hi, Actually this applies not just to random numbers, but... Suppose I type: x <- rnorm(30, 4, 1.5) to get some 30 random normal numbers with mean 4 and standard deviation 1.5. Is there anyway to "re-trace" x? i.e. am I able to find out what the mean and standard deviation for x is? More generally, suppose I generated some random numbers using a particular distribution. Can I find out the parameters of the distribution on these random numbers? Cheers, Kevin ------------------------------------------------------------------------------ Ko-Kang Kevin Wang Postgraduate PGDipSci Student Department of Statistics University of Auckland New Zealand Homepage: http://www.stat.auckland.ac.nz/~kwan022 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
On Sat, 17 Aug 2002, Ko-Kang Kevin Wang wrote:> Actually this applies not just to random numbers, but... > > Suppose I type: > x <- rnorm(30, 4, 1.5) > to get some 30 random normal numbers with mean 4 and standard deviation > 1.5.Not quite. You get numbers with a different mean and standard deviation: (4, 1.5) apply to the population.> Is there anyway to "re-trace" x? i.e. am I able to find out what the mean > and standard deviation for x is?You can only estimate the population values, the usual estimates being the sample mean and std dev.> More generally, suppose I generated some random numbers using a particular > distribution. Can I find out the parameters of the distribution on these > random numbers?By fitting e.g. by maximum likelihood. fitdistr(MASS) is a fairly general function to do it. -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272860 (secr) Oxford OX1 3TG, UK Fax: +44 1865 272595 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
At 15:48 17/08/2002 +1200, you wrote:>hi, > >Actually this applies not just to random numbers, but... > >Suppose I type: > x <- rnorm(30, 4, 1.5) >to get some 30 random normal numbers with mean 4 and standard deviation >1.5. > >Is there anyway to "re-trace" x? i.e. am I able to find out what the mean >and standard deviation for x is? > >More generally, suppose I generated some random numbers using a particular >distribution. Can I find out the parameters of the distribution on these >random numbers?Well use fitdistr () require (mass) x <- rnorm(30, 4, 1.5) fitdistr(x,"normal",list(mean=0,sd=1)) result: mean sd 3.7126893 1.2748764 (0.2327595) (0.1646173) Warning message: NaNs produced in: dnorm(x, mean, sd, log) Thanks in advance Bernardo Rangel Tura, MD, MSc National Institute of Cardiology Laranjeiras Rio de Janeiro Brazil -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._