Estimates regression coefficients and spatial autocorrelation
parameters, given spatial coordinates, a model formula, and a
stratification variable. Arguments are the same here as they are
for slmfit(), with an extra argument for
stratacol, the name of the stratification column. Note that
stratum can either by incorporated as a covariate in
slmfit(), in which case the errors have the same
spatial covariance, or, models with differing spatial covariances
for the errors can be fit to each level of stratum, as is done here
in stratafit().
stratafit(
formula,
data,
xcoordcol,
ycoordcol,
stratacol = NULL,
areacol = NULL,
CorModel = "Exponential",
estmethod = "REML"
)is an R linear model formula specifying the
response variable as well as covariates for predicting the response on the unsampled sites.
is the data set with the response column, the covariates to be used for the block kriging, and the spatial coordinates for all of the sites.
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 stratification column
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 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.
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
exampledataset$strata <- c(rep("A", 19), rep("B", 21))
strataobj <- stratafit(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
#>