# NOT RUN {
# Simple model
mod1 <- lm(mpg ~ cyl + disp, mtcars)
extract_eq(mod1)
# Include all variables
mod2 <- lm(mpg ~ ., mtcars)
extract_eq(mod2)
# Works for categorical variables too, putting levels as subscripts
mod3 <- lm(body_mass_g ~ bill_length_mm + species, penguins)
extract_eq(mod3)
set.seed(8675309)
d <- data.frame(
cat1 = rep(letters[1:3], 100),
cat2 = rep(LETTERS[1:3], each = 100),
cont1 = rnorm(300, 100, 1),
cont2 = rnorm(300, 50, 5),
out = rnorm(300, 10, 0.5)
)
mod4 <- lm(out ~ ., d)
extract_eq(mod4)
# Don't italicize terms
extract_eq(mod1, ital_vars = FALSE)
# Wrap equations in an "aligned" environment
extract_eq(mod2, wrap = TRUE)
# Wider equation wrapping
extract_eq(mod2, wrap = TRUE, terms_per_line = 4)
# Include model estimates instead of Greek letters
extract_eq(mod2, wrap = TRUE, terms_per_line = 2, use_coefs = TRUE)
# Don't fix doubled-up "+ -" signs
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, use_coefs = TRUE, fix_signs = FALSE)
# Use indices for factors instead of subscripts
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, index_factors = TRUE)
# Use other model types, like glm
set.seed(8675309)
d <- data.frame(
out = sample(0:1, 100, replace = TRUE),
cat1 = rep(letters[1:3], 100),
cat2 = rep(LETTERS[1:3], each = 100),
cont1 = rnorm(300, 100, 1),
cont2 = rnorm(300, 50, 5)
)
mod5 <- glm(out ~ ., data = d, family = binomial(link = "logit"))
extract_eq(mod5, wrap = TRUE)
# }
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