[R] 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:
> 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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