Ben Bolker wrote: > Thierry Girard <thierry.girard <at> unibas.ch> writes: > >> I do have summary data (mean, standard deviation and sample size n) >> and want to analyze this data. >> The summary data is supposed to be from a normal distribution. >> >> I need the following calculations on this summary data (no, I do not >> have the original data): >> >> - one sample t-test against a known mu >> - two sample t-test >> - analysis of variance between 4 groups. >> >> I would appreciate any help available. >> >> One possible solution could be to simulate the data using rnorm with >> the appropriate n, mu and sd, but I don't know if there would be a >> more accurate solution. > > > this is the kind of situation where you need to go back to the basics -- > knowing what computations these statistical tests are _actually > doing_ -- which you should be able to find in any basic stats book, > or by digging > into the guts of the R functions. The only other thing you need to > know is the R functions for cumulative distribution functions, pt > (for the t distribution) and pf (for the F dist.) > > For example: > > stats:::t.test.default > > has lots of complicated stuff inside but the key lines are > (for a one sample test) > > nx <- length(x) > df <- nx - 1 > stderr <- sqrt(vx/nx) > # if you already have the standard deviation then you want > # sqrt(sd^2/nx) > tstat <- (mx - mu)/stderr ## mu is the known mean you're testing against > pval <- 2 * pt(-abs(tstat), df) > > (assuming 2-tailed) > > you will find similar stuff for the two-sample t-test, > depending on your particular choices. > > The 1-way ANOVA might be harder to dig out of the R code; > there you're better off going back and (re)learning from > a basic stats treatment how to > compute the between-group and (pooled) within-group variances. > > Bottom line is that, except for knowing about pt and pf, > this is really a basic statistics question rather than an > R question. > > good luck > Ben Bolker > > PS: it is too bad, but the increasing sophistication of R is > making it harder for beginners to explore the guts --- e.g. > knowing to look for "stats:::t.test.default" in order to find > the code ...
Thanks for the hint, I already had in mind writing an R Help Desk about "Finding the code" meaning both, R source code as described above as well as C code corresponding to the .Primitive, .C, .Call and friends' entry points. Maybe for the next R News issue, if nobody is willing to contribute to the Help Desk column (hint, hint!!!). Uwe Ligges > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
