smile.plot.swing
Swing based data visualization.
Attributes
Members list
Type members
Classlikes
HTML <img>
tag of Canvas and JComponent.
HTML <img>
tag of Canvas and JComponent.
Attributes
 Supertypes

class Objecttrait Matchableclass Any
 Self type

Html.type
JFrame window.
JFrame window.
Attributes
 Companion
 object
 Supertypes

class Objecttrait Matchableclass Any
 Known subtypes

class CanvasWindowclass PlotGridWindow
Value members
Concrete methods
A box plot is a convenient way of graphically depicting groups of numerical data through their fivenumber 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.
A box plot is a convenient way of graphically depicting groups of numerical data through their fivenumber 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 nonparametric. 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 q_{1}, the median q_{2} and third quartile q_{3}.
 Calculate the interquartile range (IQR) by subtracting the first quartile from the third quartile. (q_{3} ? q_{1})
 Construct a box above the number line bounded on the bottom by the first quartile (q_{1}) and on the top by the third quartile (q_{3}).
 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.
Value parameters
 data

a data matrix of which each row will create a box plot.
Attributes
 Returns

the plot canvas which can be added other shapes.
Box plot.
Box plot.
Value parameters
 data

a data matrix of which each row will create a box plot.
 labels

the labels for each box plot.
Attributes
 Returns

the plot canvas which can be added other shapes.
Contour plot. A contour plot is a graphical technique for representing a 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D surface plot.
Contour plot. A contour plot is a graphical technique for representing a 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D surface plot.
Value parameters
 z

the data matrix to create contour plot.
Attributes
 Returns

the plot canvas which can be added other shapes.
Contour plot. A contour plot is a graphical technique for representing a 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D surface plot.
Contour plot. A contour plot is a graphical technique for representing a 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D surface plot.
Value parameters
 levels

the level values of contours.
 z

the data matrix to create contour plot.
Attributes
 Returns

the plot canvas which can be added other shapes.
Contour plot. A contour plot is a graphical technique for representing a 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D surface plot.
Contour plot. A contour plot is a graphical technique for representing a 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D surface plot.
Value parameters
 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.
Attributes
 Returns

the plot canvas which can be added other shapes.
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.
Value parameters
 hc

hierarchical clustering object.
Attributes
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.
Value parameters
 height

a set of n1 nondecreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration.
 merge

an n1 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 jn of the algorithm.
Attributes
2D grid plot.
2D grid plot.
Value parameters
 data

an m x n x 2 array which are coordinates of m x n grid.
Attributes
Pseudo heat map plot.
Pseudo heat map plot.
Value parameters
 palette

the color palette.
 z

a data matrix to be shown in pseudo heat map.
Attributes
Pseudo heat map plot.
Pseudo heat map plot.
Value parameters
 palette

the color palette.
 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.
Attributes
Pseudo heat map plot.
Pseudo heat map plot.
Value parameters
 columnLabels

the labels for columns of data matrix.
 palette

the color palette.
 rowLabels

the labels for rows of data matrix.
 z

a data matrix to be shown in pseudo heat map.
Attributes
Heat map with hex shape.
Heat map with hex shape.
Value parameters
 palette

the color palette.
 z

a data matrix to be shown in pseudo heat map.
Attributes
Histogram plot.
Histogram plot.
Value parameters
 data

a sample set.
 k

the number of bins.
Attributes
Histogram plot.
Histogram plot.
Value parameters
 breaks

an array of size k+1 giving the breakpoints between histogram cells. Must be in ascending order.
 data

a sample set.
Attributes
3D histogram plot.
3D histogram plot.
Value parameters
 data

a sample set.
 xbins

the number of bins on xaxis.
 ybins

the number of bins on yaxis.
Attributes
Line plot.
Line plot.
Value parameters
 color

the color of line.
 data

a nby2 or nby3 matrix that describes coordinates of points.
 mark

the mark used to draw data points. The default value ' ' makes the point indistinguishable from the line on purpose.
 style

the stroke style of line.
Attributes
 Returns

the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
 color

the color used to draw 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
 x

a nby2 or nby3 matrix that describes coordinates of points.
Attributes
 Returns

the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
 x

a nby2 or nby3 matrix that describes coordinates of points.
 y

labels of points.
Attributes
 Returns

the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
 x

a nby2 or nby3 matrix that describes coordinates of points.
 y

class label.
Attributes
 Returns

the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
 data

the data frame.
 x

the column as xaxis.
 y

the column as yaxis.
Attributes
 Returns

the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
 category

the category column for coloring.
 data

the data frame.
 x

the column as xaxis.
 y

the column as yaxis.
Attributes
 Returns

the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
 data

the data frame.
 x

the column as xaxis.
 y

the column as yaxis.
 z

the column as zaxis.
Attributes
 Returns

the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
 category

the category column for coloring.
 data

the data frame.
 x

the column as xaxis.
 y

the column as yaxis.
 z

the column as zaxis.
Attributes
 Returns

the plot canvas which can be added other shapes.
QQ plot of samples to standard normal distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of normal distribution.
QQ plot of samples to standard normal distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of normal distribution.
Value parameters
 x

a sample set.
Attributes
QQ plot of samples to given distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of given distribution.
QQ plot of samples to given distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of given distribution.
Value parameters
 d

a distribution.
 x

a sample set.
Attributes
QQ plot of two sample sets. The xaxis is the quantiles of x and the yaxis is the quantiles of y.
QQ plot of two sample sets. The xaxis is the quantiles of x and the yaxis is the quantiles of y.
Value parameters
 x

a sample set.
 y

a sample set.
Attributes
QQ plot of samples to given distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of given distribution.
QQ plot of samples to given distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of given distribution.
Value parameters
 d

a distribution.
 x

a sample set.
Attributes
QQ plot of two sample sets. The xaxis is the quantiles of x and the yaxis is the quantiles of y.
QQ plot of two sample sets. The xaxis is the quantiles of x and the yaxis is the quantiles of y.
Value parameters
 x

a sample set.
 y

a sample set.
Attributes
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.
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.
Value parameters
 varianceProportion

The proportion of variance contained in each principal component.
Attributes
Scatterplot Matrix (SPLOM).
Scatterplot Matrix (SPLOM).
Value parameters
 data

a data frame.
 mark

the legend for all classes.
Attributes
 Returns

the plot panel.
Scatterplot Matrix (SPLOM).
Scatterplot Matrix (SPLOM).
Value parameters
 category

the category column for coloring.
 data

an attribute frame.
 mark

the legend for all classes.
Attributes
 Returns

the plot panel.
Visualize sparsity pattern.
Visualize sparsity pattern.
Value parameters
 matrix

a sparse matrix.
Attributes
Create a plot canvas with the staircase line plot.
Create a plot canvas with the staircase line plot.
Value parameters
 data

a n x 2 or n x 3 matrix that describes coordinates of points.
Attributes
3D surface plot.
3D surface plot.
Value parameters
 palette

the color palette.
 z

the zaxis values of surface.
Attributes
 Returns

the plot canvas which can be added other shapes.
3D surface plot.
3D surface plot.
Value parameters
 palette

the color palette.
 x

the xaxis values of surface.
 y

the yaxis values of surface.
 z

the zaxis values of surface.
Attributes
 Returns

the plot canvas which can be added other shapes.
Text plot.
Text plot.
Value parameters
 coordinates

a nby2 or nby3 matrix that are the coordinates of texts.
 texts

the texts.
Attributes
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.
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.
Value parameters
 edges

an mby2 array of which each row is the vertex indices of two end points of each edge.
 vertices

a nby2 or nby3 array which are coordinates of n vertices.