In conjunction with print.summary.slmfit(), the output looks similar
to output from R's standard lm() function.
# S3 method for slmfit
summary(object, ...)is an object generated from slmfit() of class slmfit.
further arguments passed to or from other methods.
a list with
model formula
a table of fixed effects estimates and associated standard errors
estimated spatial covariance parameter estimates
residuals
generalized r-squared.
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