Estimates regression coefficients and spatial autocorrelation parameters, given spatial coordinates and a model formula.
slmfit(
formula,
data,
xcoordcol,
ycoordcol,
areacol = NULL,
stratacol = NULL,
CorModel = "Exponential",
estmethod = "REML",
covestimates = c(NA, NA, NA)
)is an R linear model formula specifying the
response variable as well as covariates for predicting the response on the unsampled sites.
is a data frame or tibble with the response column, the covariates to
be used for the block kriging, and the spatial coordinates for all of the sites. Alternatively, data can be an sp Spatial Points Data Frame or sf object with POINT geometry.
is the name of the column in the data frame with x coordinates or longitudinal coordinates
is the name of the column in the data frame with y coordinates or latitudinal coordinates
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.
is the name of the the column with the stratification variable, if strata are to be fit separately, with different covariance parameter estimates.
is the covariance structure. By default, CorModel is
Exponential but other options include the Spherical and Gaussian.
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. This argument can also be set to
"None", in which case covestimates must be provided.
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.
a list of class slmfit with
the spatial covariance estimates
the regression coefficient estimates
the covariance matrix of the fixed effects
minus two times the log-likelihood of the model
the names of the predictors
the sample size
the name of the covariance model used
a vector of residuals
the design matrix
a vector of the sampled densities
a list containing
formula, the model formula
data, the data set input as the data argument
xcoordcol, the name of the x-coordinate column
ycoordcol, the name of the y-coordinate column
estmethod, either REML or ML
CorModel, the correlation model used
estimated covariance matrix of all sites
Inverted covariance matrix on the sampled sites
the vector of areas.
data(exampledataset) ## load a toy data set
slmobj <- slmfit(formula = counts ~ pred1 + pred2, data = exampledataset,
xcoordcol = 'xcoords', ycoordcol = 'ycoords', areacol = 'areavar')
summary(slmobj)
#>
#> Call:
#> counts ~ pred1 + pred2
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -16.086 -9.120 -4.596 4.963 28.594
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 26.104 4.465 5.847 <2e-16 ***
#> pred1 2.055 6.065 0.339 0.737
#> pred2 0.214 1.913 0.112 0.912
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Covariance Parameters:
#> Exponential Model
#> Nugget 2.323211e-04
#> Partial Sill 1.543624e+02
#> Range 8.240060e-01
#>
#> Generalized R-squared: 0.00413886
data(exampledataset) ## load a toy data set
exampledataset$strata <- c(rep("A", 19), rep("B", 21))
strataobj <- slmfit(formula = counts ~ pred1 + pred2, data = exampledataset, stratacol = "strata",
xcoordcol = 'xcoords', ycoordcol = 'ycoords', areacol = 'areavar')
summary(strataobj)
#> $A
#>
#> Call:
#> counts ~ pred1 + pred2
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -7.7826 -2.1499 0.0488 1.5492 9.8233
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 22.2882 35.0930 0.635 0.5364
#> pred1 -6.5176 3.6384 -1.791 0.0965 .
#> pred2 0.5545 1.7976 0.308 0.7626
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Covariance Parameters:
#> Exponential Model
#> Nugget 20.0915
#> Partial Sill 1227.2769
#> Range 8575.6681
#>
#> Generalized R-squared: 0.1998733
#>
#> $B
#>
#> Call:
#> counts ~ pred1 + pred2
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -9.262 -4.974 1.516 3.355 14.635
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 19.146 2.994 6.395 1e-05 ***
#> pred1 1.430 5.255 0.272 0.789
#> pred2 -1.603 1.274 -1.259 0.226
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Covariance Parameters:
#> Exponential Model
#> Nugget 4.188789e-05
#> Partial Sill 4.205452e+01
#> Range 4.954309e-01
#>
#> Generalized R-squared: 0.09916644
#>