swing

Type Members

case class CanvasWindow(frame: JFrame, canvas: Canvas) extends JWindow with Product with Serializable

Plot canvas window.
trait JWindow extends AnyRef

JFrame window.
case class PlotGridWindow(frame: JFrame, canvas: PlotGrid) extends JWindow with Product with Serializable

Plot grid window.

Value Members

object Html

HTML tag of Canvas and JComponent.
object JWindow
def boxplot(data: Array[Array[Double]], labels: Array[String]): Canvas

Box plot.
Box plot.
data
a data matrix of which each row will create a box plot.
labels
the labels for each box plot.
returns
the plot canvas which can be added other shapes.
def boxplot(data: Array[Double]*): Canvas

A box plot is a convenient way of graphically depicting groups of numerical data through their five-number summaries (the smallest observation (sample minimum), lower quartile (Q1), median (Q2), upper quartile (Q3), and largest observation (sample maximum).
A box plot is a convenient way of graphically depicting groups of numerical data through their five-number summaries (the smallest observation (sample minimum), lower quartile (Q1), median (Q2), upper quartile (Q3), and largest observation (sample maximum). A box plot may also indicate which observations, if any, might be considered outliers.
Box plots can be useful to display differences between populations without making any assumptions of the underlying statistical distribution: they are non-parametric. The spacings between the different parts of the box help indicate the degree of dispersion (spread) and skewness in the data, and identify outliers.
For a data set, we construct a boxplot in the following manner:
- Calculate the first q₁, the median q₂ and third quartile q₃. - Calculate the interquartile range (IQR) by subtracting the first quartile from the third quartile. (q₃ ? q₁)
- Construct a box above the number line bounded on the bottom by the first quartile (q₁) and on the top by the third quartile (q₃).
- Indicate where the median lies inside of the box with the presence of a line dividing the box at the median value.
- Any data observation which lies more than 1.5*IQR lower than the first quartile or 1.5IQR higher than the third quartile is considered an outlier. Indicate where the smallest value that is not an outlier is by connecting it to the box with a horizontal line or "whisker". Optionally, also mark the position of this value more clearly using a small vertical line. Likewise, connect the largest value that is not an outlier to the box by a "whisker" (and optionally mark it with another small vertical line).
- Indicate outliers by dots.
data
a data matrix of which each row will create a box plot.
returns
the plot canvas which can be added other shapes.
def contour(x: Array[Double], y: Array[Double], z: Array[Array[Double]]): Canvas

Contour plot.
Contour plot. A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs. The contour plot is an alternative to a 3-D surface plot.
x
the x coordinates of the data grid of z. Must be in ascending order.
y
the y coordinates of the data grid of z. Must be in ascending order.
z
the data matrix to create contour plot.
returns
the plot canvas which can be added other shapes.
def contour(z: Array[Array[Double]], levels: Array[Double]): Canvas

Contour plot.
Contour plot. A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs. The contour plot is an alternative to a 3-D surface plot.
z
the data matrix to create contour plot.
levels
the level values of contours.
returns
the plot canvas which can be added other shapes.
def contour(z: Array[Array[Double]]): Canvas

Contour plot.
Contour plot. A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs. The contour plot is an alternative to a 3-D surface plot.
z
the data matrix to create contour plot.
returns
the plot canvas which can be added other shapes.
def dendrogram(merge: Array[Array[Int]], height: Array[Double]): Canvas

A dendrogram is a tree diagram to illustrate the arrangement of the clusters produced by hierarchical clustering.
A dendrogram is a tree diagram to illustrate the arrangement of the clusters produced by hierarchical clustering.
merge
an n-1 by 2 matrix of which row i describes the merging of clusters at step i of the clustering. If an element j in the row is less than n, then observation j was merged at this stage. If j ≥ n then the merge was with the cluster formed at the (earlier) stage j-n of the algorithm.
height
a set of n-1 non-decreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration.
def dendrogram(hc: HierarchicalClustering): Canvas

