Hello,
I am trying to generate a confidence interval (90 or 95%) of a regression
line. This is primarily just for illustration on a scatter plot (i.e. I am
trying to make this
http://www.ast.cam.ac.uk/~rgm/scratch/statsbook/graphics/anima4.gif).
I have been trying to use the predict.lm function, with interval set as
"confidence", but this still seems to be giving me a prediction interval (as
below). It is giving the interval based on the x value of a given point, as
opposed to the confidence interval of the regression line in general. Is
predict.lm the correct function to be using, or is there something else more
appropriate?
> predict.lm(test, interval = c("confidence"), level = 0.90)
fit lwr upr
1 4.170807 -0.2171226 8.558738
2 5.807453 2.1283100 9.486597
3 6.625776 3.2397994 10.011753
4 8.262422 5.2795295 11.245315
5 9.080745 6.1821741 11.979317
6 9.899068 6.9954692 12.802667
7 7.444099 4.2947040 10.593495
8 13.172360 9.4577068 16.887014
9 20.537267 13.0143098 28.060224
I have also tried using package 'ggplot' as follows:
>install.packages("ggplot")
>library(ggplot)
>qplot(wt, mpg, data=mtcars, type=c("point","smooth"), method=lm)
(following Hadley Wickham on this help list)
This generates a plot that looks roughly like what I want, but there is some
customizing of the plot that I would prefer to do in the standard 'plot'
function of the 'graphics' package. Also, ggplot does not give me a vector
of the values of the CI themselves, which would be useful to have.
Any help would be very appreciated.
Thanks,
Tim
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