knitr::opts_chunk$set(fig.width = 8, fig.height = 5)
Note: The type
argument in generate()
is automatically filled based on the entries for specify()
and
hypothesize()
. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.
library(infer) library(dplyr) mtcars <- mtcars %>% mutate(cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear), carb = factor(carb)) # For reproducibility set.seed(2018)
One numerical variable (mean)
mtcars %>% specify(response = mpg) %>% # formula alt: mpg ~ NULL hypothesize(null = "point", mu = 25) %>% generate(reps = 100, type = "bootstrap") %>% calculate(stat = "mean")
One numerical variable (median)
mtcars %>% specify(response = mpg) %>% # formula alt: mpg ~ NULL hypothesize(null = "point", med = 26) %>% generate(reps = 100, type = "bootstrap") %>% calculate(stat = "median")
One categorical (2 level) variable
mtcars %>% specify(response = am, success = "1") %>% # formula alt: am ~ NULL hypothesize(null = "point", p = .25) %>% generate(reps = 100, type = "simulate") %>% calculate(stat = "prop")
Two categorical (2 level) variables
mtcars %>% specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs hypothesize(null = "independence") %>% generate(reps = 100, type = "permute") %>% calculate(stat = "diff in props", order = c("0", "1"))
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", order = c("0", "1"))
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", order = c("0", "1"))
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")
One numerical variable (standard deviation)
Not currently implemented
mtcars %>% specify(response = mpg) %>% # formula alt: mpg ~ NULL hypothesize(null = "point", sigma = 5) %>% generate(reps = 100, type = "bootstrap") %>% calculate(stat = "sd")
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, success = "1") %>% generate(reps = 100, type = "bootstrap") %>% calculate(stat = "prop")
One numerical variable one categorical (2 levels) (diff in means)
mtcars %>% specify(mpg ~ am) %>% generate(reps = 100, type = "bootstrap") %>% calculate(stat = "diff in means", order = c("0", "1"))
Two categorical variables (diff in proportions)
mtcars %>% specify(am ~ vs, success = "1") %>% generate(reps = 100, type = "bootstrap") %>% calculate(stat = "diff in props", order = c("0", "1"))
Two numerical vars - SLR
mtcars %>% specify(mpg ~ hp) %>% generate(reps = 100, type = "bootstrap") %>% calculate(stat = "slope")
Two numerical vars - correlation
mtcars %>% specify(mpg ~ hp) %>% generate(reps = 100, type = "bootstrap") %>% calculate(stat = "correlation")
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