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1 vote

models MIMIC, predicted probabilities?

This is a slightly weird thing to want to do, because you're interested in the predicted level of a categorical variable, given the level of a factor. But the level of the factor is determined by the ...
Jeremy Miles's user avatar
  • 17.4k
4 votes

non-significant p value in a multivariable cox regression following exhaustive model selection

There are lots of problems with "exhaustive model selection" and with stepwise. These have been covered here many times. I am not hugely familiar with glmulti (it appears to do some kind of ...
Peter Flom's user avatar
  • 116k
1 vote

What metric should I use for a Regression model with a gamma distributed target?

Use the (negative) likelihood of the distribution as your loss function. You can also turn it into a pseudo r2 for easier interpretability ( and negative likelihood is a 1:1 relation to pseudo r2). I ...
Georg M. Goerg's user avatar
2 votes

Can I predict values from a multiple regression model with fewer predictors than there are in the model?

This problem was studied here with several solutions offered. One of the solutions is based on fast backwards (approximate) step down variable selection to fit a temporary submodel using only the ...
Frank Harrell's user avatar
1 vote

R=X*Y is the relationship. Is predicting R and X and obtain Y same as predicting X and Y to obtain R?

The equation states equality, not causality The equals sign gives you equality, nothing more, nothing less. You have three numbers that are in some relation to one another that's defined by the ...
Scriddie's user avatar
  • 2,084
1 vote

Make Predictions with an RNN Using a Multi-dimensional Training Set

You should try to make $N_{train} \gg N_{test}$, since you stated that there are the same number of signals in train and test. The neural network will perform better if you train with more signals ...
Leif Peterson's user avatar
1 vote

Is there a way to predict multiple columns of NA values in a dataset using R?

Check out the missing data view on CRAN: here A popular R package for imputation (which is the name of this general area of research) is mice: here
R Carnell's user avatar
  • 5,093

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