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
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class Objecttrait Matchableclass Any
- Self type
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Html.type
JFrame window.
JFrame window.
Attributes
- Companion
- object
- Supertypes
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class Objecttrait Matchableclass Any
- Known subtypes
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class CanvasWindowclass PlotGridWindow
Value members
Concrete methods
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.
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.
Value parameters
- data
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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
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the labels for each box plot.
Attributes
- Returns
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the plot canvas which can be added other shapes.
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.
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.
Value parameters
- z
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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 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.
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.
Value parameters
- levels
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the level values of contours.
- z
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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 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.
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.
Value parameters
- x
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the x coordinates of the data grid of z. Must be in ascending order.
- y
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the y coordinates of the data grid of z. Must be in ascending order.
- z
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the data matrix to create contour plot.
Attributes
- Returns
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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
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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
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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.
- merge
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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.
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
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a data matrix to be shown in pseudo heat map.
Attributes
Pseudo heat map plot.
Pseudo heat map plot.
Value parameters
- palette
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the color palette.
- x
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x coordinate of data matrix cells. Must be in ascending order.
- y
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y coordinate of data matrix cells. Must be in ascending order.
- z
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a data matrix to be shown in pseudo heat map.
Attributes
Pseudo heat map plot.
Pseudo heat map plot.
Value parameters
- columnLabels
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the labels for columns of data matrix.
- palette
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the color palette.
- rowLabels
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the labels for rows of data matrix.
- z
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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
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a sample set.
- k
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the number of bins.
Attributes
Histogram plot.
Histogram plot.
Value parameters
- breaks
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an array of size k+1 giving the breakpoints between histogram cells. Must be in ascending order.
- data
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a sample set.
Attributes
3D histogram plot.
3D histogram plot.
Value parameters
- data
-
a sample set.
- xbins
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the number of bins on x-axis.
- ybins
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the number of bins on y-axis.
Attributes
Line plot.
Line plot.
Value parameters
- color
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the color of line.
- data
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a n-by-2 or n-by-3 matrix that describes coordinates of points.
- mark
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the mark used to draw data points. The default value ' ' makes the point indistinguishable from the line on purpose.
- style
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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
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a n-by-2 or n-by-3 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 n-by-2 or n-by-3 matrix that describes coordinates of points.
- y
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labels of points.
Attributes
- Returns
-
the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
- x
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a n-by-2 or n-by-3 matrix that describes coordinates of points.
- y
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class label.
Attributes
- Returns
-
the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
- data
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the data frame.
- x
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the column as x-axis.
- y
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the column as y-axis.
Attributes
- Returns
-
the plot canvas which can be added other shapes.
Scatter plot.
Scatter plot.
Value parameters
- category
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the category column for coloring.
- data
-
the data frame.
- x
-
the column as x-axis.
- y
-
the column as y-axis.
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 x-axis.
- y
-
the column as y-axis.
- z
-
the column as z-axis.
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
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the column as x-axis.
- y
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the column as y-axis.
- z
-
the column as z-axis.
Attributes
- Returns
-
the plot canvas which can be added other shapes.
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.
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.
Value parameters
- x
-
a sample set.
Attributes
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.
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.
Value parameters
- d
-
a distribution.
- x
-
a sample set.
Attributes
QQ plot of two sample sets. The x-axis is the quantiles of x and the y-axis is the quantiles of y.
QQ plot of two sample sets. The x-axis is the quantiles of x and the y-axis is the quantiles of y.
Value parameters
- x
-
a sample set.
- y
-
a sample set.
Attributes
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.
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.
Value parameters
- d
-
a distribution.
- x
-
a sample set.
Attributes
QQ plot of two sample sets. The x-axis is the quantiles of x and the y-axis is the quantiles of y.
QQ plot of two sample sets. The x-axis is the quantiles of x and the y-axis 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
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The proportion of variance contained in each principal component.
Attributes
Scatterplot Matrix (SPLOM).
Scatterplot Matrix (SPLOM).
Value parameters
- data
-
a data frame.
- mark
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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
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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 z-axis 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 x-axis values of surface.
- y
-
the y-axis values of surface.
- z
-
the z-axis values of surface.
Attributes
- Returns
-
the plot canvas which can be added other shapes.
Text plot.
Text plot.
Value parameters
- coordinates
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a n-by-2 or n-by-3 matrix that are the coordinates of texts.
- texts
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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
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an m-by-2 array of which each row is the vertex indices of two end points of each edge.
- vertices
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a n-by-2 or n-by-3 array which are coordinates of n vertices.