You probably could have saved some time by installing Hmisc and using
rcorr:
---something like:
require(Hmisc)
typecors <- tapply(c.df[ , c("age_obs", "Ht_cm", "BD_mm", "CDA_cm",
"CDB_cm") ], c.df$type, rcorr )
The upper or lower triangle of those results could have populated most
of your table and you could cbind the combined result of:
combcors <- rcorr(c.df[ , c("age_obs", "Ht_cm", "BD_mm",
"CDA_cm", "CDB_cm") ])
... and add the age_obs Vs est_age row. Tables are actually matrices
and use the same sort of indexing. Cbind and rbind augment columnwise
and rowwise.
--
David
On Oct 15, 2009, at 10:07 PM, ms.com wrote:
dear all
I have a data set with three types (Tree, Sapling, Seedling). I have
estimated the correlation values. now i need to bring all the
correlation values in a table like the one i have shown in attached
file with R codes.could you please give me idea on this problem
thanking you
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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