Extract fitted values from fitted model objects. fitted.values
is an alias.
# S3 method for splm
fitted(object, type = "response", ...)
# S3 method for splm
fitted.values(object, type = "response", ...)
# S3 method for spautor
fitted(object, type = "response", ...)
# S3 method for spautor
fitted.values(object, type = "response", ...)
# S3 method for spglm
fitted(object, type = "response", ...)
# S3 method for spglm
fitted.values(object, type = "response", ...)
# S3 method for spgautor
fitted(object, type = "response", ...)
# S3 method for spgautor
fitted.values(object, type = "response", ...)A fitted model object from splm(), spautor(), spglm(), or spgautor().
"response" for fitted values of the response, "spcov"
for fitted values of the spatial random errors, or "randcov" for
fitted values of the random effects. If from spglm() or spgautor(),
"link" for fitted values on the link scale. The default is "response".
Other arguments. Not used (needed for generic consistency).
The fitted values according to type.
When type is "response", the fitted values
for each observation are the standard fitted values \(X \hat{\beta}\).
When type is "spcov" the fitted values for each observation
are (generally) the best linear unbiased predictors of the spatial dependent and spatial
independent random error. When type is "randcov", the fitted
values for each level of each random effect are (generally) the best linear unbiased
predictors of the corresponding random effect. The fitted values for type
"spcov" and "randcov" can generally be used to check assumptions
for each component of the fitted model object (e.g., check a Gaussian assumption).
spmod <- splm(z ~ water + tarp,
data = caribou,
spcov_type = "exponential", xcoord = x, ycoord = y
)
fitted(spmod)
#> 1 2 3 4 5 6 7 8
#> 1.966709 2.253251 2.046762 2.049806 2.336347 2.046762 2.049806 1.966709
#> 9 10 11 12 13 14 15 16
#> 2.253251 2.046762 1.966709 2.253251 2.046762 2.336347 2.129858 2.049806
#> 17 18 19 20 21 22 23 24
#> 2.336347 2.129858 2.253251 2.046762 2.129858 1.966709 2.049806 2.336347
#> 25 26 27 28 29 30
#> 2.129858 2.049806 2.336347 2.129858 1.966709 2.253251
fitted.values(spmod)
#> 1 2 3 4 5 6 7 8
#> 1.966709 2.253251 2.046762 2.049806 2.336347 2.046762 2.049806 1.966709
#> 9 10 11 12 13 14 15 16
#> 2.253251 2.046762 1.966709 2.253251 2.046762 2.336347 2.129858 2.049806
#> 17 18 19 20 21 22 23 24
#> 2.336347 2.129858 2.253251 2.046762 2.129858 1.966709 2.049806 2.336347
#> 25 26 27 28 29 30
#> 2.129858 2.049806 2.336347 2.129858 1.966709 2.253251
fitted(spmod, type = "spcov")
#> $de
#> 1 2 3 4 5 6
#> 0.19009135 0.08808946 -0.05288712 -0.06331824 -0.02856777 0.09098076
#> 7 8 9 10 11 12
#> 0.01072506 -0.09354048 -0.12314818 -0.05923981 -0.03596157 -0.07841306
#> 13 14 15 16 17 18
#> -0.13175388 -0.11441342 -0.11280667 -0.12561669 -0.08822084 -0.17365706
#> 19 20 21 22 23 24
#> -0.17132052 -0.14840042 -0.08447864 -0.10445284 -0.17663020 -0.19065413
#> 25 26 27 28 29 30
#> -0.13413900 -0.08936860 -0.15019468 -0.15571767 -0.17050442 -0.15814674
#>
#> $ie
#> 1 2 3 4 5 6
#> 0.264329030 0.101709816 -0.183964708 -0.020497847 0.069254386 0.086299996
#> 7 8 9 10 11 12
#> 0.040488767 -0.069202761 -0.167184456 0.111532939 -0.044769687 -0.044859570
#> 13 14 15 16 17 18
#> -0.054034211 0.144136768 -0.023062960 -0.287330302 0.198971087 -0.137268598
#> 19 20 21 22 23 24
#> -0.036948221 -0.057389311 0.069654486 0.133809193 -0.072211253 -0.096740681
#> 25 26 27 28 29 30
#> 0.133346093 0.062593275 -0.136219497 0.054886276 -0.007208414 -0.032119633
#>