exampleAB |>
overlap() |>
export() |>
kableExtra::save_kable("my_file.png", zoom = 4)14 Exporting scan results
The export function call
export(object, …)
The export function will make it easier to convert the results of your scan analyses into tables and descriptions you can add to your documents and presentations. Basically, export takes a scan object and converts it to an html-table or latex output.
Note
export it build on top of the knitr and kableextra packages. The list provided in the kable_options argument is implemented in the kable function of knitr and the list provided to the kable_styling_options is implemented in the kable_styling command of the kableExtra package. export sets some defaults for these functions but you can play around and overwrite them.
export works best when used within an rmarkdown file and/or within RStudio. In RStudio, the html table will be displayed in the Viewer pane. There you can click the export button (
) to export an html or bitmap file or you can try drag and drop ➡️ copy and paste the table into another application.
Alternatively, you can set the filename argument to export the table directly from within the export function. The file name extension you provide will define the resulting file format (e.g. filename = "results.html"). Possible extensions are html, png, and jpg.
If you need more control on the output parameters, use the save_kable() function from the kableExtra package.
Most of the tables will have a default caption and footnotes. If you want to change these, use the caption and footnote arguments.
14.1 Single case data files
The scdf export function call
export(object, caption = NA, footnote = NA, filename = NA, kable_styling_options = list(), kable_options = list(), cols, …)
export(exampleA1B1A2B2_zvt)| zvt | d2 | day | part | zvt | d2 | day | part | zvt | d2 | day | part |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 47 | 131 | 1 | A1 | 51 | 100 | 1 | A1 | 54 | 89 | 1 | A1 |
| 58 | 134 | 2 | A1 | 58 | 126 | 2 | A1 | 57 | 116 | 2 | A1 |
| 76 | 141 | 3 | A1 | 70 | 130 | 3 | A1 | 51 | 114 | 3 | A1 |
| 63 | 141 | 4 | B1 | 65 | 130 | 4 | B1 | 61 | 131 | 4 | B1 |
| 71 | 140 | 5 | B1 | 67 | 137 | 5 | B1 | 57 | 132 | 5 | B1 |
| 59 | 140 | 6 | B1 | 63 | 133 | 6 | B1 | 53 | 130 | 6 | B1 |
| 64 | 138 | 7 | A2 | 64 | 136 | 7 | A2 | 58 | 128 | 7 | A2 |
| 69 | 140 | 8 | A2 | 70 | 137 | 8 | A2 | 57 | 131 | 8 | A2 |
| 72 | 141 | 9 | A2 | 70 | 135 | 9 | A2 | 60 | 130 | 9 | A2 |
| 77 | 140 | 10 | B2 | 68 | 128 | 10 | B2 | 55 | 129 | 10 | B2 |
| 76 | 138 | 11 | B2 | 69 | 137 | 11 | B2 | 58 | 118 | 11 | B2 |
| 73 | 140 | 12 | B2 | 70 | 138 | 12 | B2 | 58 | 131 | 12 | B2 |
14.2 Descriptive stats
The describe export function call
export(object, caption = NA, footnote = NA, filename = NA, kable_styling_options = list(), kable_options = list(), flip = FALSE, …)
res <- describe(GruenkeWilbert2014)
