I am trying to do a power analysis to get the number of replicas per treatment. If I try to get the power it works just fine: setn=c(2,3) sdx=c(1.19,4.35) power.t.test(n = setn, delta = 13.5, sd = sdx, sig.level = 0.05,power NULL) If I go the other way to obtain the "n" I have problems. sdx=c(1.19,4.35) pow=c(.8,.8) power.t.test(n = NULL, delta = 13.5, sd = sdx, sig.level = 0.05, power 0.8) Is there any way to do this? Thank you. -- View this message in context: http://r.789695.n4.nabble.com/Power-Analysis-tp3458786p3458786.html Sent from the R help mailing list archive at Nabble.com.
First, note that you are doing two separate power calculations, one with n=2 and sd = 1.19, the other with n=3 and sd = 4.35. I will assume this was on purpose. Now...> power.t.test(n = 2, delta = 13.5, sd = 1.19, sig.level = 0.05)Two-sample t test power calculation n = 2 delta = 13.5 sd = 1.19 sig.level = 0.05 power = 0.9982097 alternative = two.sided Now, with n=2, the power is already .99. With n=1, there are zero df. So, what n corresponds to a power of .8?> power.t.test(n = 1.6305, delta = 13.5, sd = 1.19, sig.level = 0.05)Two-sample t test power calculation n = 1.6305 delta = 13.5 sd = 1.19 sig.level = 0.05 power = 0.8003734 alternative = two.sided It looks like 1.63 subjects will do the job :-) Finally, look at the power.t.test function, there is a line that explains your error message: else if (is.null(n)) n <- uniroot(function(n) eval(p.body) - power, c(2, 1e+07))$root power.t.test() is making the sensible assumption that we only care about sample sizes of at least n = 2.... albyn On Mon, Apr 18, 2011 at 02:31:19PM -0700, Schatzi wrote:> I am trying to do a power analysis to get the number of replicas per > treatment. > > If I try to get the power it works just fine: > setn=c(2,3) > sdx=c(1.19,4.35) > power.t.test(n = setn, delta = 13.5, sd = sdx, sig.level = 0.05,power > NULL) > > If I go the other way to obtain the "n" I have problems. > sdx=c(1.19,4.35) > pow=c(.8,.8) > power.t.test(n = NULL, delta = 13.5, sd = sdx, sig.level = 0.05, power > 0.8) > > Is there any way to do this? Thank you. > > -- > View this message in context: http://r.789695.n4.nabble.com/Power-Analysis-tp3458786p3458786.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >-- Albyn Jones Reed College jones at reed.edu
It seems to me, with deltas this large (relative to the SD), that a significance test is a moot point! David Cross d.cross at tcu.edu www.davidcross.us On Apr 18, 2011, at 5:14 PM, Albyn Jones wrote:> First, note that you are doing two separate power calculations, > one with n=2 and sd = 1.19, the other with n=3 and sd = 4.35. > I will assume this was on purpose. Now... > >> power.t.test(n = 2, delta = 13.5, sd = 1.19, sig.level = 0.05) > > Two-sample t test power calculation > > n = 2 > delta = 13.5 > sd = 1.19 > sig.level = 0.05 > power = 0.9982097 > alternative = two.sided > > Now, with n=2, the power is already .99. With n=1, there are zero df. > So, what n corresponds to a power of .8? > >> power.t.test(n = 1.6305, delta = 13.5, sd = 1.19, sig.level = 0.05) > > Two-sample t test power calculation > > n = 1.6305 > delta = 13.5 > sd = 1.19 > sig.level = 0.05 > power = 0.8003734 > alternative = two.sided > > It looks like 1.63 subjects will do the job :-) > > Finally, look at the power.t.test function, there is a line that explains > your error message: > > else if (is.null(n)) > n <- uniroot(function(n) eval(p.body) - power, c(2, 1e+07))$root > > power.t.test() is making the sensible assumption that we only care about > sample sizes of at least n = 2.... > > albyn > > On Mon, Apr 18, 2011 at 