From: Uwe Ligges <ligges_at_statistik.uni-dortmund.de>

Date: Tue 27 Jun 2006 - 19:38:54 EST

*>
*

*>
*

> 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 ...
*

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jun 27 19:42:00 2006

Date: Tue 27 Jun 2006 - 19:38:54 EST

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 --

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

*> ______________________________________________
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*

> https://stat.ethz.ch/mailman/listinfo/r-help

> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jun 27 19:42:00 2006

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