it seem
to calculate SE for each observation from the line (I assume), while 
I want
the CI-interval and SE for each line fitted line for the treatment. I 
do not
really understand what  parameter mean these SEs are calculated from 
when
there would be several means along the line...?. This is what I get with
predict:

predict(model, se.fit = TRUE, interval = "confidence")

Another way I can think of to show how well the lines fit the data is to
look at the intercepts and slopes instead. I can specify the line for 
each
level and would then get the estimate of slope and intercept, 
although I do
not know how I show the standard errors of the slope and intercept.
lm(decrease[treatment=="A"]~colpos[treatment=="A"])

Call:
lm(formula = decrease[treatment == "A"] ~ colpos[treatment == "A"])

Coefficients:
              (Intercept)  colpos[treatment == "A"]
                   2.5357                    0.4643

Please let me know if you know how to find st. errors for (or st. 
error for
slope and intercept) of lines for each factor of a treatment.

Thank you
~S




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