I tried multiple imputation with aregImpute() and
fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1.
The warning message below suggests that summary(f) of
fit.mult.impute() would only use the last imputed data set.
Thus, the whole imputation process is ignored.
Not using a Design
).
library(pwr) does not seem to support either.
Many thanks in advance,
Yuelin Li.
=
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I get an error when I call bugs(..., program=OpenBUGS,
bugs.directory=c:/Program Files/OpenBUGS/OpenBUGS312),
expecting, as suggested in help(bugs), that it would fit the
model with openbugs() via BRugs.
help(bugs)
... either winbugs/WinBUGS or openbugs/OpenBUGS, the latter
makes use of
I have a general question about how to interpret the plotcp() graph.
The cross-validation xerror value typically follows a decreasing pattern,
from approximately 1.0 at the root node, then it crosses the 1SE boundary,
reaches a plateau, and decreases further when the tree gets very complex
What happens if plotcp() shows an V-shape profile?
It seems that the predictors (I have a few) do not help the splits.
Would it be reasonable to prune it at size 6?
Or perhaps rpart() is not suitable for this analysis?
What I meant on the last sentence was perhaps I made a
results[i]- nls(Tw ~ mu + ((alpha - mu)/(1 + exp(gamma*(B -
Mean_Air,
data = dem16,
start = list(mu = 0.0001, alpha = 21.8, gamma = 0.22, B = 12.8))
}
If you have a variable that codes site then you can try something
like this to get the parameters over sites.
Many thanks to John Fox and Charles C. Berry. Both point to the paper
by Hoenig and Heisey (email from John Fox below):
Another paper critical of post-hoc power calculations is Hoenig and Heisey,
2001. The Abuse of Power: The Pervasive Fallacy of Power Calculations for
Data Analysis. The
Hope I am not too late joining this thread. I believe the difference
between R and SPSS is because SPSS adjusts the Type III SS by the
harmonic mean of the unbalanced cell sizes. This calculation is
discussed in Maxwell and Delaney (1990, pp. 271-297).
In short, the best explanation I can offer
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