Takes a fitted `fosr`

-object produced by `bayes_fosr`

and produces predictions given a
new set of values for the model covariates or the original values used for the model fit.

```
# S3 method for fosr
predict(object, newdata, ...)
```

object

a fitted `fosr`

object as produced by `bayes_fosr`

newdata

a named list containing the values of the model covariates at which predictions are required. If this is not provided then predictions corresponding to the original data are returned. All variables provided to newdata should be in the format supplied to the model fitting function.

...

additional (unused) arguments

...

# NOT RUN { library(reshape2) library(dplyr) library(ggplot2) ##### Cross-sectional real-data example ##### ## organize data data(DTI) DTI = subset(DTI, select = c(cca, case, pasat)) DTI = DTI[complete.cases(DTI),] DTI$gender = factor(sample(c("male","female"), dim(DTI)[1], replace = TRUE)) DTI$status = factor(sample(c("RRMS", "SPMS", "PPMS"), dim(DTI)[1], replace = TRUE)) ## fit models VB = bayes_fosr(cca ~ pasat, data = DTI, Kp = 4, Kt = 10) ## obtain predictions pred = predict(VB, sample_n(DTI, 10)) # } # NOT RUN { # }