`marginaleffects` examples

@malcolmbarrett requested an issue instead of a PR, so here is a copy of examples from the G-computation chapter I posted here: https://github.com/r-causal/causal-inference-in-R/pull/239

# A linear model for wait_minutes_posted_avg
fit_wait_minutes <- lm(
  wait_minutes_posted_avg ~ 
    park_extra_magic_morning + park_ticket_season + park_close +
    park_temperature_high,
  data = seven_dwarfs_9
)

avg_predictions(
  fit_wait_minutes, 
  variables = "park_extra_magic_morning")

 park_extra_magic_morning Estimate Std. Error    z Pr(>|z|)     S 2.5 % 97.5 %
                        0     68.1      0.915 74.4   <0.001   Inf  66.3   69.9
                        1     74.2      2.052 36.2   <0.001 949.9  70.2   78.3

avg_predictions(
  fit_wait_minutes, 
  hypothesis = "b2 - b1 = 0",
  variables = "park_extra_magic_morning")

    Term Estimate Std. Error    z Pr(>|z|)   S 2.5 % 97.5 %
 b2-b1=0     6.16       2.26 2.73  0.00636 7.3  1.74   10.6

fit_wait_minutes_actual <- lm(
  wait_minutes_actual_avg ~ 
    ns(wait_minutes_posted_avg, df = 3) + 
    park_extra_magic_morning +
    park_ticket_season + park_close +
    park_temperature_high,
  data = wait_times
)

avg_predictions(fit_wait_minutes_actual, 
  variables = list(wait_minutes_posted_avg = c(60, 30))
)

 wait_minutes_posted_avg Estimate Std. Error     z Pr(>|z|)    S 2.5 % 97.5 %
                      60     29.8       7.05  4.23   <0.001 15.4  16.0   43.7
                      30     40.7       3.84 10.60   <0.001 84.8  33.2   48.2

avg_comparisons(fit_wait_minutes_actual, 
  variables = list(wait_minutes_posted_avg = c(60, 30))
)

                    Term            Contrast Estimate Std. Error     z Pr(>|z|)   S 2.5 % 97.5 %
 wait_minutes_posted_avg mean(60) - mean(30)    -10.8       8.13 -1.33    0.182 2.5 -26.8   5.09

r-causal / causal-inference-in-R

`marginaleffects` examples #240