scdf() is the constructor for the scdf class. It stores single-case study
data with one or more single-cases.
Usage
scdf(
values,
B_start,
mt,
phase,
phase_design = NULL,
phase_starts = NULL,
name = NULL,
dvar = "values",
pvar = "phase",
mvar = "mt",
...
)Arguments
- values
A vector containing measurement values of the dependent variable.
- B_start
The first measurement of phase B (simple coding if design is strictly AB).
- mt
A vector defining measurement times. Default is
mt = (1,2,3,...,n).- phase
A vector defining phase assignments.
- phase_design
A list defining the length and label of each phase. E.g.,
phase_design = c(A1 = 10, B1 = 10, A2 = 10, B2 = 10).- phase_starts
A vector defining the label and measurement time of each phase start. E.g.,
phase_starts = c(A1 = 1, B1 = 6, A2 = 14, B2 = 19).- name
A name for the case.
- 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.
- ...
Additional variables. E.g.,
teacher = c(0,1,0,1,0,0,1), lesson = c(1,3,4,5,2,3).
Value
Returns a single-case data frame scdf suitable for all
functions of the scan package. Multiple data sets (e.g. from
Multiple Baseline Designs) can be listed.
Details
If the dependent variable is a named vector then the names are
extracted to create a phase design (e.g., values = c(A = 2,3,5,4,3, B = 6,5,4,3) will create an AB phase design with five and four measurements).
An scdf contains several attributes: dvar The name of the dependent
variable. phase The name of the phase variable. mt The name
of the measurement time variable. author Information on the author
of the data. info Further information on the data. E.g., a
publication. dvar, phase, and mt are the defaults most of the
scan function use. You can change the values of the attributes with
the scdf_attr function (e.g., scdf_attr(exampleAB_add, "dvar") <- "depression" defines depression as the dependent variable. Please
notice that all scan functions have arguments to define dvar,
phase, and mt for a given analysis.
See also
Other data manipulation functions:
add_l2(),
as.data.frame.scdf(),
as_scdf(),
fill_missing(),
moving_median(),
outlier(),
ranks(),
rescale(),
select_cases(),
set_vars(),
shift(),
smooth_cases(),
standardize(),
truncate_phase()
Examples
## Scores on a letter naming task were collected on eleven days in a row.
## The intervention started after the fifth measurement,
## so the first B phase measurement was 6 (B_start = 6).
klaas <- scdf(
c(5, 7, 8, 5, 7, 12, 16, 18, 15, 14, 19),
B_start = 6, name = "Klaas"
)
describe(klaas)
#> Describe Single-Case Data
#>
#> Klaas
#> Design A-B
#> n.A 5
#> n.B 6
#> mis.A 0
#> mis.B 0
#>
#> Klaas
#> m.A 6.4
#> m.B 15.667
#> md.A 7
#> md.B 15.5
#> sd.A 1.342
#> sd.B 2.582
#> mad.A 1.483
#> mad.B 2.965
#> min.A 5
#> min.B 12
#> max.A 8
#> max.B 19
#> trend.A 0.2
#> trend.B 0.743
# Alternative coding 1:
klaas <- scdf(
c(A = 5, 7, 8, 5, 7, B = 12, 16, 18, 15, 14, 19),
name = "Klaas"
)
# Alternative coding 2:
klaas <- scdf(
c(5, 7, 8, 5, 7, 12, 16, 18, 15, 14, 19),
phase_design = c(A = 5, B = 6), name = "Klaas"
)
# Alternative coding 3:
klaas <- scdf(
c(5, 7, 8, 5, 7, 12, 16, 18, 15, 14, 19),
phase_starts = c(A = 1, B = 7), name = "Klaas"
)
## Unfortunately in a similar study there were no data collected on
## days 3 and 9. Use NA to pass them to the function:
emmi <- scdf(c(5, 7, NA, 5, 7, 12, 16, 18, NA, 14, 19),
phase_design = c(A = 5, B = 6), name = "Emmi"
)
describe(emmi)
