Dear Peter, This is exactly what I needed. The "input" is coming from the Mouse Phenome Project database (http://aretha.jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home) which only gives pearson's correlations r, and n. Thank you very much. I have used this R-help resource twice now recently and it is incredibly helpful and fast. Thanks again. It is much appreciated.
cc Dirk Best, Justin At 03:43 AM 9/4/2005, Peter Dalgaard wrote: >Dirk Eddelbuettel <[EMAIL PROTECTED]> writes: > > > On 3 September 2005 at 17:59, Justin Rhodes wrote: > > | Dear R-help, > > | > > | Can someone please help me discover what function or code will give > > | me a p-value from the input: 1) R-square statistic from a simple > > | linear regression, and 2) sample size, n > > | > > | This would be greatly appreciated. I need this because I am using a > > | database that gives me R-square and sample size for multiple > > | comparisons, and I wish to determine the false discovery rate using > > | q-value. Thanks again, > > > > Do > > > example(lm) # just to get an lm object > > > str(summary(lm.D9)) # to examine summary of an object > > > > and you'll see that the object returned from summary has the two common R^2 > > measures, as well as things like residuals from which can compute n quite > > easily -- which you could obviously also from > your regressors and regressand. > > > > > length(summary(lm.D9)$residuals) > >I think the problem was somewhat different: The *input* is coming from >some sort of (closed-source or otherwise impenetrable) database which >only gives out n and R^2, right? > >Now R^2 = SSDmodel/(SSDmodel+SSDres) and F = >DFres/DFmodel*SSDmodel/SSDres, i.e. > > 1/R^2 = 1 + 1/F*DFmodel/DFres > >or > > F = 1/(1/R^2 - 1)*DFres/DFmodel = R^2/(1-R^2)*DFres/DFmodel > >which can be looked up "in the F-table" using > > pf(F, 1, N-2, lower.tail=FALSE) > >(provided we have a 1 DF model) > >Actually, R^2 itself has a beta distribution and you could use pbeta >directly, but then you'd need to figure out (or recall) what the >relation between the DF and the shape parameters of the beta >distribution are. By my reckoning, this should do it: > > pbeta(Rsq, 1/2, (N-2)/2, lower.tail=FALSE) > >"Proof": > >.... >Residual standard error: 1.143 on 8 degrees of freedom >Multiple R-Squared: 0.0004207, Adjusted R-squared: -0.1245 >F-statistic: 0.003367 on 1 and 8 DF, p-value: 0.9552 > > > pbeta(0.0004207, 1/2, 8/2, lower=F) >[1] 0.9551511 > > >-- > O__ ---- Peter Dalgaard Ă˜ster Farimagsgade 5, Entr.B > c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K > (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 >~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 Justin S. Rhodes Assistant Professor of Psychology Affiliate, Institute for Genomic Biology and Neuroscience Program University of Illinois at Urbana-Champaign 405 N Mathews Ave, Urbana, Il, 61801 Tel. 217-265-0021 Fax 217-244-5876 Website: http://s.psych.uiuc.edu/people/showprofile.php?id=545 ______________________________________________ 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