export(res)| Case | Design | A | B | A | B | A | B | A | B | A | B | A | B | A | B | A | B | A | B |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Anton | A-B | 4 | 14 | 0 | 0 | 5.00 | 9.14 | 5 | 9 | 0.82 | 0.77 | 0.74 | 1.48 | 4 | 8 | 6 | 10 | -0.40 | 0.03 |
| Bob | A-B | 7 | 11 | 0 | 0 | 3.00 | 8.82 | 3 | 9 | 0.82 | 0.87 | 1.48 | 0.00 | 2 | 7 | 4 | 10 | 0.04 | 0.04 |
| Paul | A-B | 6 | 12 | 0 | 0 | 3.83 | 8.83 | 4 | 9 | 0.75 | 0.72 | 0.74 | 0.74 | 3 | 8 | 5 | 10 | -0.26 | 0.02 |
| Robert | A-B | 8 | 10 | 0 | 0 | 4.12 | 8.90 | 4 | 9 | 0.83 | 0.99 | 1.48 | 1.48 | 3 | 7 | 5 | 10 | -0.06 | -0.14 |
| Sam | A-B | 5 | 13 | 0 | 0 | 4.60 | 9.08 | 5 | 9 | 0.55 | 0.86 | 0.00 | 1.48 | 4 | 8 | 5 | 10 | 0.10 | 0.03 |
| Tim | A-B | 4 | 14 | 0 | 0 | 3.00 | 9.00 | 3 | 9 | 0.82 | 0.96 | 0.74 | 1.48 | 2 | 7 | 4 | 10 | -0.60 | 0.00 |
| Note: n = Number of measurements; Missing = Number of missing values; M = Mean; Median = Median; SD = Standard deviation; MAD = Median average deviation; Min = Minimum; Max = Maximum; Trend = Slope of dependent variable regressed on measurement-time. |
14.3 Standardized mean differences
The smd export function call
export(object, caption = NA, footnote = NA, filename = NA, select = c(“Case”, Mean A = “mA”, Mean B = “mB”, SD A = “sdA”, SD B = “sdB”, SD Cohen = “sd cohen”, SD Hedges = “sd hedges”, “Glass’ delta”, “Hedges’ g”, “Hedges’ g correction”, “Hedges’ g durlak correction”, “Cohen’s d”), kable_styling_options = list(), kable_options = list(), round = 2, flip = FALSE, …)
smd(exampleAB) |> export(flip = TRUE)| Statistic | Johanna | Karolina | Anja |
|---|---|---|---|
| Mean B | 74.13 | 73.47 | 74.07 |
| SD A | 2.41 | 6.83 | 3.05 |
| SD B | 8.94 | 9.76 | 7.57 |
| SD Cohen | 6.55 | 8.43 | 5.77 |
| SD Hedges | 7.97 | 9.19 | 6.83 |
| Glass' delta | 8.11 | 3.17 | 6.71 |
| Hedges' g | 2.45 | 2.36 | 3.00 |
| Hedges' g correction | 2.35 | 2.26 | 2.87 |
| Hedges' g durlak correction | 2.23 | 2.14 | 2.72 |
| Cohen's d | 2.98 | 2.57 | 3.55 |
| Note: SD Cohen = unweigted average of the variance of both phases; SD Hedges = weighted average of the variance of both phases with a degrees of freedom correction; Glass' delta = mean difference divided by the standard deviation of the A-phase; Hedges' g = mean difference divided by SD Hedges; Hedges' g (durlak) correction = approaches for correcting Hedges' g for small sample sizes; Cohens d = mean difference divided by SD Cohen. |
14.4 Trend analysis
The trend export function call
export(object, caption = NA, footnote = NA, filename = NA, kable_styling_options = list(), kable_options = list(), round = 2, …)
exampleABC$Marie %>%
trend() %>%
export()| Phase | Intercept | B | Beta |
|---|---|---|---|
| Linear | |||
| ALL | 55.16 | 0.61 | 0.39 |
| A | 60.62 | -1.92 | -0.70 |
| B | 74.85 | -0.61 | -0.16 |
| C | 68.87 | -0.19 | -0.05 |
| Quadratic | |||
| ALL | 59.13 | 0.02 | 0.33 |
| A | 57.94 | -0.21 | -0.71 |
| B | 73.22 | -0.04 | -0.10 |
| C | 68.49 | -0.02 | -0.04 |
14.5 Overlap indices
The overlap export function call
