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

## ----setup--------------------------------------------------------------------
library(dyadMLM)

## ----installation-cran, eval=FALSE--------------------------------------------
# install.packages("dyadMLM")

## ----installation, eval=FALSE-------------------------------------------------
# # install.packages("pak")
# pak::pak("Pascal-Kueng/dyadMLM")

## ----cross-sectional-raw, echo = FALSE----------------------------------------
head(dyads_cross)

## ----prepare-cross-distinguishable--------------------------------------------
cross_distinguishable_data <- dyadMLM::prepare_dyad_data(
  data = dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,

  # In this example, we optionally specify a predictor variable
  # and a model type to generate the columns needed for that model type.
  predictors = provided_support,
  model_types = "apim",
  # All three observed compositions in `dyads_cross` are detected and retained by
  # default. This example focuses on `female-male` dyads, so we restrict the
  # analysis here.
  keep_compositions = "female-male"
)

print(cross_distinguishable_data, n = 4)


## ----prepare-cross-exchangeable-basic-----------------------------------------
cross_exchangeable_data <- dyadMLM::prepare_dyad_data(
  data = dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  keep_compositions = "female-female",
  seed = 123
)

print(cross_exchangeable_data, n = 4)


## ----prepare-cross-set-exchangeable-------------------------------------------
cross_exchangeable_data <- dyadMLM::prepare_dyad_data(
  data = dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  keep_compositions = "female-male",
  set_exchangeable_compositions = "male-female",
  seed = 123
)

print(cross_exchangeable_data, n = 4)


## ----prepare-cross-dim--------------------------------------------------------
cross_dim_data <- dyadMLM::prepare_dyad_data(
  data = dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  predictors = provided_support,
  model_types = "dim",
  keep_compositions = "female-female",
  seed = 123
)

print(cross_dim_data, n = 4)

## ----prepare-cross-dsm--------------------------------------------------------
cross_dsm_data <- dyadMLM::prepare_dyad_data(
  data = dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  predictors = provided_support,
  model_types = "dsm",
  dsm_role_order = c("female", "male"),
  keep_compositions = "female-male"
)

print(cross_dsm_data, n = 4)

## ----ild-raw, echo=FALSE------------------------------------------------------
head(dyads_ild)

## ----prepare-ild-apim---------------------------------------------------------
ild_apim_data <- dyadMLM::prepare_dyad_data(
  dyads_ild,
  dyad = coupleID,
  member = personID,
  role = gender,
  time = diaryday,
  predictors = provided_support,
  model_types = "apim",
  keep_compositions = "female-male",
  seed = 123
)

print(ild_apim_data, n = 6)


## ----prepare-ild-apim-dynamic-------------------------------------------------
ild_apim_data_dynamic <- dyadMLM::prepare_dyad_data(
  dyads_ild,
  dyad = coupleID,
  member = personID,
  role = gender,
  time = diaryday,
  predictors = closeness,
  lag1_predictors = closeness,
  model_types = "apim",
  keep_compositions = "female-female",
  seed = 123
)

print(ild_apim_data_dynamic, n = 6)


## ----prepare-mixed-cross-sectional--------------------------------------------
mixed_cross_data <- dyadMLM::prepare_dyad_data(
  dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  seed = 123
)

print(mixed_cross_data, n = 4)

## ----prepare-mixed-cross-sectional-included-----------------------------------
mixed_cross_data_included <- dyadMLM::prepare_dyad_data(
  dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  keep_compositions = c("female-female", "male-male"),
  seed = 123
)

print(mixed_cross_data_included, n = 4)

## ----prepare-mixed-cross-sectional-exchangeable-------------------------------
mixed_cross_exchangeable_data <- dyadMLM::prepare_dyad_data(
  dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  set_exchangeable_compositions = c("male-female"),
  seed = 123
)

print(mixed_cross_exchangeable_data, n = 4)

## ----prepare-mixed-cross-sectional_pooled-------------------------------------
mixed_cross_data_pooled <- dyadMLM::prepare_dyad_data(
  dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  pool_compositions = list(
    "same-sex" = c("male-male", "female_female")
  ),
  seed = 123
)

print(mixed_cross_data_pooled)

## ----prepare-mixed-cross-sectional_pooled_constrained-------------------------
mixed_cross_data_pooled_constrained <- dyadMLM::prepare_dyad_data(
  dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  set_exchangeable_compositions = "male female",
  pool_compositions = list(
    "pooled_exchangeable" = c("male-male", "male_female")
  ),
  seed = 123
)

print(mixed_cross_data_pooled_constrained)