Retorna diagnosticos clássicos: hat, residuos studentizados, Cook's D, DFBETAS, DFFITS, leverage, e testes de normalidade e homocedasticidade.
Value
lista:
pontos: tibble com indice, residuo, studentizado, hat, cooks_d, dffits.testes: tibble com Shapiro-Wilk residuos, Breusch-Pagan (homocedasticidade), Durbin-Watson (autocorrelacao).
Examples
fit <- lm(mpg ~ wt + hp, mtcars)
rnp_regressao_diagnosticos(fit)
#>
#> ── Diagnosticos de regressao ───────────────────────────────────────────────────
#>
#> ── Pontos
#> # A tibble: 32 × 12
#> indice residuo studentizado hat cooks_d dffits dfbeta_1 dfbeta_2 dfbeta_3
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 -2.57 -1.02 0.0443 0.0159 -0.218 -0.161 0.0639 0.033
#> 2 2 -1.58 -0.617 0.0405 0.0055 -0.127 -0.0693 -0.0004 0.0458
#> 3 3 -2.48 -0.984 0.0602 0.0207 -0.249 -0.211 0.0972 0.0434
#> 4 4 0.135 0.0524 0.0475 0 0.0117 0.0027 0.0045 -0.0068
#> 5 5 0.373 0.144 0.0369 0.0003 0.0282 0.0018 -0.0016 0.0092
#> 6 6 -2.37 -0.946 0.0672 0.0216 -0.254 -0.006 -0.152 0.180
#> 7 7 -1.30 -0.526 0.117 0.0126 -0.192 0.0047 0.0781 -0.16
#> 8 8 1.51 0.614 0.116 0.0168 0.222 0.0343 0.122 -0.190
#> 9 9 0.806 0.316 0.06 0.0022 0.0798 0.0198 0.0333 -0.0551
#> 10 10 -0.780 -0.303 0.0469 0.0016 -0.0672 -0.0032 -0.0337 0.0367
#> # ℹ 22 more rows
#> # ℹ 3 more variables: leverage_alta <lgl>, cooks_alto <lgl>, outlier_t <lgl>
#>
#> ── Testes
#> # A tibble: 3 × 4
#> teste estatistica p_valor interpretacao
#> <chr> <dbl> <dbl> <chr>
#> 1 shapiro-wilk (residuos) 0.928 0.0343 Rejeita normalidade
#> 2 breusch-pagan 10.2 0.006 Heterocedasticidade
#> 3 durbin-watson 1.36 NA Possivel autocorrelacao positiva