slmfit: Fits a Spatial Linear Model
In highamm/FPBK-with-Detection: Finite Population Block Kriging with Detection

Description Usage Arguments Value Examples

Estimates regression coefficients and spatial autocorrelation parameters.

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slmfit(formula, data, xcoordcol, ycoordcol, CorModel = "Exponential",
  estmethod = "REML", covestimates = c(NA, NA, NA),
  detectionobj = NULL, coordtype = "TM", areacol = NULL)

`formula`	is an R linear model formula specifying density as the response variable as well as covariates for predicting densities on the unsampled sites.
`data`	is the data set with the response column of densities, the covariates to be used for the block kriging, and the spatial coordinates for all of the sites.
`xcoordcol`	is the name of the column in the data frame with x coordinates or longitudinal coordinates
`ycoordcol`	is the name of the column in the data frame with y coordinates or latitudinal coordinates
`CorModel`	is the covariance structure. By default, `CorModel` is Exponential but other options include the Spherical and Gaussian.
`estmethod`	is either the default `"REML"` for restricted maximum likelihood to estimate the covariance parameters and regression coefficients or `"ML"` to estimate the covariance parameters and regression coefficients.
`covestimates`	is an optional vector of covariance parameter estimates (nugget, partial sill, range). If these are given and `estmethod = "None"`, the the provided vector are treated as the estimators to create the covariance structure.
`detectionobj`	is a fitted model obj from `get_detection`. The default is for this object to be `NULL`, resulting in spatial prediction that assumes perfect detection.
`coordtype`	specifies whether the coordinates are in Lat/Lon or already in TM or UTM
`areacol`	is the name of the column with the areas of the sites. By default, we assume that all sites have equal area, in which case a vector of 1's is used as the areas.

a list with

the spatial covariance estimates
the regression coefficient estimates
the estimated covariance matrix
minus two times the log-likelihood
the names of the predictors used as covariates
the sample size
the name of the correlation model used
a vector of residuals
the design matrix for the sampled sites
a vector of the density of the observed counts
an indicator for whether the user input detection data
a list with information for predict containing
1. formula
2. data, the data set without any missing values
3. a vector of x-coordinates (in TM)
4. a vector of y-coordinates (in TM)
5. whether REML or ML was used
6. the correlation model used
7. the covariance matrix for all of the sites
8. Inverted covariance matrix on the sampled sites
9. Other items used in predict