export(object, caption = NA, footnote = NULL, filename = NA, kable_styling_options = list(), kable_options = list(), round = 2, flip = FALSE, …)
exampleA1B1A2B2_zvt %>%
select_phases(A = c(1,3), B = c(2,4)) %>%
overlap() %>%
export(flip = TRUE)| Statistic | Tick | Trick | Track |
|---|---|---|---|
| PND | 16.67 | 0.00 | 16.67 |
| PEM | 66.67 | 50.00 | 50.00 |
| PET | 66.67 | 33.33 | 33.33 |
| NAP | 68.06 | 51.39 | 58.33 |
| NAP-R | 36.11 | 2.78 | 16.67 |
| PAND | 66.67 | 50.00 | 66.67 |
| IRD | 0.33 | 0.33 | 0.17 |
| Tau-U (A + B - trend A) | 0.07 | -0.16 | -0.04 |
| Tau-U (A + B - trend A + trend B) | 0.14 | 0.03 | -0.03 |
| Base Tau | 0.27 | -0.25 | 0.13 |
| Delta M | 5.50 | 3.17 | 0.83 |
| Delta Trend | -0.31 | -1.10 | -0.74 |
| SMD | 0.52 | 0.40 | 0.26 |
| Hedges g | 0.56 | 0.50 | 0.26 |
| Note: PND = Percentage Non-Overlapping Data; PEM = Percentage Exceeding the Median; PET = Percentage Exceeding the Trend; NAP = Nonoverlap of all pairs; NAP-R = NAP rescaled; PAND = Percentage all nonoverlapping data; IRD = Improvement rate difference; Tau U (A + B - trend A) = Parker's Tau-U; Tau U (A + B - trend A + trend B) = Parker's Tau-U; Base Tau = Baseline corrected Tau; Delta M = Mean difference between phases; Delta Trend = Trend difference between phases; SMD = Standardized Mean Difference; Hedges g = Corrected SMD. |
tau_u(exampleAB_decreasing) |> export()
14.6 Tau-U
The tauu export function call
export(object, caption = NA, footnote = NA, filename = NA, select = “auto”, kable_styling_options = list(), kable_options = list(), meta = TRUE, …)
Set the argument meta = TRUE (the default) to get the results of the meta analysis or set meta = FALSE to get a table with each case.
tau_u(exampleAB_decreasing) |> export()| Model | Tau U | se | CI lower | CI upper | z | p |
|---|---|---|---|---|---|---|
| A vs. B | -0.98 | 0.14 | -0.99 | -0.96 | -16.04 | <.001 |
| A vs. B - Trend A | -0.59 | 0.14 | -0.74 | -0.38 | -4.82 | <.001 |
| A vs. B + Trend B | -0.58 | 0.14 | -0.73 | -0.37 | -4.72 | <.001 |
| A vs. B + Trend B - Trend A | -0.53 | 0.14 | -0.70 | -0.31 | -4.21 | <.001 |
| Note: Method is ' complete '. Analyses based on Kendall's Tau b . 95 % CIs for tau are reported. |
tau_u(exampleAB_decreasing) |> export(meta = FALSE)| Case | Model | Tau | CI lower | CI upper | Z | p |
|---|---|---|---|---|---|---|
| Peter | ||||||
| A vs. B | -0.98 | -0.99 | -0.94 | -3.53 | <.001 | |
| Trend A | -0.29 | -0.86 | 0.59 | -0.90 | .36 | |
| Trend B | -0.10 | -0.62 | 0.47 | -0.49 | .62 | |
| A vs. B - Trend A | -0.57 | -0.81 | -0.18 | -3.18 | <.01 | |
| A vs. B + Trend B | -0.55 | -0.80 | -0.14 | -3.23 | <.01 | |
| A vs. B + Trend B - Trend A | -0.48 | -0.76 | -0.05 | -2.96 | <.01 | |
| Tony | ||||||
| A vs. B | -0.98 | -0.99 | -0.95 | -3.63 | <.001 | |
| Trend A | -0.18 | -0.79 | 0.60 | -0.62 | .53 | |
| Trend B | -0.09 | -0.63 | 0.51 | -0.41 | .68 | |
| A vs. B - Trend A | -0.58 | -0.82 | -0.19 | -3.28 | <.01 | |
| A vs. B + Trend B | -0.57 | -0.81 | -0.18 | -3.37 | <.001 | |