A dendrogram is a tree diagram to illustrate the arrangement of the clusters produced by hierarchical clustering.
A dendrogram is a tree diagram to illustrate the arrangement of the clusters produced by hierarchical clustering.
hc
hierarchical clustering object.
def grid(data: Array[Array[Array[Double]]]): Canvas

2D grid plot.
2D grid plot.
data
an m x n x 2 array which are coordinates of m x n grid.
def heatmap(rowLabels: Array[String], columnLabels: Array[String], z: Array[Array[Double]], palette: Array[Color]): Canvas

Pseudo heat map plot.
Pseudo heat map plot.
rowLabels
the labels for rows of data matrix.
columnLabels
the labels for columns of data matrix.
z
a data matrix to be shown in pseudo heat map.
palette
the color palette.
def heatmap(x: Array[Double], y: Array[Double], z: Array[Array[Double]], palette: Array[Color]): Canvas

Pseudo heat map plot.
Pseudo heat map plot.
x
x coordinate of data matrix cells. Must be in ascending order.
y
y coordinate of data matrix cells. Must be in ascending order.
z
a data matrix to be shown in pseudo heat map.
palette
the color palette.
def heatmap(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas

Pseudo heat map plot.
Pseudo heat map plot.
z
a data matrix to be shown in pseudo heat map.
palette
the color palette.
def hexmap(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas

Heat map with hex shape.
Heat map with hex shape.
z
a data matrix to be shown in pseudo heat map.
palette
the color palette.
def hist(data: Array[Double], breaks: Array[Double], prob: Boolean, color: Color): Canvas

Histogram plot.
Histogram plot.
data
a sample set.
breaks
an array of size k+1 giving the breakpoints between histogram cells. Must be in ascending order.
def hist(data: Array[Double], k: Int = 10, prob: Boolean = false, color: Color = Color.BLUE): Canvas

Histogram plot.
Histogram plot.
data
a sample set.
k
the number of bins.
def hist3(data: Array[Array[Double]], xbins: Int = 10, ybins: Int = 10, prob: Boolean = false, palette: Array[Color] = Palette.jet(16)): Canvas

3D histogram plot.
3D histogram plot.
data
a sample set.
xbins
the number of bins on x-axis.
ybins
the number of bins on y-axis.
def line(data: Array[Array[Double]], style: Style = Line.Style.SOLID, color: Color = Color.BLACK, mark: Char = ' ', label: String = null): Canvas

Line plot.
Line plot.
data
a n-by-2 or n-by-3 matrix that describes coordinates of points.
style
the stroke style of line.
color
the color of line.
mark
the mark used to draw data points. The default value ' ' makes the point indistinguishable from the line on purpose.
returns
the plot canvas which can be added other shapes.
def plot(data: DataFrame, x: String, y: String, z: String, category: String, mark: Char): Canvas

Scatter plot.
Scatter plot.
data
the data frame.
x
the column as x-axis.
y
the column as y-axis.
z
the column as z-axis.
category
the category column for coloring.
returns
the plot canvas which can be added other shapes.
def plot(data: DataFrame, x: String, y: String, z: String, mark: Char, color: Color): Canvas

Scatter plot.
Scatter plot.
data
the data frame.
x
the column as x-axis.
y
the column as y-axis.
z
the column as z-axis.
returns
the plot canvas which can be added other shapes.
def plot(data: DataFrame, x: String, y: String, category: String, mark: Char): Canvas

Scatter plot.
Scatter plot.
data
the data frame.
x
the column as x-axis.
y
the column as y-axis.
category
the category column for coloring.
returns
the plot canvas which can be added other shapes.
def plot(data: DataFrame, x: String, y: String, mark: Char, color: Color): Canvas

Scatter plot.
Scatter plot.
data
the data frame.
x
the column as x-axis.
y
the column as y-axis.
returns
the plot canvas which can be added other shapes.
def plot(x: Array[Array[Double]], y: Array[Int], mark: Char): Canvas

Scatter plot.
Scatter plot.
x
a n-by-2 or n-by-3 matrix that describes coordinates of points.
y
class label.
returns
the plot canvas which can be added other shapes.
def plot(x: Array[Array[Double]], y: Array[String], mark: Char): Canvas

