`R/variable_creation_functions.R`

`draw_discrete.Rd`

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_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_quantile(type, N)
```

- 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`

, the latent data for each observation.- breaks
vector of breaks to cut a latent outcome into ordered categories with

`draw_ordered`

- 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
The number of buckets to split data into. For a median split, enter 2; for terciles, enter 3; for quartiles, enter 4; for quintiles, 5; for deciles, 10.

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.

For variables with intra-cluster correlations, see
`draw_binary_icc`

and `draw_normal_icc`

```
# 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 2.557354 1
#> 2 2 -11.789878 0
#> 3 3 -7.021686 0
# 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 4
#> 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 -0.4480415 2
#> 2 2 1.1573840 3
#> 3 3 -0.3886420 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 1.701911 Very concerned
#> 2 2 -3.840224 Not at all concerned
#> 3 3 0.315974 Somewhat concerned
# 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 6
#> 3 3 25 28
#> 4 4 50 50
#> 5 5 100 93
# 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 0.4410702 0.16962063 0.02071859 1
#> 2 2 0.7369205 0.20115172 0.85366067 1
#> 3 3 0.9815113 0.00863554 0.73225794 1
#> 4 4 0.4019670 0.72577925 0.93173699 3
#> 5 5 0.7157953 0.36336891 0.75901858 1
#> 6 6 0.1646743 0.92153621 0.13260611 1
```