See inline.
On Sat, Jan 12, 2019 at 9:56 AM Witold E Wolski wrote:
> ypred_oob <- predict(diachp.rf)
AFAIK these are, indeed, the out-of-bag predictions.
> dataX <- data %>% select(-quality) # remove response.
> ypred <- predict( diachp.rf, dataX )
These are not out of bag predictions. dataX
Off topic.
But see here:
https://stats.stackexchange.com/questions/61405/random-forest-and-prediction
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Sa
Dear David,
you are (as always) come to the rescue!
With your wonderful pedagogical talent of explanation!
Many thanks for the support!
My warmest and sincere wishes for the new year!
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
h
Hello,
I am just not sure what the predict.RandomForest function is doing...
I confused.
I would expect the predictions for these 2 function calls to predict the same:
```{r}
diachp.rf <- randomForest(quality~.,data=data,ntree=50, importance=TRUE)
ypred_oob <- predict(diachp.rf)
dataX <- data %>
On 1/12/19 1:18 AM, Medic wrote:
How to note (in code) a few (!) adjusting covariates for cox
regression. I had an example for one covariate, and tried (according
to my own understanding) two variantes of code (pls, see below), and
got ... a different p-value. What is the right code? Many than
Dear R-experts,
The reproducible example is below.
I am trying to use the visreg package for 2 ways-interactions and for 3
ways-interactions.
For 2 ways-interactions, everything goes fine except that the legend on the top
(2.HS Grad ; 3. Some College ; 4. College Grad) is not entire/complete, b
David, Richard,
Many thanks for your responses.
Le mardi 8 janvier 2019 à 04:25:19 UTC+1, Richard M. Heiberger
a écrit :
## Here is an example using the 3-way interaction plot from the HH package
install.packages("HH") ## if necessary
## The HH package supports the book
## Statistic
Hello,
You have to test for NA. Some (12) of the values of t1$Petal.Width are
NA therefore t1$Petal.Width == 2.0 alone returns 12 NA values.
t1[t1$Petal.Width == 2.0 & !is.na(t1$Petal.Width == 2.0), ]
Or use which(t1$Petal.Width == 2.0).
t1[which(t1$Petal.Width == 2.0), ]
Hope this helps,
Dear All,
After replacing some values in a data.frame, NAs rows have appeared
and cannot be removed. I have googled these issues and found that
several people have encountered it. Solutions in stackoverflow seem to
provide work-arounds but does not remove it from the data.frame.
Therefore, I am tu
How to note (in code) a few (!) adjusting covariates for cox
regression. I had an example for one covariate, and tried (according
to my own understanding) two variantes of code (pls, see below), and
got ... a different p-value. What is the right code? Many thanks!!!
(1)
survdiff (Surv(survt,statu
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