plotSlice

Plot of slices through fitted multinomial regression surface

Since R2023a

Syntax

plotSlice(mdl)

plotSlice(mdl,plotType)

plotSlice(ax,___)

plotSlice(___,Name=Value)

h = plotSlice(___)

Description

plotSlice(mdl) generates an interactive figure containing a plot for each predictor variable. Each plot shows the fitted category probability of the first response category as a function of its corresponding predictor variable, with the other predictor variables fixed at their sample means. You can customize the figure by selecting a different response class, changing the values of the fixed predictors, and selecting a subset of predictor variables.

plotSlice(mdl,plotType) specifies the type of plot to generate: an interactive figure, a histogram, or a stacked histogram.

plotSlice(ax,___) plots on the axes specified by ax instead of the current axes, using any of the input argument combinations in the previous syntaxes. Note that plotType must be "histogram" or "stackedhist".

plotSlice(___,Name=Value) specifies additional options using one or more name-value arguments. For example, you can specify the predictor variables for a histogram.

h = plotSlice(___) returns a handle for the Figure object, Histogram array, or Bar array. Use h to modify the properties of the figure, histogram, or stacked histogram after you create the plot. For a list of properties, see Figure Properties, Histogram Properties, or Bar Properties.

Examples

Plot Probabilities

Load the carbig sample data set.

load carbig

The variables Displacement, Weight, and Cylinders contain data for car engine displacement, weight, and number of engine cylinders, respectively.

Fit a multinomial regression model using Displacement and Weight as predictor variables and Cylinders as the response data. Display the vector of response category names.

mdl = fitmnr([Displacement,Weight],Cylinders,PredictorNames=["Displacement","Weight"]);
mdl.ClassNames

ans = 5×1

     3
     4
     5
     6
     8

The output shows that the first category in the vector of response category names corresponds to cars with three engine cylinders.

Generate plots for the probability of a car having three engine cylinders as a function of each predictor variable.

plotSlice(mdl)

By default, plotSlice displays the probability for the first response category in ClassNames. The two plots for the predictor variables share a vertical axis. In the left plot, the Weight predictor is fixed at 3376.5 whereas the Displacement predictor varies. In the right plot, Displacement is fixed at 261.5 whereas Weight varies. The probability of a car having three cylinders is small for all values of Weight when Displacement is 261.5.

To change the response category to cars with five engine cylinders, click Class then click Select. In the dialog box, click 5 then click OK.

plotSlice updates the plots to display the probability that a car has five engine cylinders.

The right plot shows that the probability of a car having five cylinders is also small for all values of Weight when Displacement is 261.5.

To fix Displacement at 150 in the right plot, enter 150 in the Displacement box.

The shared vertical axis automatically rescales to accommodate larger values in the right plot. The right plot shows that when Displacement is fixed at 150, the probability of a car having five engine cylinders peaks when Weight is approximately 4000.

Plot Overlayed Histogram

Load the carbig sample data set.

load carbig

The variables Displacement, Weight, and Cylinders contain data for car engine displacement, weight, and number of engine cylinders, respectively.

Fit a multinomial regression model using Displacement and Weight as predictor variables and Cylinders as the response data. Display the vector of response category names.

mdl = fitmnr([Displacement,Weight],Cylinders,PredictorNames=["Displacement","Weight"]);
mdl.ClassNames

ans = 5×1

     3
     4
     5
     6
     8

The output shows that the response categories correspond to cars with three, four, five, six, and eight engine cylinders.

Display the vector of predictor names.

mdl.PredictorNames

ans = 2x1 cell
    {'Displacement'}
    {'Weight'      }

The output shows that the first predictor in the vector of predictor names corresponds to Displacement.

Generate overlayed histograms of the estimated probabilities for each response category. By default, plotSlice varies the first predictor Displacement and fixes the value of the remaining predictor Weight at its training data mean.

plotSlice(mdl,"histogram")
lgd = legend;
title(lgd,"Number of Cylinders")

The output shows that the probability for each response category peaks in a different interval of Displacement. Cars with higher engine displacement have a higher probability of having more cylinders.

Fix the value of Displacement at the training data mean, and then plot histograms of the response category probabilities for varying Weight.

plotSlice(mdl,"histogram",PredictorToVary="Weight")
lgd = legend;
title(lgd,"Number of Cylinders")

The output shows that a car is most likely to have an engine with six cylinders for all values of Weight. Together, the plots show that the probability of a car being in each category depends on Displacement more than Weight.

Input Arguments

mdl — Multinomial regression model object

MultinomialRegression model object

Multinomial regression model object, specified as a MultinomialRegression model object created with the fitmnr function.

plotType — Type of plot to generate

"interactive" (default) | "histogram" | "stackedhist"

Type of plot to generate, specified as one of the following options.

Option	Description
"interactive" (default)	Generates an interactive figure containing a plot for each predictor variable. The default plot shows the fitted category probability of the first response category as a function of its corresponding predictor variable, with the other predictor variables fixed at their sample means. To select a different response category, choose Select from the Class menu and then select a response category. To view plots corresponding to a different subset of predictor variables, choose Select from the Predictors menu and then select a subset of predictors. Alternatively, you can select the response category to plot by using the ClassToPlot name-value argument.
"histogram"	Plots overlayed histograms of the probabilities for each response category. The default plot shows the change in the response categories as the first predictor varies, with the other predictor variables fixed at their sample means. To vary a different predictor variable, use the PredictorToVary name-value argument.
"stackedhist"	Plots stacked histograms of the probabilities for each response category. The default plot shows the change in the response categories as the first predictor varies, with the other predictor variables fixed at their sample means. To vary a different predictor, use the PredictorToVary name-value argument.

