stat QQLine
Parameters
The data to be displayed in this layer. If null, the default, the data is inherited from the plot data as specified in the call to letsPlot.
The geometry to display the Q-Q stat for this layer, default is Geom.qqLine()
, see Geom.
Position adjustment: positionIdentity
, positionStack()
, positionDodge()
, etc. see Position
default = true. false - do not show legend for this layer.
default = true. false - do not combine the layer aesthetic mappings with the plot shared mappings.
Result of the call to the samplingXxx()
function. To prevent any sampling for this layer pass value samplingNone
. For more info see sampling.html.
Y-axis value.
Transparency level of a layer. Understands numbers between 0 and 1.
Color of the geometry. For more info see: aesthetics.html#color-and-fill.
Type of the line. Accept codes or names (0 = "blank", 1 = "solid", 2 = "dashed", 3 = "dotted", 4 = "dotdash", 5 = "longdash", 6 = "twodash"), a hex string (up to 8 digits for dash-gap lengths), or a pattern offset to listOf(dash, gap, ...)
/ listOf(dash, gap, ...)
. For more info see: aesthetics.html#line-types.
Lines width.
default = "norm". Distribution function to use: "norm", "uniform", "t", "gamma", "exp", "chi2".
Additional parameters passed on to distribution function.
If
distribution
is "norm" thendParams
is a pair (mean, std) (default = listOf(0.0, 1.0)).If
distribution
is "uniform" thendParams
is a pair (a, b) (default = listOf(0.0, 1.0)).If
distribution
is "t" thendParams
is an integer number (d) (default = listOf(1)).If
distribution
is "gamma" thendParams
is a pair (alpha, beta) (default = listOf(1.0, 1.0)).If
distribution
is "exp" thendParams
is a float number (lambda) (default = listOf(1.0)).If
distribution
is "chi2" thendParams
is an integer number (k) (default = listOf(1)).
default = 0.25, 0.75. Pair of quantiles to use when fitting the Q-Q line.
default = "color" ("fill", "color", "paint_a", "paint_b", "paint_c"). Defines the color aesthetic for the geometry.
Set of aesthetic mappings. Aesthetic mappings describe the way that variables in the data are mapped to plot "aesthetics".