maryannet / infer

An R package for tidyverse-friendly statistical inference

• Hypothesis tests
• Confidence intervals

Infer: an R package for tidyverse-friendly statistical inference.

---

The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidyverse design framework.

Hypothesis tests

Examples

These examples assume that mtcars has been overwritten so that the variables cyl, vs, am, gear, and carb are factors.

mtcars <- as.data.frame(mtcars) %>%
  mutate(cyl = factor(cyl),
         vs = factor(vs),
         am = factor(am),
         gear = factor(gear),
         carb = factor(carb))

---

One numerical variable (mean)

mtcars %>%
  specify(response = mpg) %>% # alt: mpg ~ NULL (or mpg ~ 1)
  hypothesize(null = "point", mu = 25) %>% 
  generate(reps = 100, type = "bootstrap") %>% 
  calculate(stat = "mean")

One numerical variable (median)

mtcars %>%
  specify(response = mpg) %>% # alt: mpg ~ NULL (or mpg ~ 1)
  hypothesize(null = "point", med = 26) %>% 
  generate(reps = 100, type = "bootstrap") %>% 
  calculate(stat = "median")

One numerical variable (standard deviation)

mtcars %>%
  specify(response = mpg) %>% # alt: mpg ~ NULL (or mpg ~ 1)
  hypothesize(null = "point", sigma = 5) %>% 
  generate(reps = 100, type = "bootstrap") %>% 
  calculate(stat = "sd")

One categorical (2 level) variable

mtcars %>%
  specify(response = am) %>% # alt: am ~ NULL (or am ~ 1)
  hypothesize(null = "point", p = c("1" = .25)) %>% 
  generate(reps = 100, type = "simulate") %>% 
  calculate(stat = "prop")

Two categorical (2 level) variables

mtcars %>%
  specify(am ~ vs) %>% # alt: response = am, explanatory = vs
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute") %>%
  calculate(stat = "diff in props")

One categorical (>2 level) - GoF

mtcars %>%
  specify(cyl ~ NULL) %>% # alt: response = cyl
  hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
  generate(reps = 100, type = "simulate") %>%
  calculate(stat = "Chisq")

Two categorical (>2 level) variables

mtcars %>%
  specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute") %>%
  calculate(stat = "Chisq")

One numerical variable one categorical (2 levels) (diff in means)

mtcars %>%
  specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute") %>%
  calculate(stat = "diff in means")

One numerical variable one categorical (2 levels) (diff in medians)

mtcars %>%
  specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute") %>%
  calculate(stat = "diff in medians")

One numerical one categorical (>2 levels) - ANOVA

mtcars %>%
  specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute") %>%
  calculate(stat = "F")

Two numerical vars - SLR

mtcars %>%
  specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute") %>%
  calculate(stat = "slope")

Confidence intervals

One numerical (one mean)

mtcars %>%
  specify(response = mpg) %>%
  generate(reps = 100, type = "bootstrap") %>%
  calculate(stat = "mean")

One numerical (one median)

mtcars %>%
  specify(response = mpg) %>%
  generate(reps = 100, type = "bootstrap") %>%
  calculate(stat = "median")

One numerical (standard deviation)

mtcars %>%
  specify(response = mpg) %>%
  generate(reps = 100, type = "bootstrap") %>%
  calculate(stat = "sd")

One categorical (one proportion)

mtcars %>%
  specify(response = am) %>%
  generate(reps = 100, type = "bootstrap") %>%
  calculate(stat = "prop", success = "1")

One numerical variable one categorical (2 levels) (diff in means)

mtcars %>%
  specify(mpg ~ am) %>%
  generate(reps = 100, type = "bootstrap") %>%
  calculate(stat = "diff in means")

Two categorical variables (diff in proportions)

mtcars %>%
  specify(am ~ vs) %>%
  generate(reps = 100, type = "bootstrap") %>%
  calculate(stat = "diff in props")

Two numerical vars - SLR

mtcars %>%
  specify(mpg ~ hp) %>% 
  generate(reps = 100, type = "bootstrap") %>%
  calculate(stat = "slope")