#> Describe Single-Case Data
#>
#> Emmi
#> Design A-B
#> n.A 5
#> n.B 6
#> mis.A 1
#> mis.B 1
#>
#> Emmi
#> m.A 6
#> m.B 15.8
#> md.A 6
#> md.B 16
#> sd.A 1.155
#> sd.B 2.864
#> mad.A 1.483
#> mad.B 2.965
#> min.A 5
#> min.B 12
#> max.A 7
#> max.B 19
#> trend.A 0.2
#> trend.B 0.779
## In a MBD over three cases, data were collected eleven days in a row.
## Intervention starting points differ between subjects as they were
## randomly assigned. The three SCDFs are then combined in a list for
## further conjoined analyses.
charlotte <- scdf(c(A = 5, 7, 10, 5, 12, B = 7, 10, 18, 15, 14, 19))
theresa <- scdf(c(A = 3, 4, 3, 5, B = 7, 4, 7, 9, 8, 10, 12))
antonia <- scdf(c(A = 9, 8, 8, 7, 5, 7, B = 6, 14, 15, 12, 16))
mbd <- c(charlotte, theresa, antonia)
names(mbd) <- c("Charlotte", "Theresa", "Antonia")
overlap(mbd)
#> Overlap Indices
#>
#> Comparing phase 1 against phase 2
#>
#> Charlotte Theresa Antonia
#> Design A-B A-B A-B
#> PND 67 86 80
#> PEM 83 100 80
#> PET 33 86 100
#> NAP 87 95 83
#> NAP rescaled 73 89 67
#> PAND 82 82 82
#> IRD 0.63 0.80 0.82
#> Tau_U(A) 0.37 0.52 0.63
#> Tau_U(BA) 0.49 0.71 0.69
#> Base_Tau 0.56 0.66 0.66
#> Diff_mean 6.03 4.39 5.27
#> Diff_trend 0.77 0.50 2.37
#> SMD 1.94 4.59 3.85
#> Hedges_g 1.37 1.87 1.70
## In a classroom-based intervention it was not possible to measure outcomes
## every day, but only on schooldays. The sequence of measurements is passed
## to the package by using a vector of measurement times.
frida <- scdf(
c(A = 3, 2, 4, 2, 2, 3, 5, 6, B = 8, 10, 8, 12, 14, 13, 12),
mt = c(1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18)
)
summary(frida)
#> #A single-case data frame with one case
#>
#> Measurements Design
#> Case1 15 A-B
#>
#> Variable names:
#> values <dependent variable>
#> phase <phase variable>
#> mt <measurement-time variable>
#>
describe(frida)
#> Describe Single-Case Data
#>
#> Case1
#> Design A-B
#> n.A 8
#> n.B 7
#> mis.A 0
#> mis.B 0
#>
#> Case1
#> m.A 3.375
#> m.B 11
#> md.A 3
#> md.B 12
#> sd.A 1.506
#> sd.B 2.38
#> mad.A 1.483
#> mad.B 2.965
#> min.A 2
#> min.B 8
#> max.A 6
#> max.B 14
#> trend.A 0.305
#> trend.B 0.71
## example with two independent variables and four phases
jim <- scdf(
zvt = c(47, 58, 76, 63, 71, 59, 64, 69, 72, 77, 76, 73),
d2 = c(131, 134, 141, 141, 140, 140, 138, 140, 141, 140, 138, 140),
phase_design = c(A1 = 3, B1 = 3, A2 = 3, B2 = 3), dvar = "zvt"
)
overlap(jim, phases = list(c("A1", "A2"), c("B1", "B2")))
#> Overlap Indices
#>
#> Comparing phases A1 + A2 against phases B1 + B2
#>
#> Case1
#> Design A1-B1-A2-B2
#> PND 17
#> PEM 67
#> PET 67
#> NAP 68
#> NAP rescaled 36
#> PAND 67
#> IRD 0.33
#> Tau_U(A) 0.07
#> Tau_U(BA) 0.14
#> Base_Tau 0.27
#> Diff_mean 5.5
#> Diff_trend -0.31
#> SMD 0.52
#> Hedges_g 0.56
#>
#> The following variables were used in this analysis:
#> 'zvt' as dependent variable, 'phase' as phase variable, and 'mt' as measurement-time variable.