The SPRITE algorithm aims to construct possible distributions that conform to
observed/reported parameters. This function performs some checks and returns a list of these
parameters that can then be passed to the functions that actually generate
the distributions (e.g. find_possible_distribution)
set_parameters(
mean,
sd,
n_obs,
min_val,
max_val,
m_prec = NULL,
sd_prec = NULL,
n_items = 1,
restrictions_exact = NULL,
restrictions_minimum = NULL,
dont_test = FALSE
)The mean of the distribution
The standard deviation of the distribution
The number of observations (sample size)
The minimum value
The maximum value
The precision of the mean, as number of digits after the decimal point.
If not provided, taken based on the significant digits of mean - so only needed if reported mean ends in 0
The precision of the standard deviation, again only needed if reported standard deviation ends in 0.
Number of items in scale, if distribution represents scale averages. Defaults to 1, which represents any single-item measure.
Restrictions on the exact frequency of specific responses, see Details
Restrictions on the minimum frequency of specific responses, see Details
By default, this function tests whether the mean is possible, given the sample size (GRIM-test) and whether the standard deviation is possible, given mean and sample size (GRIMMER test), and fails otherwise. If you want to override this, and run SPRITE anyway, you can set this to TRUE.
A named list of parameters, pre-processed for further rsprite2 functions.
Restrictions can be used to define how often a specific value should appear in the sample.
They need to be passed as a list in the form value = frequency. Thus, to specify that
there should be no 3s and five 4s in the distribution, you would pass
restrictions_exact = list("3" = 0, "4" = 5). To specify that there should be at least
one 1 and one 7, you would pass restrictions_minimum = list("1" = 1, "7" = 1). If you just want to
specify that the minimum and maximum values appear at least once (for instance when they are the
reported rather than possible range), you can use the shortcut restrictions_minimum = "range". Finally,
if you work with multi-item scales that result in decimal responses, round those names to two decimal points, e.g.,
when n_items = 3 you could specify list("1.67" = 0).
set.seed(1234) #To get reproducible results
# Simple case
sprite_parameters <- set_parameters(mean = 2.2, sd = 1.3, n_obs = 20, min_val = 1, max_val = 5)
find_possible_distribution(sprite_parameters)
#> $outcome
#> [1] "success"
#>
#> $values
#> [1] 2 1 4 2 1 4 1 1 2 2 1 4 3 3 1 2 4 1 5 1
#>
#> $mean
#> [1] 2.25
#>
#> $sd
#> [1] 1.332785
#>
#> $iterations
#> [1] 2
#>
# With restrictions
sprite_parameters <- set_parameters(mean = 1.95, sd = 1.55, n_obs = 20,
min_val = 1, max_val = 5, n_items = 3,
restrictions_exact = list("3"=0, "3.67" = 2),
restrictions_minimum = "range")
find_possible_distribution(sprite_parameters)
#> $outcome
#> [1] "success"
#>
#> $values
#> [1] 5.000000 1.000000 1.000000 1.000000 1.000000 1.666667 1.000000 2.000000
#> [9] 1.000000 1.000000 5.000000 1.000000 1.000000 1.000000 1.000000 1.000000
#> [17] 1.000000 5.000000 3.666667 3.666667
#>
#> $mean
#> [1] 1.95
#>
#> $sd
#> [1] 1.549476
#>
#> $iterations
#> [1] 90
#>