Uses an object of class `slmfit`

from the `slmfit()`

function to predict the response on the unsampled sites.
The column of the data set that has the response should have numeric values for the observed response
on the sampled sites and `NA` for any site that was not sampled.
Note that there is no `newdata`

argument to
`predict.slmfit()`

: any point in space for which a prediction
is needed should be included in the original data set in `slmfit()`

with the response variable as `NA`

.

```
# S3 method for slmfit
predict(object, wtscol = NULL, conf_level = 0.9, ...)
```

- object
is an object generated from

`slmfit()`

- wtscol
is the name of the column that contains the weights for prediction. The default setting predicts the population total

- conf_level
is the confidence level for a prediction interval, 0.90 by default

- ...
further arguments passed to or from other methods.

a list with

the estimated population total

the estimated prediction variance

a data frame containing

x-coordinates

y-coordinates

density predictions

count predictions

site-by-site density prediction variances

site-by-site count prediction variances

indicator variable for whether or not the each site was sampled

estimated mean for each site

area of each site

vector with estimated covariance parameters

the formula used to fit the model in

`slmfit()`

the confidence level

the confidence interval bounds

```
data(exampledataset) ## load a toy data set
slmobj <- slmfit(formula = counts ~ pred1 + pred2, data = exampledataset,
xcoordcol = 'xcoords', ycoordcol = 'ycoords', areacol = 'areavar')
predict(slmobj)
#> Prediction Info:
#> Prediction SE 90% LB 90% UB
#> counts 813.2 24.64 772.6 853.7
#> Numb. Sites Sampled Total Numb. Sites Total Observed Average Density
#> counts 35 40 679 26.77
```