R help - Mar 2012 - Summary values from Glm function (rms package)

Dear fellow R-users,

I?m using the Glm function (gamma family of distributions) from the rms
package to compare 2 groups on costs data. Although the summary function
does provide the mean cost difference and  standard errors, I believe these
values were in the (natural) log ratio format. Is there a function to
express these values into the original scale of the response variable (i.e.,
dollars) such that I could obtain the mean adjusted cost difference (and the
95%CI)?

Many thanks to everyone in advance!

YH 


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Dear fellow R-users,

I?m using the Glm function (gamma family of distributions) from the rms
package to compare 2 groups on costs data. Although the summary function
does provide the mean cost difference and  standard errors, I believe these
values were in the (natural) log ratio format. Is there a function to
express these values into the original scale of the response variable (i.e.,
dollars) such that I could obtain the mean adjusted cost difference (and the
95%CI)?

Many thanks to everyone in advance!

YH 


--
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The smearingEst function in the Hmisc package, given a set of residuals, can
compute the smearing estimator of the mean.  To get the confidence interval
for the mean or difference in the mean will take some work (unless you have
no covariates to adjust for; in that case you can just bootstrap the
original data install of using the gamma model).  See the Hmisc function
areg.boot and associated functions for example code.

Frank

-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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