The describe() function provides common descriptive statistics for
single-case data.
Arguments
- data
A single-case data frame. See
scdf()to learn about this format.- dvar
Character string with the name of the dependent variable. Defaults to the attributes in the scdf file.
- pvar
Character string with the name of the phase variable. Defaults to the attributes in the scdf file.
- mvar
Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file.
Value
A list containing a data frame of descriptive statistics (descriptives); the cse design (design); the number of cases (N)
Details
n = number of measurements; mis = number of missing vaues; m = mean; md = median; sd = standard deviation; mad = median average deviation; min = minimum; max = maximum; trend = weight of depended variable regressed on time (values ~ mt).
Examples
## Descriptive statistics for a study of three single-cases
describe(Grosche2011)
#> Describe Single-Case Data
#>
#> Eva Georg Olaf
#> Design A-B A-B A-B
#> n.A 6 7 12
#> n.B 13 17 8
#> mis.A 0 0 0
#> mis.B 0 0 0
#>
#> Eva Georg Olaf
#> m.A 2.677 10.469 6.036
#> m.B 3.435 9.799 5.995
#> md.A 2.945 8.820 6.355
#> md.B 3.310 10.590 5.905
#> sd.A 0.750 4.112 1.524
#> sd.B 1.029 2.089 1.225
#> mad.A 0.541 4.448 1.794
#> mad.B 1.290 2.090 1.416
#> min.A 1.46 5.82 3.02
#> min.B 1.86 5.60 4.53
#> max.A 3.39 17.40 7.69
#> max.B 4.98 12.79 8.14
#> trend.A 0.014 -0.268 0.007
#> trend.B 0.044 0.043 0.000
## Descriptives of a three phase design
describe(exampleABC)
#> Describe Single-Case Data
#>
#> Marie Rosalind Lise
#> Design A-B-C A-B-C A-B-C
#> n.A 10 15 20
#> n.B 10 8 7
#> n.C 10 7 3
#> mis.A 0 0 0
#> mis.B 0 0 0
#> mis.C 0 0 0
#>
#> Marie Rosalind Lise
#> m.A 52.000 52.267 52.350
#> m.B 72.100 73.250 73.571
#> m.C 68.000 66.429 71.333
#> md.A 53.5 52.0 52.0
#> md.B 72.5 72.0 73.0
#> md.C 69 68 76
#> sd.A 8.287 8.146 10.869
#> sd.B 11.367 13.134 10.644
#> sd.C 12.702 10.486 21.385
#> mad.A 11.119 7.413 10.378
#> mad.B 10.378 10.378 16.309
#> mad.C 17.791 11.861 20.756
#> min.A 39 37 35
#> min.B 47 54 60
#> min.C 51 52 48
#> max.A 63 65 74
#> max.B 85 97 87
#> max.C 87 78 90
#> trend.A -1.915 0.500 -0.088
#> trend.B -0.612 0.643 1.929
#> trend.C -0.194 -2.929 -14.000
## Write descriptive statistics to .csv-file
study <- describe(Waddell2011)
write.csv(study$descriptives, file = tempfile())