probit ordinal logistic regression with `MASS::polr`: How to make prediction on new data

Question

I want to do ordinal regression in R, so I want to use the `polr` function from the `MASS` package. First I create a model like this: ``` model <- polr(labels ~ var1 + var2, Hess = TRUE) ``` Now I want to use the model to predict new cases. I thought that would simply be: ``` pred <- predict(model, data = c(newVar1, newVar2)) ``` However it seems that predict is somehow predicting on the training set, not the new data. When my training set is 2000 examples, and my new data is 700 examples. I still get 2000 predicted labels. So my question is: how do I use `polr` to make predictions on new data?

Accepted Answer

Sadly there is no documentation entry for `predict.polr`, otherwise you can simply read that for how to use `predict` correctly.

In R, only for few primitive model fitting functions like `smooth.spline`, `predict` expect a vector for newdata (this is reasonable as `smooth.spline` handles univariate regression). Generally, `predict` expects a data frame or list, whose names match variables specified in model formula or as shown in model frame (the "terms" attributes). If you fit a model:

```
labels ~ var1 + var2
```

then you should construct `newdata`:

```
predict(model, newdata = data.frame(var1 = newVar1, var2 = newVar2))
```

or

```
predict(model, newdata = list(var1 = newVar1, var2 = newVar2))
```

Note, it is `newdata`, not `data` for `predict`.

Since there is no documentation, it may be good if we look at:

```
args(MASS:::predict.polr)
#function (object, newdata, type = c("class", "probs"), ...) 
```

and you can even check source code (not long):

```
MASS:::predict.polr
```

You will see in source code:

```
newdata <- as.data.frame(newdata)
m <- model.frame(Terms, newdata, na.action = function(x) x, 
       xlev = object$xlevels)
```

This explains why `newdata` should be passed as a data frame, and why variable names must match what is in `Terms`.

Here is a reproducible example:

```
library(MASS)
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)

## check model terms inside model frame
attr(terms(house.plr$model), "term.labels")
# [1] "Infl" "Type" "Cont"
```

When making prediction, these will not work:

```
## `data` ignored as no such argument
predict(house.plr, data = data.frame("Low", "Tower", "Low"))
## no_match in names 
predict(house.plr, newdata = data.frame("Low", "Tower", "Low"))
```

This works:

```
predict(house.plr, newdata = data.frame(Infl = "Low", Type = "Tower", Cont = "Low"))

#[1] Low
#Levels: Low Medium High
```

probit ordinal logistic regression with `MASS::polr`: How to make prediction on new data

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