I've tried using cor() by the following sequence:
C <- cor(x, y = NULL, use = "all.obs", method = c("pearson"))
where x is my matrix of 21 columns and 3471 rows.
and I get this error:
Error in cor(x, y = NULL, use = "all.obs", method = c("pearson")) : missing observations in cov/cor
any suggestions?
Reading ?cor would have given you a solution:
Quoting from ?cor
If 'use' is '"all.obs"', then the presence of missing observations
will produce an error. If 'use' is '"complete.obs"' then missing
values are handled by casewise deletion. Finally, if 'use' has
the value '"pairwise.complete.obs"' then the correlation between
each pair of variables is computed using all complete pairs of
observations on those variables. This can result in covariance or
correlation matrices which are not positive semidefinite.You have missing values in your data and with your usage of cor and argument use = "all.obs" precludes this. Choose another value for use, noting the advice in the help pages for cor.
G
At 08:32 AM 2/7/2005 -0800, Spencer Graves wrote:
Have you considered "cor"? The command 'help.search("correlation")' suggests among other functions "var(stats)", the documentation for which also includes "cor".
If this is not adequate, "PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ". It may help you get more useful replies in the future. spencer graves
Jessica Higgs wrote:
Hi all:
I have a question on how to go about creating a correlation matrix. I have a huge amount of data....21 variables for 3471 times. I want to see how each of the variables correlate to each other. Any help would be appreciated, including which package and which functions I should use to do this.
Thanks, Jessica Higgs
Masters Student Department of Meteorology Penn State University
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