R/anova.R, R/anova_glm.R
anova.spmodel.RdCompute analysis of variance tables for a fitted model object or a likelihood ratio test for two fitted model objects.
# S3 method for splm
anova(object, ..., test = TRUE, Terms, L)
# S3 method for spautor
anova(object, ..., test = TRUE, Terms, L)
# S3 method for spglm
anova(object, ..., test = TRUE, Terms, L)
# S3 method for spgautor
anova(object, ..., test = TRUE, Terms, L)
# S3 method for anova.splm
tidy(x, ...)
# S3 method for anova.spautor
tidy(x, ...)
# S3 method for anova.spglm
tidy(x, ...)
# S3 method for anova.spgautor
tidy(x, ...)A fitted model object from splm(), spautor(), spglm(), or spgautor().
An additional fitted model object.
A logical value indicating whether p-values from asymptotic Chi-squared
hypothesis tests should be returned. Defaults to TRUE.
An optional character or integer vector that specifies terms in the model
used to jointly compute test statistics and p-values (if test = TRUE)
against a null hypothesis of zero. Terms is only used when a single fitted model
object is passed to the function. If Terms is a character vector, it
should contain the names of the fixed effect terms. If Terms is an integer
vector, it should correspond to the order (starting at one) of the names
of the fixed effect terms. The easiest way to obtain the names of
all possible terms is to run tidy(anova(object))$effects (the
integer representation matches the positions of this vector).
An optional numeric matrix or list specifying linear combinations
of the coefficients in the model used to compute test statistics
and p-values (if test = TRUE) for coefficient constraints corresponding to a null
hypothesis of zero. L is only used when a single fitted model
object is passed to the function. If L is a numeric matrix, its rows
indicate coefficient constraints and its columns
represent coefficients. Then a single hypothesis test is conducted
against a null hypothesis of zero.
If L is a list, each list element is a numeric matrix specified as above.
Then separate hypothesis tests are conducted. The easiest
way to obtain all possible coefficients is to run tidy(object)$term.
An object from anova(object).
When one fitted model object is present, anova()
returns a data frame with degrees of
freedom (Df), test statistics (Chi2), and p-values
(Pr(>Chi2) if test = TRUE) corresponding
to asymptotic Chi-squared hypothesis tests for each model term.
When two fitted model objects are present, anova() returns a data frame
with the difference in degrees of freedom between the full and reduced model (Df), a test
statistic (Chi2), and a p-value corresponding to the likelihood ratio test
(Pr(>Chi2) if test = TRUE).
Whether one or two fitted model objects are provided,
tidy() can be used
to obtain tidy tibbles of the anova(object) output.
When one fitted model object is present, anova()
performs a general linear hypothesis test corresponding to some hypothesis
specified by a matrix of constraints. If Terms and L are not specified,
each model term is tested against zero (which correspond to type III or marginal
hypothesis tests from classical ANOVA). If Terms is specified and L
is not specified, all terms are tested jointly against zero. When L is
specified, the linear combinations of terms specified by L are jointly
tested against zero.
When two fitted model objects are present, one must be a "reduced"
model nested in a "full" model. Then anova() performs a likelihood ratio test.
# one-model anova
spmod <- splm(z ~ water + tarp,
data = caribou,
spcov_type = "exponential", xcoord = x, ycoord = y
)
anova(spmod)
#> Analysis of Variance Table
#>
#> Response: z
#> Df Chi2 Pr(>Chi2)
#> (Intercept) 1 43.4600 4.327e-11 ***
#> water 1 1.6603 0.1975631
#> tarp 2 15.4071 0.0004512 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tidy(anova(spmod))
#> # A tibble: 3 × 4
#> effects df statistic p.value
#> <chr> <int> <dbl> <dbl>
#> 1 (Intercept) 1 43.5 4.33e-11
#> 2 water 1 1.66 1.98e- 1
#> 3 tarp 2 15.4 4.51e- 4
# see terms
tidy(anova(spmod))$effects
#> [1] "(Intercept)" "water" "tarp"
tidy(anova(spmod, Terms = c("water", "tarp")))
#> # A tibble: 1 × 4
#> effects df statistic p.value
#> <chr> <int> <dbl> <dbl>
#> 1 water, tarp 3 17.1 0.000685
# same as
tidy(anova(spmod, Terms = c(2, 3)))
#> # A tibble: 1 × 4
#> effects df statistic p.value
#> <chr> <int> <dbl> <dbl>
#> 1 water, tarp 3 17.1 0.000685
# likelihood ratio test
lmod <- splm(z ~ water + tarp,
data = caribou,
spcov_type = "none"
)
tidy(anova(spmod, lmod))
#> # A tibble: 1 × 5
#> full reduced df statistic p.value
#> <chr> <chr> <int> <dbl> <dbl>
#> 1 spmod lmod 2 4.17 0.124