Print fitted model objects and summaries.
# S3 method for class 'splm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for class 'spautor'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for class 'summary.splm'
print(
x,
digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'summary.spautor'
print(
x,
digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'anova.splm'
print(
x,
digits = max(getOption("digits") - 2L, 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'anova.spautor'
print(
x,
digits = max(getOption("digits") - 2L, 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'decorrelate'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for class 'spglm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for class 'spgautor'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for class 'summary.spglm'
print(
x,
digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'summary.spgautor'
print(
x,
digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'anova.spglm'
print(
x,
digits = max(getOption("digits") - 2L, 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'anova.spgautor'
print(
x,
digits = max(getOption("digits") - 2L, 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'splmRF'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for class 'summary.splmRF'
print(
x,
digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"),
...
)
# S3 method for class 'spautorRF'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for class 'summary.spautorRF'
print(
x,
digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"),
...
)A fitted model object from splm(), spautor(), spglm(), spgautor(), splmRF(), spautorRF()
or output from summary(x) or anova(x).
The number of significant digits to use when printing.
Other arguments passed to or from other methods.
Logical. If TRUE, significance stars are printed for each coefficient
Printed fitted model objects and summaries with formatting.
spmod <- splm(z ~ water + tarp,
data = caribou,
spcov_type = "exponential", xcoord = x, ycoord = y
)
print(spmod)
#>
#> Call:
#> splm(formula = z ~ water + tarp, data = caribou, spcov_type = "exponential",
#> xcoord = x, ycoord = y)
#>
#>
#> Coefficients (fixed):
#> (Intercept) waterY tarpnone tarpshade
#> 2.05021 -0.08336 0.08006 0.28663
#>
#>
#> Coefficients (exponential spatial covariance):
#> de ie range
#> 0.10672 0.02244 18.01186
#>
print(summary(spmod))
#>
#> Call:
#> splm(formula = z ~ water + tarp, data = caribou, spcov_type = "exponential",
#> xcoord = x, ycoord = y)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.41321 -0.20784 -0.11238 0.02915 0.45415
#>
#> Coefficients (fixed):
#> Estimate Std. Error df t value Pr(>|t|)
#> (Intercept) 2.05021 0.30373 0.01152 6.750 0.9459
#> waterY -0.08336 0.06443 22.39570 -1.294 0.2089
#> tarpnone 0.08006 0.07750 20.25968 1.033 0.3137
#> tarpshade 0.28663 0.07657 18.75092 3.743 0.0014 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Pseudo R-squared: 0.3972
#>
#> Coefficients (exponential spatial covariance):
#> de ie range
#> 0.10672 0.02244 18.01186
print(anova(spmod))
#> Analysis of Variance Table
#>
#> Response: z
#> NumDF DenDF F value Pr(>F)
#> (Intercept) 1 NA 45.5642 NA
#> water 1 22.396 1.6738 0.208929
#> tarp 2 19.525 7.7293 0.003369 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# \donttest{
sulfate$var <- rnorm(NROW(sulfate)) # add noise variable
sprfmod <- splmRF(sulfate ~ var, data = sulfate, spcov_type = "exponential")
print(sprfmod)
#> ranger:
#> Ranger result
#>
#> Call:
#> NA
#>
#> Type: Regression
#> Number of trees: 500
#> Sample size: 197
#> Number of independent variables: 1
#> Mtry: 1
#> Target node size: 5
#> Variable importance mode: none
#> Splitrule: variance
#> OOB prediction error (MSE): 137.0728
#> R squared (OOB): -0.491435
#>
#> splm on ranger residuals:
#>
#> Call:
#> NA
#>
#>
#> Coefficients (fixed):
#> (Intercept)
#> -5.348
#>
#>
#> Coefficients (exponential spatial covariance):
#> de ie range
#> 1.011e+02 4.353e+01 2.494e+06
#>
# }
# \donttest{
sprfmod <- spautorRF(log_trend ~ stock, data = seal, spcov_type = "car")
print(sprfmod)
#> ranger:
#> Ranger result
#>
#> Call:
#> NA
#>
#> Type: Regression
#> Number of trees: 500
#> Sample size: 94
#> Number of independent variables: 1
#> Mtry: 1
#> Target node size: 5
#> Variable importance mode: none
#> Splitrule: variance
#> OOB prediction error (MSE): 0.02559924
#> R squared (OOB): 0.04867566
#>
#> spautor on ranger residuals:
#>
#> Call:
#> NA
#>
#>
#> Coefficients (fixed):
#> (Intercept)
#> 0.009089
#>
#>
#> Coefficients (car spatial covariance):
#> de range extra
#> 0.05095 0.40158 0.02048
#>
# }