Hello
Running R2.9.2 on Windows XP
I am puzzled by the performance of LME in situations where there are missing
data. As I understand it, one of the strengths of this sort of model is how
well it deals with missing data, yet lme requires nonmissing data.
Thus,
m1.mod1 <- lme(fixed = math_1 ~ I(year-2007.5)*TFC_,
data = long,
random = ~I(year-2007.5)|schoolnum)
causes an error in na.fail.default, but adding na.action = "na.omit" makes a
model with no errors. However, if I create that model, i.e.
m1.mod1 <- lme(fixed = math_1 ~ I(year-2007.5)*TFC_,
data = long,
random = ~I(year-2007.5)|schoolnum,
na.action = "na.omit")
then the diagnostic plots suggested in Pinheiro & Bates produce errors; e.g.
plot(m1.mod1, schoolnum~resid(.), abline = 0)
gives an error "could not find function "NaAct".
Searching the archives showed a similar question from 2007, but did not show
any responses.
Thanks for any help
Peter
)
Peter L. Flom, PhD
Statistical Consultant
Website: www DOT peterflomconsulting DOT com
Writing; http://www.associatedcontent.com/user/582880/peter_flom.html
Twitter: @peterflom
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