declare_measurement | R Documentation |

This function adds measured data columns that can be functions of unmeasured data columns.

declare_measurement(..., handler = measurement_handler, label = NULL) measurement_handler(data, ...)

`...` |
arguments to be captured, and later passed to the handler |

`handler` |
a tidy-in, tidy-out function |

`label` |
a string describing the step |

`data` |
A data.frame. |

It is also possible to include measured variables in your declare_model call or to add variables 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 M and which parts are in D.

A function that returns a data.frame.

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

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