02:31:19PM -0700, Schatzi wrote: >> I am trying to do a power analysis to get the number of replicas per >> treatment. >> >> If I try to get the power it works just fine: >> setn=c(2,3) >> sdx=c(1.19,4.35) >> power.t.test(n = setn, delta = 13.5, sd = sdx, sig.level = 0.05,power >> NULL) >> >> If I go the other way to obtain the "n" I have problems. >> sdx=c(1.19,4.35) >> pow=c(.8,.8) >> power.t.test(n = NULL, delta = 13.5, sd = sdx, sig.level = 0.05, power >> 0.8) >> >> Is there any way to do this? Thank you. >> >> -- >> View this message in context: http://r.789695.n4.nabble.com/Power-Analysis-tp3458786p3458786.html >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> R-help at r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > -- > Albyn Jones > Reed College > jones at reed.edu > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
Yes, Richard Savage used to call this "inter ocular data"; the answer should leap up and strike you right between the eyes... albyn On Mon, Apr 18, 2011 at 05:23:05PM -0500, David Cross wrote:> It seems to me, with deltas this large (relative to the SD), that a significance test is a moot point! > > David Cross > d.cross at tcu.edu > www.davidcross.us > > > > > On Apr 18, 2011, at 5:14 PM, Albyn Jones wrote: > > > First, note that you are doing two separate power calculations, > > one with n=2 and sd = 1.19, the other with n=3 and sd = 4.35. > > I will assume this was on purpose. Now... > > > >> power.t.test(n = 2, delta = 13.5, sd = 1.19, sig.level = 0.05) > > > > Two-sample t test power calculation > > > > n = 2 > > delta = 13.5 > > sd = 1.19 > > sig.level = 0.05 > > power = 0.9982097 > > alternative = two.sided > > > > Now, with n=2, the power is already .99. With n=1, there are zero df. > > So, what n corresponds to a power of .8? > > > >> power.t.test(n = 1.6305, delta = 13.5, sd = 1.19, sig.level = 0.05) > > > > Two-sample t test power calculation > > > > n = 1.6305 > > delta = 13.5 > > sd = 1.19 > > sig.level = 0.05 > > power = 0.8003734 > > alternative = two.sided > > > > It looks like 1.63 subjects will do the job :-) > > > > Finally, look at the power.t.test function, there is a line that explains > > your error message: > > > > else if (is.null(n)) > > n <- uniroot(function(n) eval(p.body) - power, c(2, 1e+07))$root > > > > power.t.test() is making the sensible assumption that we only care about > > sample sizes of at least n = 2.... > > > > albyn > > > > On Mon, Apr 18, 2011 at 02:31:19PM -0700, Schatzi wrote: > >> I am trying to do a power analysis to get the number of replicas per > >> treatment. > >> > >> If I try to get the power it works just fine: > >> setn=c(2,3) > >> sdx=c(1.19,4.35) > >> power.t.test(n = setn, delta = 13.5, sd = sdx, sig.level = 0.05,power > >> NULL) > >> > >> If I go the other way to obtain the "n" I have problems. > >> sdx=c(1.19,4.35) > >> pow=c(.8,.8) > >> power.t.test(n = NULL, delta = 13.5, sd = sdx, sig.level = 0.05, power > >> 0.8) > >> > >> Is there any way to do this? Thank you. > >> > >> -- > >> View this message in context: http://r.789695.n4.nabble.com/Power-Analysis-tp3458786p3458786.html > >> Sent from the R help mailing list archive at Nabble.com. > >> > >> ______________________________________________ > >> R-help at r-project.org mailing list > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > >> > > > > -- > > Albyn Jones > > Reed College > > jones at reed.edu > > > > ______________________________________________ > > R-help at r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > >-- Albyn Jones Reed College jones at reed.edu
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