Modelo linear generalizado (GLM)

Ajuste unificado de GLM para as familias mais comuns, com saida tidy.

Usage

rnp_glm(
  formula,
  data,
  familia = c("gaussian", "binomial", "poisson", "gamma", "inverse_gaussian"),
  ligacao = NULL,
  conf = 0.95,
  digits = 4L
)

Arguments

formula

Formula.

data

data.frame.

familia

String: "gaussian", "binomial", "poisson", "gamma", "inverse_gaussian".

ligacao

String opcional (funcao de ligacao). NULL usa o padrao canonico da familia.

conf

Nivel de confianca para os IC (Wald).

digits

Inteiro.

Value

Uma lista com coeficientes (tibble), modelo (AIC, deviance, dispersao, nobs) e objeto (glm).

See also

Other glm: rnp_binomial_negativa(), rnp_gam(), rnp_glm_diagnosticos(), rnp_grafico_efeitos(), rnp_modelo_misto(), rnp_regressao_ordinal()

Examples

rnp_glm(am ~ mpg + wt, mtcars, familia = "binomial")$coeficientes
#> # A tibble: 3 × 7
#>   termo       estimativa erro_padrao estatistica p_valor  ic_inf ic_sup
#>   <chr>            <dbl>       <dbl>       <dbl>   <dbl>   <dbl>  <dbl>
#> 1 (Intercept)     25.9        12.2          2.12  0.0338   1.99  49.8  
#> 2 mpg             -0.324       0.240       -1.35  0.176   -0.794  0.145
#> 3 wt              -6.42        2.55        -2.52  0.0118 -11.4   -1.42 
rnp_glm(carb ~ hp + wt, mtcars, familia = "poisson")$coeficientes
#> # A tibble: 3 × 7
#>   termo       estimativa erro_padrao estatistica p_valor  ic_inf ic_sup
#>   <chr>            <dbl>       <dbl>       <dbl>   <dbl>   <dbl>  <dbl>
#> 1 (Intercept)     0.139       0.399       0.348   0.728  -0.643  0.920 
#> 2 hp              0.0055      0.0016      3.34    0.0008  0.0023 0.0087
#> 3 wt              0.0045      0.131       0.0342  0.973  -0.252  0.261