Scatter plot.
Scatter plot.
x
a n-by-2 or n-by-3 matrix that describes coordinates of points.
y
labels of points.
returns
the plot canvas which can be added other shapes.
def plot(x: Array[Array[Double]], mark: Char = '*', color: Color = Color.BLACK): Canvas

Scatter plot.
Scatter plot.
x
a n-by-2 or n-by-3 matrix that describes coordinates of points.
mark
the mark used to draw points.
- . : dot
- + : +
- - : -
- | : |
- * : star
- x : x
- o : circle
- O : large circle
- @ : solid circle
- # : large solid circle
- s : square
- S : large square
- q : solid square
- Q : large solid square
- others : dot
color
the color used to draw points.
returns
the plot canvas which can be added other shapes.
def qqplot(x: Array[Int], y: Array[Int]): Canvas

QQ plot of two sample sets.
QQ plot of two sample sets. The x-axis is the quantiles of x and the y-axis is the quantiles of y.
x
a sample set.
y
a sample set.
def qqplot(x: Array[Int], d: DiscreteDistribution): Canvas

QQ plot of samples to given distribution.
QQ plot of samples to given distribution. The x-axis is the quantiles of x and the y-axis is the quantiles of given distribution.
x
a sample set.
d
a distribution.
def qqplot(x: Array[Double], y: Array[Double]): Canvas

QQ plot of two sample sets.
QQ plot of two sample sets. The x-axis is the quantiles of x and the y-axis is the quantiles of y.
x
a sample set.
y
a sample set.
def qqplot(x: Array[Double], d: Distribution): Canvas

QQ plot of samples to given distribution.
QQ plot of samples to given distribution. The x-axis is the quantiles of x and the y-axis is the quantiles of given distribution.
x
a sample set.
d
a distribution.
def qqplot(x: Array[Double]): Canvas

QQ plot of samples to standard normal distribution.
QQ plot of samples to standard normal distribution. The x-axis is the quantiles of x and the y-axis is the quantiles of normal distribution.
x
a sample set.
def screeplot(pca: PCA): Canvas

The scree plot is a useful visual aid for determining an appropriate number of principal components.
The scree plot is a useful visual aid for determining an appropriate number of principal components. The scree plot graphs the eigenvalue against the component number. To determine the appropriate number of components, we look for an "elbow" in the scree plot. The component number is taken to be the point at which the remaining eigenvalues are relatively small and all about the same size.
pca
principal component analysis object.
def splom(data: DataFrame, mark: Char, category: String): PlotGrid

Scatterplot Matrix (SPLOM).
Scatterplot Matrix (SPLOM).
data
an attribute frame.
mark
the legend for all classes.
category
the category column for coloring.
returns
the plot panel.
def splom(data: DataFrame, mark: Char, color: Color): PlotGrid

Scatterplot Matrix (SPLOM).
Scatterplot Matrix (SPLOM).
data
a data frame.
mark
the legend for all classes.
returns
the plot panel.
def spy(matrix: SparseMatrix, k: Int = 1): Canvas

Visualize sparsity pattern.
Visualize sparsity pattern.
matrix
a sparse matrix.
def staircase(data: Array[Array[Double]], color: Color = Color.BLACK, label: String = null): Canvas

Create a plot canvas with the staircase line plot.
Create a plot canvas with the staircase line plot.
data
a n x 2 or n x 3 matrix that describes coordinates of points.
def surface(x: Array[Double], y: Array[Double], z: Array[Array[Double]], palette: Array[Color]): Canvas

3D surface plot.
3D surface plot.
x
the x-axis values of surface.
y
the y-axis values of surface.
z
the z-axis values of surface.
palette
the color palette.
returns
the plot canvas which can be added other shapes.
def surface(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas

3D surface plot.
3D surface plot.
z
the z-axis values of surface.
palette
the color palette.
returns
the plot canvas which can be added other shapes.
def text(texts: Array[String], coordinates: Array[Array[Double]]): Canvas

