Single-case data generator — random

The random_scdf function generates random single-case data frames for monte-carlo studies and demonstration purposes. design is used to set up a design matrix with all parameters needed for the random_scdf function.

Usage

random_scdf(design = NULL, round = NA, random_names = FALSE, seed = NULL, ...)

Arguments

design: A design matrix which is created by design and specifies all parameters.
round: Rounds the scores to the defined decimal. To round to the second decimal, set round = 2.
random_names: Is FALSE by default. If set random_names = TRUE cases are assigned random first names. If set "neutral", "male" or "female" only gender neutral, male, or female names are chosen. The names are drawn from the 2,000 most popular names for newborns in 2012 in the U.S. (1,000 male and 1,000 female names).
seed: A seed number for the random generator.
...: arguments that are directly passed to the design function for a more concise coding.

Value

A single-case data frame. See scdf to learn about this format.

Author

Juergen Wibert

Examples


## Create random single-case data and inspect it
design <- design(
  n = 3, rtt = 0.75, slope = 0.1, extreme_prop = 0.1,
  missing_prop = 0.1
)
dat <- random_scdf(design, round = 1, random_names = TRUE, seed = 123)
describe(dat)
#> Describe Single-Case Data
#> 
#>        Vanessa Bryn Tia
#> Design     A-B  A-B A-B
#> n.A          5    5   5
#> n.B         15   15  15
#> mis.A        0    1   0
#> mis.B        2    1   2
#> 
#>         Vanessa   Bryn    Tia
#> m.A       51.12  50.00  54.36
#> m.B      57.115 52.793 56.892
#> md.A       50.4   49.2   52.6
#> md.B      59.60  55.75  59.20
#> sd.A      4.672  3.631  4.538
#> sd.B     10.403 12.893  8.003
#> mad.A     2.520  2.076  3.410
#> mad.B     7.858 10.601  6.672
#> min.A      46.8   46.5   50.3
#> min.B      29.7   19.7   38.7
#> max.A      59.0   55.1   61.8
#> max.B      71.3   65.2   67.7
#> trend.A    0.95   1.36   2.27
#> trend.B   0.935  1.693  1.358

## And now have a look at poisson-distributed data
design <- design(
  n = 3, B_start = c(6, 10, 14), mt = c(12, 20, 22), start_value = 10,
  distribution = "poisson", level = -5, missing_prop = 0.1
)
dat <- random_scdf(design, seed = 1234)
pand(dat, decreasing = TRUE)
#> Percentage of all non-overlapping data
#> 
#> Method: sort 
#> 
#> PAND = 91.8%
#> Φ =  0.836  ; Φ² =  0.699 
#> 
#> 49 measurements (23 Phase A, 26 Phase B) in 3 cases
#> Overlapping data: n = 4 ; percentage = 8.2 
#> 
#> 2 x 2 Matrix of percentages
#>          A    B total
#> A     42.9  4.1  46.9
#> B      4.1 49.0  53.1
#> total 46.9 53.1 100.0
#> 
#> 2 x 2 Matrix of counts
#>        A  B total
#> A     21  2    23
#> B      2 24    26
#> total 23 26    49
#> 
#> 
#> Chi-Squared test:
#> X² = 34.256, df = 1, p = 0.000 
#> 
#> Fisher exact test:
#> Odds ratio = 99.881, p = 0.000