Compare OLS, SLX, and other lm-like models side by side

Builds a tidy tibble of fit statistics (observations, coefficient count, R-squared, adjusted R-squared, residual standard error, AIC, BIC) for any combination of lm and slx objects. If a weights matrix is supplied, also reports Moran's I on each model's residuals - the standard diagnostic for spatial autocorrelation left unexplained by the fitted model.

Usage

slx_compare(..., W = NULL)

Arguments

...

Named lm or slx models. Names are used as the model labels in the output.

W

Optional weights matrix to use for Moran's I on residuals. Accepts an slx_W, a listw object from spdep, or NULL to skip the test. In panel mode, supply a list keyed by time value.

Value

A tibble with one row per model.

Examples

data(defense_burden)
W <- slx_weights(style = "custom", matrix = defense_burden$W_contig,
                 row_standardize = FALSE)
ols <- lm(ch_milex ~ milex_tm1 + civilwar_tm1,
          data = defense_burden$data)
slx_fit <- slx(ch_milex ~ milex_tm1 + civilwar_tm1,
               data = defense_burden$data, W = W,
               lag = "civilwar_tm1")
slx_compare(OLS = ols, SLX = slx_fit, W = W)
#> # A tibble: 2 × 11
#>   model class     n     k r.squared adj.r.squared sigma   AIC   BIC moran_I
#>   <chr> <chr> <int> <int>     <dbl>         <dbl> <dbl> <dbl> <dbl>   <dbl>
#> 1 OLS   lm      179     3     0.404         0.397  1.46  647.  660. -0.0444
#> 2 SLX   slx     179     4     0.404         0.394  1.46  649.  665. -0.0461
#> # ℹ 1 more variable: moran_p <dbl>