[Rd] Infinite degrees of freedom for F-distribution
This is just a suggestion/wish that it would be nice for the F-distribution functions to recognize limiting cases for infinite degrees of freedom, as the t-distribution functions already do. The t-distribution functions recognize that df=Inf is equivalent to the standard normal distribution: pt(1,df=Inf) [1] 0.8413447 pnorm(1) [1] 0.8413447 On the other hand, pf() will accept Inf for df1, but returns the wrong result: pf(1,df1=Inf,df2=1) [1] 1 whereas the correct limiting value is pchisq(1,df=1,lower.tail=FALSE) [1] 0.3173105 pf() returns NaN when df2=Inf: pf(1,df1=1,df2=Inf) [1] NaN Warning message: NaNs produced in: pf(q, df1, df2, lower.tail, log.p) although the correct value is available as pchisq(1,df=1) [1] 0.6826895 Gordon version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major2 minor1.0 year 2005 month04 day 18 language R --- Dr Gordon K Smyth, Senior Research Scientist, Bioinformatics, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Vic 3050, Australia Tel: (03) 9345 2326, Fax (03) 9347 0852, Email: [EMAIL PROTECTED], www: http://www.statsci.org __ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Infinite degrees of freedom for F-distribution
Gordon Smyth [EMAIL PROTECTED] writes: This is just a suggestion/wish that it would be nice for the F-distribution functions to recognize limiting cases for infinite degrees of freedom, as the t-distribution functions already do. The t-distribution functions recognize that df=Inf is equivalent to the standard normal distribution: pt(1,df=Inf) [1] 0.8413447 pnorm(1) [1] 0.8413447 On the other hand, pf() will accept Inf for df1, but returns the wrong result: pf(1,df1=Inf,df2=1) [1] 1 whereas the correct limiting value is pchisq(1,df=1,lower.tail=FALSE) [1] 0.3173105 pf() returns NaN when df2=Inf: pf(1,df1=1,df2=Inf) [1] NaN Warning message: NaNs produced in: pf(q, df1, df2, lower.tail, log.p) although the correct value is available as pchisq(1,df=1) [1] 0.6826895 Gordon version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major2 minor1.0 year 2005 month04 day 18 language R This is actually a regression. It worked as you suggest in 2.0.1, at least on Linux. Also, somewhat disturbing, pf(1,df1=1,df2=Inf) [1] NaN Warning message: NaNs produced in: pf(q, df1, df2, lower.tail, log.p) pf(1,df1=1,df2=) [1] 0.6826895 pf(1,df1=1,df2=) [1] 0.6826841 pf(1,df1=1,df2=) [1] 0 (notice that the middle case has actually begun to diverge from the limiting value) -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 __ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Infinite degrees of freedom for F-distribution
On Fri, April 22, 2005 7:36 pm, Peter Dalgaard said: Gordon Smyth [EMAIL PROTECTED] writes: This is just a suggestion/wish that it would be nice for the F-distribution functions to recognize limiting cases for infinite degrees of freedom, as the t-distribution functions already do. The t-distribution functions recognize that df=Inf is equivalent to the standard normal distribution: pt(1,df=Inf) [1] 0.8413447 pnorm(1) [1] 0.8413447 On the other hand, pf() will accept Inf for df1, but returns the wrong result: pf(1,df1=Inf,df2=1) [1] 1 whereas the correct limiting value is pchisq(1,df=1,lower.tail=FALSE) [1] 0.3173105 pf() returns NaN when df2=Inf: pf(1,df1=1,df2=Inf) [1] NaN Warning message: NaNs produced in: pf(q, df1, df2, lower.tail, log.p) although the correct value is available as pchisq(1,df=1) [1] 0.6826895 Gordon version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major2 minor1.0 year 2005 month04 day 18 language R This is actually a regression. It worked as you suggest in 2.0.1, at least on Linux. Also, somewhat disturbing, pf(1,df1=1,df2=Inf) [1] NaN Warning message: NaNs produced in: pf(q, df1, df2, lower.tail, log.p) pf(1,df1=1,df2=) [1] 0.6826895 pf(1,df1=1,df2=) [1] 0.6826841 pf(1,df1=1,df2=) [1] 0 (notice that the middle case has actually begun to diverge from the limiting value) -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 You're right, it worked ok in R 2.0.1 in Windows as well. I guess the new behaviour is associated with the new algorithm for pbeta() in R 2.1.0, which will affect a number of functions besides pf(). The new algorithm is faster for large shape parameters but seems to have accuracy problems: a - 1; pbeta(0.5,a,a) [1] 0.5 a - 10; pbeta(0.5,a,a) [1] 0.499 a - 100; pbeta(0.5,a,a) [1] 0 pbeta(0.5,Inf,Inf) [1] 0 Gordon __ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