| A vs. B + Trend B - Trend A | -0.50 | -0.77 | -0.08 | -3.09 | <.01 | |
| Bruce | ||||||
| A vs. B | -0.98 | -0.99 | -0.94 | -3.38 | <.001 | |
| Trend A | 0.07 | -0.79 | 0.83 | 0.19 | .85 | |
| Trend B | -0.33 | -0.73 | 0.24 | -1.64 | .10 | |
| A vs. B - Trend A | -0.61 | -0.83 | -0.22 | -3.34 | <.001 | |
| A vs. B + Trend B | -0.62 | -0.83 | -0.24 | -3.69 | <.001 | |
| A vs. B + Trend B - Trend A | -0.60 | -0.82 | -0.21 | -3.67 | <.001 | |
| Note: Method is ' complete '. Analyses based on Kendall's Tau b . 95 % CIs for tau are reported. |
14.7 Piecewise linear models
The plm export function call
export(object, caption = NA, footnote = NA, filename = NA, kable_styling_options = list(), kable_options = list(), nice = TRUE, …)
res <- plm(exampleA1B1A2B2$Pawel)
export(res)| Parameter | B | 2.5% | 97.5% | SE | t | p | delta R² |
|---|---|---|---|---|---|---|---|
| Intercept | 12.69 | 6.18 | 19.20 | 3.32 | 3.82 | <.01 | |
| Trend mt | 0.22 | -1.00 | 1.44 | 0.62 | 0.36 | .72 | 0.001 |
| Level phase B1 | 16.28 | 6.31 | 26.26 | 5.09 | 3.20 | <.01 | 0.118 |
| Level phase A2 | 1.48 | -18.81 | 21.76 | 10.35 | 0.14 | .88 | 0.000 |
| Level phase B2 | 11.46 | -20.49 | 43.40 | 16.30 | 0.70 | .48 | 0.006 |
| Slope phase B1 | -1.41 | -3.13 | 0.32 | 0.88 | -1.60 | .12 | 0.029 |
| Slope phase A2 | -1.10 | -2.83 | 0.62 | 0.88 | -1.25 | .21 | 0.018 |
| Slope phase B2 | -1.08 | -2.81 | 0.64 | 0.88 | -1.23 | .22 | 0.017 |
| Note: F(7, 32) = 7.86; p <.001; R² = 0.632; Adjusted R² = 0.552 |
14.8 Hierarchical piecewise regressions
The hplm export function call
export(object, caption = NA, footnote = NA, filename = NA, kable_styling_options = list(), kable_options = list(), round = 2, nice = TRUE, …)
exampleAB_50 %>%
add_l2(exampleAB_50.l2) %>%
hplm(lr.test = TRUE, random.slopes = TRUE) %>%
export()| Predictors | B | SE | df | t | p |
|---|---|---|---|---|---|
| Intercept | 48.21 | 1.4 | 1328 | 34.5 | <.001 |
| Trend mt | 0.62 | 0.11 | 1328 | 5.52 | <.001 |
| Level phase B | 13.87 | 0.89 | 1328 | 15.51 | <.001 |
| Slope phase B | 0.86 | 0.12 | 1328 | 7.43 | <.001 |
Random effects |
|||||
| SD | L | df | p | ||
| Intercept | 9.35 | 348.85 | 4 | <.001 | |
| Trend mt | 0.1 | 0.83 | 4 | .93 | |
| Level phase B | 4.54 | 42.82 | 4 | <.001 | |
| Slope phase B | 0.13 | 0.76 | 4 | .94 | |
| Residual | 4.97 | NA | NA | NA | |
Model |
|||||
| AIC | 8693.2 | ||||
| BIC | 8771.7 | ||||
| ICC | 0.29 | L = 339 | p <.001 | ||
| Note: Estimation method ML; Slope estimation method: W; first; 50 cases. | |||||
14.9 Power analyses
The power_test export function call
export(object, caption = NA, footnote = NA, filename = NA, kable_styling_options = list(), kable_options = list(), …)
design <- design(
n = 1, phase_design = list(A = 6, B = 9),
rtt = 0.8, level = 1.4, trend = 0.05
)
set.seed(124)
power_test(design, n_sim = 10) |> export()| Method | Power | Alpha Error | Alpha:Beta | Correct |
|---|---|---|---|---|
| plm_level | 80 | 10 | 1:2.0 | 85 |
| rand | 90 | 30 | 1:0.3 | 80 |
| tauU | 100 | 10 | 1:0.0 | 95 |