Dear All,
I am attempting to make predictions based on a survreg() model with some
censoring and a frailty term, as below: predict works fine on the original
data, but not if I specify newdata.
# a model with groups as fixed effect
model1 <- survreg(Surv(y,cens)~ x1 + x2 + groups,
dist = "gaussian")
# and with groups as a random effect
fr <- frailty(groups, dist = "gaussian", sparse = F)
model2 <- survreg(Surv(y,cens)~ x1 + x2 + fr,
dist = "gaussian")
# predict() works fine for groups as fixed effect
predict(model1, newdata = list(x1 = 1, x2 = 1, groups = groups[1]))
# [,1]
# 1 10.51833 # no problem!
# but not so well with frailty(groups)
predict(model2, newdata = list(x1 = 1, x2 = 1, fr = factor(1, levels = 1:10)))
#> Error in x %*% coef : non-conformable arguments
I have found references on the R and S-Plus lists which suggest that others
have had this problem (in both packages), but I can't find any solution or
further explanation. Can anyone suggest a way of getting the predictions I'm
after? Any help is greatly appreciated.
Kind regards Joe
Joe Crombie
Information and Risk Sciences
Bureau of Rural Science
Canberra Australia
p: +61 2 6272 5906
e: [EMAIL PROTECTED] <blocked::mailto:[EMAIL PROTECTED]>
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