% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/declare_measurement.R,
%   R/declare_potential_outcomes.R
\name{declare_measurement}
\alias{declare_measurement}
\alias{measurement_handler}
\alias{potential_outcomes_handler}
\title{Declare measurement procedure}
\usage{
declare_measurement(..., handler = measurement_handler, label = NULL)

measurement_handler(data, ...)

potential_outcomes_handler(data, ...)
}
\arguments{
\item{...}{arguments to be captured, and later passed to the handler}

\item{handler}{a tidy-in, tidy-out function}

\item{label}{a string describing the step}

\item{data}{A data.frame.}
}
\value{
A function that returns a data.frame.
}
\description{
Add a step to a design that explicitly indicates what variables are measured by a design.
It is always possible to include measured variables directly in your declare_model calls or using declare_step.
However, putting latent variables in declare_model and variables-as-measured in declare_measurement helps communicate which parts of your research design are in the model and which parts are in the data strategy.
}
\examples{

# declare_measurement a in use a
## Two-arm randomized experiment
design <-
  declare_model(
    N = 500,
    X = rep(c(0, 1), each = N / 2),
    U = rnorm(N, sd = 0.25),
    potential_outcomes(Y ~ 0.2 * Z + X + U)
  ) +
  declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
  declare_sampling(S = complete_rs(N = N, n = 200)) +
  declare_assignment(Z = complete_ra(N = N, m = 100)) +
  declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
  declare_estimator(Y ~ Z, inquiry = "ATE")
  
run_design(design)

# Reveal potential outcomes according to treatment assignment
design <-
  declare_model(N = 100,
                potential_outcomes(Y ~ rbinom(
                  N, size = 1, prob = 0.1 * Z + 0.5
                ))) +
  declare_assignment(Z = complete_ra(N, m = 50)) +
  declare_measurement(Y = reveal_outcomes(Y ~ Z))
  
head(draw_data(design))

# Generate observed measurement from a latent value
design <- 
  declare_model(N = 100, latent = runif(N)) +
  declare_measurement(observed = rbinom(N, 1, prob = latent))
  
head(draw_data(design))

if(require("psych")) {

  # Index creation
  
  library(psych)
  
  design <-
    declare_model(
      N = 500,
      X = rep(c(0, 1), each = N / 2),
      Y_1 = 0.2 * X + rnorm(N, sd = 0.25),
      Y_2 = 0.3 * X + 0.5 * rnorm(N, sd = 0.50),
      Y_3 = 0.1 * X + 0.4 * rnorm(N, sd = 0.75)) +
    declare_measurement(
      index = fa(
        r = cbind(Y_1, Y_2, Y_3),
        nfactors = 1,
        rotate = "varimax"
      )$scores
    )
  
  draw_data(design)
}
}
