11 Models
Task: Extract coefficients from a model.
library(magrittr)
lm(Sepal.Width ~ Species, data = iris) %>%
broom::tidy(.)
#> term estimate std.error statistic p.value
#> 1 (Intercept) 3.428 0.04803910 71.358540 5.707614e-116
#> 2 Speciesversicolor -0.658 0.06793755 -9.685366 1.832489e-17
#> 3 Speciesvirginica -0.454 0.06793755 -6.682608 4.538957e-10
# ____________________________________________________________________________Task: Extract residuals from a model.
lm(Sepal.Width ~ Species, data = iris) %>%
broom::augment(.) %>% head()
#> Sepal.Width Species .fitted .se.fit .resid .hat .sigma .cooksd
#> 1 3.5 setosa 3.428 0.0480391 0.072 0.02 0.3407959 0.0003118616
#> 2 3.0 setosa 3.428 0.0480391 -0.428 0.02 0.3389658 0.0110200713
#> 3 3.2 setosa 3.428 0.0480391 -0.228 0.02 0.3403157 0.0031272785
#> 4 3.1 setosa 3.428 0.0480391 -0.328 0.02 0.3397443 0.0064720901
#> 5 3.6 setosa 3.428 0.0480391 0.172 0.02 0.3405456 0.0017797285
#> 6 3.9 setosa 3.428 0.0480391 0.472 0.02 0.3385573 0.0134023471
#> .std.resid
#> 1 0.2141113
#> 2 -1.2727728
#> 3 -0.6780191
#> 4 -0.9753959
#> 5 0.5114881
#> 6 1.4036185
# ____________________________________________________________________________Task: Extract measures of fit from a model.
lm(Sepal.Width ~ Species, data = iris) %>%
broom::glance()
#> r.squared adj.r.squared sigma statistic p.value df logLik
#> 1 0.4007828 0.3926302 0.3396877 49.16004 4.492017e-17 3 -49.3663
#> AIC BIC deviance df.residual
#> 1 106.7326 118.7751 16.962 147
# ____________________________________________________________________________