auto_anova {autostats}

auto anova

Description

A wrapper around lm and anova to run a regression of a continuous variable against categorical variables. Used for determining the whether the mean of a continuous variable is statistically significant amongst different levels of a categorical variable.

Usage

auto_anova(
  data,
  ...,
  baseline = c("mean", "median", "first_level", "user_supplied"),
  user_supplied_baseline = NULL,
  sparse = FALSE,
  pval_thresh = 0.1
)

Arguments

data

a data frame

...

tidyselect specification or cols

baseline

choose from "mean", "median", "first_level", "user_supplied". what is the baseline to compare each category to? can use the mean and median of the target variable as a global baseline

user_supplied_baseline

if intercept is "user_supplied", can enter a numeric value

sparse

default FALSE; if true returns a truncated output with only significant results

pval_thresh

control significance level for sparse output filtering

Details

Columns can be inputted as unquoted names or tidyselect. Continuous and categorical variables are automatically determined. If no character or factor column is present, the column with the lowest amount of unique values will be considered the categorical variable.

Description of columns in the output

target

continuous variables

predictor

categorical variables

level

levels in the categorical variables

estimate

difference between level target mean and baseline

target_mean

target mean per level

n

rows in predictor level

std.error

standard error of target in predictor level

level_p.value

p.value for t.test of whether target mean differs significantly between level and baseline

level_significance

level p.value represented by stars

predictor_p.value

p.value for significance of entire predictor given by F test

predictor_significance

predictor p.value represented by stars

conclusion

text interpretation of tests

Value

data frame

Examples

iris %>%
auto_anova(tidyselect::everything()) -> iris_anova1


iris_anova1 %>%
print(width = Inf)