Aliases: augment.nlrq
Keywords:
### ** Examples # fit model n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2)) # summarize model fit with tidiers + visualization tidy(n)
# A tibble: 2 × 5 term estimate std.error statistic p.value <chr> <dbl> <dbl> <dbl> <dbl> 1 k 49.7 3.79 13.1 5.96e-14 2 e 0.746 0.0199 37.5 8.86e-27
augment(n)
# A tibble: 32 × 4 mpg wt .fitted .resid <dbl> <dbl> <dbl> <dbl> 1 21 2.62 23.0 -2.01 2 21 2.88 21.4 -0.352 3 22.8 2.32 25.1 -2.33 4 21.4 3.22 19.3 2.08 5 18.7 3.44 18.1 0.611 6 18.1 3.46 18.0 0.117 7 14.3 3.57 17.4 -3.11 8 24.4 3.19 19.5 4.93 9 22.8 3.15 19.7 3.10 10 19.2 3.44 18.1 1.11 # … with 22 more rows
glance(n)
# A tibble: 1 × 9 sigma isConv finTol logLik AIC BIC deviance df.residual nobs <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> 1 2.67 TRUE 0.00000204 -75.8 158. 162. 214. 30 32
library(ggplot2) ggplot(augment(n), aes(wt, mpg)) + geom_point() + geom_line(aes(y = .fitted))
newdata <- head(mtcars) newdata$wt <- newdata$wt + 1 augment(n, newdata = newdata)
# A tibble: 6 × 13 .rownames mpg cyl disp hp drat wt qsec vs am gear carb <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Mazda RX4 21 6 160 110 3.9 3.62 16.5 0 1 4 4 2 Mazda RX4 W… 21 6 160 110 3.9 3.88 17.0 0 1 4 4 3 Datsun 710 22.8 4 108 93 3.85 3.32 18.6 1 1 4 1 4 Hornet 4 Dr… 21.4 6 258 110 3.08 4.22 19.4 1 0 3 1 5 Hornet Spor… 18.7 8 360 175 3.15 4.44 17.0 0 0 3 2 6 Valiant 18.1 6 225 105 2.76 4.46 20.2 1 0 3 1 # … with 1 more variable: .fitted <dbl>