fabricatr for R

Drawing discrete data based on probabilities or latent traits is a common task that can be cumbersome. Each function in our discrete drawing set creates a different type of discrete data: draw_binary creates binary 0/1 data, draw_binomial creates binomial data (repeated trial binary data), draw_categorical creates categorical data, draw_ordered transforms latent data into observed ordered categories, draw_count creates count data (poisson-distributed). draw_likert is an alias to draw_ordered that pre-specifies break labels and offers default breaks appropriate for a likert survey question.

draw_binomial(prob = link(latent), trials = 1, N = length(prob),
  latent = NULL, link = "identity", quantile_y = NULL)

draw_categorical(prob = link(latent), N = NULL, latent = NULL,
  link = "identity", category_labels = NULL)

draw_ordered(x = link(latent), breaks = c(-1, 0, 1),
  break_labels = NULL, N = length(x), latent = NULL,
  strict = FALSE, link = "identity")

draw_count(mean = link(latent), N = length(mean), latent = NULL,
  link = "identity", quantile_y = NULL)

draw_binary(prob = link(latent), N = length(prob), link = "identity",
  latent = NULL, quantile_y = NULL)

draw_likert(x, type = 7, breaks = NULL, N = length(x),
  latent = NULL, link = "identity", strict = !is.null(breaks))

draw_quantile(type, N)

Arguments

prob	A number or vector of numbers representing the probability for binary or binomial outcomes; or a number, vector, or matrix of numbers representing probabilities for categorical outcomes. If you supply a link function, these underlying probabilities will be transformed.
trials	for `draw_binomial`, the number of trials for each observation
N	number of units to draw. Defaults to the length of the vector of probabilities or latent data you provided.
latent	If the user provides a link argument other than identity, they should provide the variable `latent` rather than `prob` or `mean`
link	link function between the latent variable and the probability of a positive outcome, e.g. "logit", "probit", or "identity". For the "identity" link, the latent variable must be a probability.
quantile_y	A vector of quantiles; if provided, rather than drawing stochastically from the distribution of interest, data will be drawn at exactly those quantiles.
category_labels	vector of labels for the categories produced by `draw_categorical`. If provided, must be equal to the number of categories provided in the `prob` argument.
x	for `draw_ordered` or `draw_likert`, the latent data for each observation.
breaks	vector of breaks to cut a latent outcome into ordered categories with `draw_ordered` or `draw_likert`
break_labels	vector of labels for the breaks to cut a latent outcome into ordered categories with `draw_ordered`. (Optional)
strict	Logical indicating whether values outside the provided breaks should be coded as NA. Defaults to `FALSE`, in which case effectively additional breaks are added between -Inf and the lowest break and between the highest break and Inf.
mean	for `draw_count`, the mean number of count units for each observation
type	Type of Likert scale data for `draw_likert`. Valid options are 4, 5, and 7. Type corresponds to the number of categories in the Likert scale.

Value

A vector of data in accordance with the specification; generally numeric but for some functions, including draw_ordered, may be factor if break labels are provided.

Details

For variables with intra-cluster correlations, see draw_binary_icc and draw_normal_icc

Examples


# Drawing binary values (success or failure, treatment assignment)
fabricate(N = 3,
   p = c(0, .5, 1),
   binary = draw_binary(prob = p))
#>   ID   p binary
#> 1  1 0.0      0
#> 2  2 0.5      0
#> 3  3 1.0      1

# Drawing binary values with probit link (transforming continuous data
# into a probability range).
fabricate(N = 3,
   x = 10 * rnorm(N),
   binary = draw_binary(latent = x, link = "probit"))
#>   ID          x binary
#> 1  1 12.1515166      1
#> 2  2  0.4221889      0
#> 3  3  3.9740704      1

# Repeated trials: `draw_binomial`
fabricate(N = 3,
   p = c(0, .5, 1),
   binomial = draw_binomial(prob = p, trials = 10))
#>   ID   p binomial
#> 1  1 0.0        0
#> 2  2 0.5        6
#> 3  3 1.0       10

# Ordered data: transforming latent data into observed, ordinal data.
# useful for survey responses.
fabricate(N = 3,
   x = 5 * rnorm(N),
   ordered = draw_ordered(x = x,
                          breaks = c(-Inf, -1, 1, Inf)))
#>   ID          x ordered
#> 1  1 -0.7245082       2
#> 2  2 -1.6222348       1
#> 3  3 -0.8628245       2

# Providing break labels for latent data.
fabricate(N = 3,
   x = 5 * rnorm(N),
   ordered = draw_ordered(x = x,
                          breaks = c(-Inf, -1, 1, Inf),
                          break_labels = c("Not at all concerned",
                                           "Somewhat concerned",
                                           "Very concerned")))
#>   ID          x              ordered
#> 1  1 -6.1803146 Not at all concerned
#> 2  2 -9.5115210 Not at all concerned
#> 3  3 -0.4725201   Somewhat concerned

# Likert data: often used for survey data
fabricate(N = 10,
          support_free_college = draw_likert(x = rnorm(N),
                                             type = 5))
#>    ID support_free_college
#> 1  01 Don't Know / Neutral
#> 2  02 Don't Know / Neutral
#> 3  03                Agree
#> 4  04 Don't Know / Neutral
#> 5  05                Agree
#> 6  06 Don't Know / Neutral
#> 7  07             Disagree
#> 8  08             Disagree
#> 9  09             Disagree
#> 10 10 Don't Know / Neutral

# Count data: useful for rates of occurrences over time.
fabricate(N = 5,
   x = c(0, 5, 25, 50, 100),
   theft_rate = draw_count(mean=x))
#>   ID   x theft_rate
#> 1  1   0          0
#> 2  2   5          5
#> 3  3  25         26
#> 4  4  50         51
#> 5  5 100         88

# Categorical data: useful for demographic data.
fabricate(N = 6, p1 = runif(N), p2 = runif(N), p3 = runif(N),
          cat = draw_categorical(cbind(p1, p2, p3)))
#>   ID         p1         p2        p3 cat
#> 1  1 0.09022236 0.22092086 0.1625581   2
#> 2  2 0.23832468 0.06540764 0.5351122   2
#> 3  3 0.25913474 0.64489713 0.5850109   1
#> 4  4 0.02566954 0.31255200 0.3186254   2
#> 5  5 0.17555081 0.63886730 0.4680848   3
#> 6  6 0.61982165 0.97387534 0.6392598   1

Draw discrete variables including binary, binomial count, poisson count, ordered, and categorical

Arguments

Value

Details

Examples