predict.graf: Predictions for 'graf' objects.
In GRaF: Species distribution modelling using latent Gaussian random fields

Predictions and associated credible intervals for graf objects, either for the data used for fitting or for a different dataset.

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## S3 method for class 'graf'
predict(object, newdata = NULL, type = c("response", "latent"), CI = 0.95,
			maxn = NULL, ...)

`object`	A `graf` object.
`newdata`	An optional dataframe giving covariates to predict to. If `NULL` a prediction for the covariates used to fit the model is returned.
`type`	The level of the prediction. `"response"` (default) gives predictions on the probability scale and `"latent"` gives predictions on the scale of the latent Gaussian.
`CI`	The level at which to calculate predictive credible intervals. The default value returns upper and lower 95% credible intervals. If credible intervals are not required this can be set to `NULL`. If `type = 'latent'` setting `CI = 'std'` returns the mean and standard deviation of the latent field.
`maxn`	The maximum number of records to predict to in each batch. To avoid computationally expensive operations on large matrices, `predict.graf` splits the dataset for prediction into batches with maximum number of records `maxn`. This can be adjusted by the user to optimise computational efficiency on different machines. If `maxn = NULL` maxn is set at approximately one tenth the number rows used to fit the model.
`...`	Additional arguments for future versions.