lme4-lmer.R
In equatiomatic: Transform Models into 'LaTeX' Equations

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(equatiomatic)
library(lme4)

## ----unconditional1-----------------------------------------------------------
um_hsb <- lmer(math ~ 1 + (1 | sch.id), data = hsb)
extract_eq(um_hsb)

## ----unconditional2-----------------------------------------------------------
um_long3 <- lmer(score ~ 1 + (1 | sid) + (1 | school) + (1 | district),
  data = sim_longitudinal
)
extract_eq(um_long3)

## ----l1-preds-----------------------------------------------------------------
lev1_hsb <- lmer(math ~ female + ses + minority + (1 | sch.id), hsb)
extract_eq(lev1_hsb)

## ----mean_separate------------------------------------------------------------
extract_eq(lev1_hsb, mean_separate = FALSE)

## ----wrapping-----------------------------------------------------------------
extract_eq(lev1_hsb, wrap = TRUE, terms_per_line = 2)

## ----l1-preds2----------------------------------------------------------------
lev1_long <- lmer(score ~ wave + (1 | sid) + (1 | school) + (1 | district),
  data = sim_longitudinal
)
extract_eq(lev1_long)

## ----unstructured-vcv1--------------------------------------------------------
hsb1 <- lmer(
  math ~ female + ses + minority + (minority | sch.id),
  hsb
)
extract_eq(hsb1)

## ----unstructured-vcv2--------------------------------------------------------
hsb2 <- lmer(
  math ~ female + ses + minority + (female + ses | sch.id),
  hsb
)
extract_eq(hsb2)

## ----unstructured-vcv3--------------------------------------------------------
hsb3 <- lmer(
  math ~ female * ses + minority +
    (ses * female + minority | sch.id),
  hsb
)
extract_eq(hsb3)

## ----group-level-preds-prep---------------------------------------------------
# calculate district means
dist_mean <- tapply(
  sim_longitudinal$score,
  sim_longitudinal$district,
  mean
)

# put them in a df to merge
dist_mean <- data.frame(
  district = names(dist_mean),
  dist_mean = dist_mean
)

# create a new df with dist_mean added
d <- merge(sim_longitudinal, dist_mean, by = "district")

## ----group-preds-model1-------------------------------------------------------
group_preds_m1 <- lmer(score ~ wave + group + treatment + prop_low + dist_mean +
  (wave | sid) + (wave | school) + (wave | district),
data = d
)
extract_eq(group_preds_m1)

## ----l1-interaction-----------------------------------------------------------
l1_hsb_int <- lmer(math ~ minority * female + (1 | sch.id),
  data = hsb
)
extract_eq(l1_hsb_int)

## ----l2-interaction-----------------------------------------------------------
l2_hsb_int <- lmer(math ~ meanses * sector + (1 | sch.id),
  data = hsb
)
extract_eq(l2_hsb_int)

## ----cross-level1-------------------------------------------------------------
cl_long1 <- lmer(score ~ wave * treatment + (wave | sid) + (1 | school) +
  (1 | district),
data = sim_longitudinal
)
extract_eq(cl_long1)

## ----cross-level2-------------------------------------------------------------
cl_long2 <- lmer(score ~ wave * treatment + (1 | sid) + (1 | school) +
  (1 | district),
data = sim_longitudinal
)
extract_eq(cl_long2)

## ----cross-level3-------------------------------------------------------------
cl_long3 <- lmer(
  score ~ wave * group * treatment + wave * prop_low * treatment +
    (wave | sid) + (wave | school) +
    (wave + treatment | district),
  sim_longitudinal
)
extract_eq(cl_long3)

## ----vars-only----------------------------------------------------------------
hsb_varsonly <- lmer(math ~ minority * female + (minority * female || sch.id),
  data = hsb
)
extract_eq(hsb_varsonly)

## ----doublegroup--------------------------------------------------------------
hsb_doublegroup <- lmer(math ~ minority * female +
  (minority | sch.id) + (female | sch.id),
data = hsb
)
extract_eq(hsb_doublegroup)

## ----mix----------------------------------------------------------------------
long_mixed_ranef <- lmer(
  score ~ wave +
    (wave || sid) + (wave | school) + (1 | school) + (wave || district),
  sim_longitudinal
)
extract_eq(long_mixed_ranef)

## -----------------------------------------------------------------------------
extract_eq(group_preds_m1, use_coef = TRUE)