Debiased score test of the null fit.0 against the alternative
fit.1. Both are internally refit as Gaussian-family GLMs and the
call is dispatched to compare_models.glm.
Usage
# S3 method for class 'lm'
compare_models(fit.0, fit.1, ...)Arguments
- fit.0
The null model as a fitted
lmobject.- fit.1
The alternative model as a fitted
lmobject. Must be a supermodel offit.0; seecompare_models.glm.- ...
Additional arguments passed to
compare_models.glm.
Examples
set.seed(42)
n <- 500
dat <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
dat$x3 <- dat$x3 + (dat$x1 + dat$x2) / 3
dat$y <- 1 + dat$x1 + 2 * dat$x3 + rnorm(n)
fit.0 <- lm(y ~ x1 + x3, data = dat)
fit.1 <- lm(y ~ x1 + x2 + x3, data = dat)
# test fit.0 against fit.1: should not be rejected
compare_models(fit.0, fit.1)
#> Debiased score test:
#> y ~ X, with X consists of (Intercept), x1, x2, x3.
#> (hunt.style = optimal, hunt.method = glm)
#> n = 500, two-way split: hunt = 250, debias & test = 250
#>
#> T = -0.3495, p-value = 0.636634
anova(fit.0, fit.1)
#> Analysis of Variance Table
#>
#> Model 1: y ~ x1 + x3
#> Model 2: y ~ x1 + x2 + x3
#> Res.Df RSS Df Sum of Sq F Pr(>F)
#> 1 497 502.73
#> 2 496 502.72 1 0.0097927 0.0097 0.9217
# misspecified model: should be rejected
fit.00 <- lm(y ~ x1 + x2, data = dat)
compare_models(fit.00, fit.1)
#> Debiased score test:
#> y ~ X, with X consists of (Intercept), x1, x2, x3.
#> (hunt.style = optimal, hunt.method = glm)
#> n = 500, two-way split: hunt = 250, debias & test = 250
#>
#> T = 10.5216, p-value = 3.43451e-26
plot(compare_models(fit.00, fit.1))
compare_models(fit.00, fit.1, hunt.style="wls")
#> Debiased score test:
#> y ~ X, with X consists of (Intercept), x1, x2, x3.
#> (hunt.style = wls, hunt.method = glm)
#> n = 500, two-way split: hunt = 250, debias & test = 250
#>
#> T = 10.4095, p-value = 1.12168e-25
anova(fit.00, fit.1)
#> Analysis of Variance Table
#>
#> Model 1: y ~ x1 + x2
#> Model 2: y ~ x1 + x2 + x3
#> Res.Df RSS Df Sum of Sq F Pr(>F)
#> 1 497 2295.63
#> 2 496 502.72 1 1792.9 1768.9 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1