Turns out that this is not a simple question. Depending on what you want to do, some statistical methods will just deal with missing data and use what is available, in different ways, e.g., cor(). For other purposes, you might want to "impute" (fill in) the missing values, and then there are many ways to do this, depending on what else you have (correlated variables?) and what assumptions you are willing to make. Two methods (among many) that I have found useful are in aregImpute() and transcan(), both in the Hmisc package.
To learn more, see my R search page: http://finzi.psych.upenn.edu/ and I also have an example of aregImpute() in http://www.psych.upenn.edu/~baron/rpsych/rpsych.html but see the help files first. I found the following article very helpful when I was a beginner with respect to this topic (which is still close to true): Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177. Jon On 04/24/05 10:15, Giordano Sanchez wrote: Hello, I have climatic data of various years with many missing values. I would like to know what tools in R are most suited to estimate this missing values. (New in R and quite new on statistics). -- Jonathan Baron, Professor of Psychology, University of Pennsylvania Home page: http://www.sas.upenn.edu/~baron ______________________________________________ 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
