I fit a multivariate model with lmRob in the robust package and I like the fit. How can I use the fit to make a prediction at a given point? The hackish solution is to plot it and place horizontal and vertical lines on the plot to pinpoint
How can I feed the model a point, and have it spit back the prediction? I'm imagining it's something like:
predict(model, newdata = data.frame(x = 2, y = 90))
But this gives me the error:
predict(model, newdata = data.frame(x = 2, y = 90))
Error in `contrasts<-`(`*tmp*`, value = contrasts.arg[[nn]]) :
contrasts apply only to factors
The traceback() is:
> traceback()
7: stop("contrasts apply only to factors")
6: `contrasts<-`(`*tmp*`, value = contrasts.arg[[nn]])
5: model.matrix.default(delete.response(Terms), newdata, contrasts = object$contrasts,
xlevels = attr(object, "xlevels"))
4: model.matrix(delete.response(Terms), newdata, contrasts = object$contrasts,
xlevels = attr(object, "xlevels"))
3: predict.lmRob(model, newdata = data.frame(x = 1,
y = 90), interval = "predict")
2: predict(model, newdata = data.frame(x = 1, y = 90),
interval = "predict")
1: predict(model, newdata = data.frame(x = 1, y = 90),
interval = "predict")
If I just try passing the original data set into predict, I get:
Error in x %*% coefs : non-conformable arguments
Adding the appropriate factor levels fixes the first warning, but leaves the second.
You need to make sure the newdata has the same levels as the original, ie.
dat <- data.frame(x=1:10, y=factor(sample(letters[1:2], 10, rep=T)),
z=runif(10))
fit <- lmRob(z ~ ., data=dat)
## Fails, wrong factor
predict(fit, newdata=data.frame(x=11, y="a"))
## Works
predict(fit, newdata=data.frame(x=11, y=factor("a", levels=letters[1:2])))
You will get the second error if you do something like this
dat <- data.frame(x=1:10, y=factor(sample(letters[1:2], 10, rep=T), levels=letters[1:3]),
z=runif(10)) # data has empty "c" level
fit <- lmRob(z ~ ., data=dat)
## Fails
predict(fit, newdata=dat)
## Works
predict(fit, newdata=droplevels(dat))