Text plot.
Text plot.
texts
the texts.
coordinates
a n-by-2 or n-by-3 matrix that are the coordinates of texts.
def wireframe(vertices: Array[Array[Double]], edges: Array[Array[Int]]): Canvas

Wire frame plot.
Wire frame plot. A wire frame model specifies each edge of the physical object where two mathematically continuous smooth surfaces meet, or by connecting an object's constituent vertices using straight lines or curves.
vertices
a n-by-2 or n-by-3 array which are coordinates of n vertices.
edges
an m-by-2 array of which each row is the vertex indices of two end points of each edge.

package swing

Type Members

case class CanvasWindow(frame: JFrame, canvas: Canvas) extends JWindow with Product with Serializable

trait JWindow extends AnyRef

case class PlotGridWindow(frame: JFrame, canvas: PlotGrid) extends JWindow with Product with Serializable

Value Members

object Html

object JWindow

def boxplot(data: Array[Array[Double]], labels: Array[String]): Canvas

def boxplot(data: Array[Double]*): Canvas

def contour(x: Array[Double], y: Array[Double], z: Array[Array[Double]]): Canvas

def contour(z: Array[Array[Double]], levels: Array[Double]): Canvas

def contour(z: Array[Array[Double]]): Canvas

def dendrogram(merge: Array[Array[Int]], height: Array[Double]): Canvas

def dendrogram(hc: HierarchicalClustering): Canvas

def grid(data: Array[Array[Array[Double]]]): Canvas

def heatmap(rowLabels: Array[String], columnLabels: Array[String], z: Array[Array[Double]], palette: Array[Color]): Canvas

def heatmap(x: Array[Double], y: Array[Double], z: Array[Array[Double]], palette: Array[Color]): Canvas

def heatmap(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas

def hexmap(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas

def hist(data: Array[Double], breaks: Array[Double], prob: Boolean, color: Color): Canvas

def hist(data: Array[Double], k: Int = 10, prob: Boolean = false, color: Color = Color.BLUE): Canvas

def hist3(data: Array[Array[Double]], xbins: Int = 10, ybins: Int = 10, prob: Boolean = false, palette: Array[Color] = Palette.jet(16)): Canvas

def line(data: Array[Array[Double]], style: Style = Line.Style.SOLID, color: Color = Color.BLACK, mark: Char = ' ', label: String = null): Canvas

def plot(data: DataFrame, x: String, y: String, z: String, category: String, mark: Char): Canvas

def plot(data: DataFrame, x: String, y: String, z: String, mark: Char, color: Color): Canvas

def plot(data: DataFrame, x: String, y: String, category: String, mark: Char): Canvas

def plot(data: DataFrame, x: String, y: String, mark: Char, color: Color): Canvas

def plot(x: Array[Array[Double]], y: Array[Int], mark: Char): Canvas

def plot(x: Array[Array[Double]], y: Array[String], mark: Char): Canvas

def plot(x: Array[Array[Double]], mark: Char = '*', color: Color = Color.BLACK): Canvas

def qqplot(x: Array[Int], y: Array[Int]): Canvas

def qqplot(x: Array[Int], d: DiscreteDistribution): Canvas

def qqplot(x: Array[Double], y: Array[Double]): Canvas

def qqplot(x: Array[Double], d: Distribution): Canvas

def qqplot(x: Array[Double]): Canvas

def screeplot(pca: PCA): Canvas

def splom(data: DataFrame, mark: Char, category: String): PlotGrid

def splom(data: DataFrame, mark: Char, color: Color): PlotGrid

def spy(matrix: SparseMatrix, k: Int = 1): Canvas

def staircase(data: Array[Array[Double]], color: Color = Color.BLACK, label: String = null): Canvas

def surface(x: Array[Double], y: Array[Double], z: Array[Array[Double]], palette: Array[Color]): Canvas

def surface(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas

def text(texts: Array[String], coordinates: Array[Array[Double]]): Canvas

def wireframe(vertices: Array[Array[Double]], edges: Array[Array[Int]]): Canvas

Inherited from AnyRef

Inherited from Any

Ungrouped