plot.scdf: Plot single-case data
In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Description Usage Arguments Value Author(s) See Also Examples

The plotSC function provides a plot of a single-case or multiple single-cases.

## S3 method for class 'scdf'
plot(...)

plotSC(
  data,
  dvar,
  pvar,
  mvar,
  ylim = NULL,
  xlim = NULL,
  xinc = 1,
  lines = NULL,
  marks = NULL,
  phase.names = NULL,
  xlab = NULL,
  ylab = NULL,
  main = "",
  case.names = NULL,
  style = "grid",
  ...
)

`...`	Further arguments passed to the plot command.
`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.
`ylim`	Lower and upper limits of the y-axis (e.g., `ylim = c(0, 20)` sets the y-axis to a scale from 0 to 20). With multiple single-cases you can use `ylim = c(0, NA)` to scale the y-axis from 0 to the maximum of each case. `ylim` is not set by default, which makes `scan` set a proper scale based on the given data.
`xlim`	Lower and upper limits of the x-axis (e.g., `xlim = c(0, 20)` sets the x-axis to a scale from 0 to 20). With multiple single-cases you can use `ylim = c(0, NA)` to scale the x-axis from 0 to the maximum of each case. `xlim` is not set by default, which makes `scan` set a proper scale based on the given data.
`xinc`	An integer. Increment of the x-axis. 1 :each mt value will be printed, 2 : every other value, 3 : every third values etc.
`lines`	A character or list defining one or more lines or curves to be plotted. The argument is either passed as a character string (e.g., `lines = "median"`) or as a list (e.g., `list("median", "trend")`. Some of the procedures can be refined with an additional argument (e.g., `lines = list("mean" = 0.20)` adds a 20% trimmed mean line. By default no additional lines are plotted. Possible lines are: `"median"` Separate lines for phase A and B medians. `"mean"` Separate lines for phase A and B means. By default it is 10%-trimmed. Other trims can be set, using a second parameter (e.g., `lines = list(mean = 0.2)` draws a 20%-trimmed mean line). `"trend"` Separate lines for phase A and B trends. `"trendA"` Trend line for phase A, extrapolated throughout phase B. `"maxA/minA"` Line at the level of the highest or lowest phase A score. `"medianA"` Line at the phase A median score. `"meanA"` Line at the phase A 10%-trimmed mean score. Apply a different trim, by using the additional argument (e.g., `lines = list(meanA = 0.2)`). `"plm"` Regression lines for piecewise linear regression model. `"plm.ar"` Regression lines for piecewise autoregression model. The lag is specified like this: `lines = list(plm.ar = 2)`. Default lag is set to 2. `"movingMean"` Draws a moving mean curve, with a specified lag: `lines = list(movingMean = 2)`. Default is a lag 1 curve. `"movingMedian"` Draws a moving median curve, with a specified lag: `lines = list(movingMedian = 3)`. Default is a lag 1 curve. `"loreg"` Draws a non-parametric local regression line. The proportion of data influencing each data point can be specified using `lines = list("loreg" = 0.66)`. The default is 0.5. `"lty"` Use this argument to define the line type. Examples are: `"solid"`, `"dashed"`, `"dotted"`. `"lwd"` Use this argument to define the line's thickness, e.g., `lwd = 4`. `"col"` Use this argument to define the line's color, e.g., `col = "red"`.
`marks`	A list of parameters defining markings of certain data points. `"positions"` A vector or a list of vectors indicating measurement-times to be highlighted. In case of a vector, the marked measurement-times are the same for all plotted cases. In case of a list of vectors, marks are set differently for each case. The list must have the same length as there are cases in the data file. `"col"` Color of the marks. `"cex"` Size of the marks. Use for example `marks = list(positions = c(1, 8, 15), col = "red", cex = 3)` to make the MTs one, eight and 18 appear big and red.
`phase.names`	By default phases are labeled based on the levels of the phase variable. Use this argument to specify different labels: `phase.names = c("Baseline", "Intervention")`.
`xlab`	The label of the x-axis. Default is `xlab = "Measurement time"`.
`ylab`	The labels of the y-axis. Default is `ylab = "Score"`.
`main`	Main title of the plot.
`case.names`	Case names. If not provided, names are taken from the scdf. Set `case.names = ""` if you don't like to include case names.
`style`	Either a character with the name of a pre-implemented style or a style object. See `style.plotSC` to learn about this format.

Returns a plot of one or multiple single-cases.

Juergen Wilbert

style.plotSC, describeSC, overlapSC

## Request the default plot of the data from Borckhardt (2014)
plot(Borckardt2014)

## Plot the three cases from Grosche (2011) and visualize the phase A trend
plot(Grosche2011, style = "grid", lines = "trendA")

## Request the local regression line for Georg from that data set and customize the plot
plot(Grosche2011$Georg, style = "sienna", ylim = c(0,NA),
       xlab = "Training session", ylab = "Words per minute",
       phase.names = c("Baseline", "Intervention"), 
       lines = list("loreg", lty = "solid", col = "black", lwd = 3))

## Plot a random MBD over three cases and mark interesting MTs
dat <- rSC(design = design_rSC(3))
plot(dat, marks = list(positions = list(c(2,4,5),c(1,2,3),c(7,8,9)), col = "blue",
       cex = 1.4), style = c("grid", "annotate", "tiny"))