Example: "histogram"

Data Types: char | string

ax — Target axes

Axes object

Target axes, specified as an Axes object. You can specify ax only when plotType is "histogram" or "stackedhist". If you do not specify the axes, then plotSlice uses the current axes (gca).

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: plotSlice(mdl,ClassToPlot="setosa") generates an interactive plot of the setosa response category.

Model Data

ClassToPlot — Response category to plot

response category name

Response category to plot, specified as one of the response category names in mdl.ClassNames. The default value for ClassToPlot is the first entry in mdl.ClassNames. This argument is valid only when plotType is "interactive".

Example: ClassToPlot="setosa"

Data Types: single | double | logical | char | string | cell

PredictorToVary — Predictor to vary

predictor variable name

Predictor to vary, specified as one of the predictor variable names in mdl.PredictorNames. The default value for PredictorToVary is the first entry in mdl.PredictorNames. This argument is valid only when plotType is "histogram" or "stackedhist".

Example: PredictorToVary="x1"

Data Types: char | string

Histogram Graphics

FaceColor — Histogram bar color

"auto" (default) | "none" | RGB triplet | hexadecimal color code | color name

Histogram bar color, specified as one of these values:

• "none" — Bars are not filled.

• "auto" — The histogram bar color is selected automatically (default).

• RGB triplet, hexadecimal color code, or color name — Bars are filled with the specified color.

RGB triplets and hexadecimal color codes are useful for specifying custom colors.

• An RGB triplet is a three-element row vector whose elements specify the intensities of the red, green, and blue components of the color. The intensities must be in the range [0,1]; for example, [0.4 0.6 0.7].

• A hexadecimal color code is a character vector or a string scalar that starts with a hash symbol (#) followed by three or six hexadecimal digits, which can range from 0 to F. The values are not case sensitive. Thus, the color codes "#FF8800", "#ff8800", "#F80", and "#f80" are equivalent.

Alternatively, you can specify some common colors by name. This table lists the named color options, the equivalent RGB triplets, and hexadecimal color codes.

Color Name	Short Name	RGB Triplet	Hexadecimal Color Code	Appearance
"red"	"r"	[1 0 0]	"#FF0000"
"green"	"g"	[0 1 0]	"#00FF00"
"blue"	"b"	[0 0 1]	"#0000FF"
"cyan"	"c"	[0 1 1]	"#00FFFF"
"magenta"	"m"	[1 0 1]	"#FF00FF"
"yellow"	"y"	[1 1 0]	"#FFFF00"
"black"	"k"	[0 0 0]	"#000000"
"white"	"w"	[1 1 1]	"#FFFFFF"

Here are the RGB triplets and hexadecimal color codes for the default colors MATLAB^® uses in many types of plots.

RGB Triplet	Hexadecimal Color Code	Appearance
[0 0.4470 0.7410]	"#0072BD"
[0.8500 0.3250 0.0980]	"#D95319"
[0.9290 0.6940 0.1250]	"#EDB120"
[0.4940 0.1840 0.5560]	"#7E2F8E"
[0.4660 0.6740 0.1880]	"#77AC30"
[0.3010 0.7450 0.9330]	"#4DBEEE"
[0.6350 0.0780 0.1840]	"#A2142F"

Example: FaceColor="g"

Data Types: single | double | char | string

FaceAlpha — Transparency of histogram bars

0.6 (default) | scalar value between 0 and 1 inclusive

Transparency of histogram bars, specified as a scalar value between 0 and 1 inclusive. plotSlice uses the same transparency for all the bars of the histogram. A value of 1 means fully opaque and 0 means completely transparent (invisible).

Example: FaceAlpha=1

Data Types: single | double

LineWidth — Width of bar outlines

0.5 (default) | positive value

Width of bar outlines, specified as a positive value in point units. One point equals 1/72 inch.

Example: LineWidth=1.5

Data Types: single | double

Note

The graphics properties for histograms listed here are only a subset. For a complete list, see Histogram Properties.

Stacked Histogram Graphics

EdgeColor — Outline color

"flat" | RGB triplet | hexadecimal color code | "r" | "g" | "b" | ...

Outline color, specified as "flat", an RGB triplet, a hexadecimal color code, a color name, or a short name. If the plot has 150 bars or fewer, the default value is [0 0 0], which corresponds to black. If the plot has more than 150 adjacent bars, the default value is "none".

For a custom color, specify an RGB triplet or a hexadecimal color code.

• An RGB triplet is a three-element row vector whose elements specify the intensities of the red, green, and blue components of the color. The intensities must be in the range [0,1], for example, [0.4 0.6 0.7].

• A hexadecimal color code is a character vector or a string scalar that starts with a hash symbol (#) followed by three or six hexadecimal digits, which can range from 0 to F. The values are not case sensitive. Therefore, the color codes "#FF8800", "#ff8800", "#F80", and "#f80" are equivalent.

Alternatively, you can specify some common colors by name. This table lists the named color options, the equivalent RGB triplets, and hexadecimal color codes.

Color Name	Short Name	RGB Triplet	Hexadecimal Color Code	Appearance
"red"	"r"	[1 0 0]	"#FF0000"
"green"	"g"	[0 1 0]	"#00FF00"
"blue"	"b"	[0 0 1]	"#0000FF"
"cyan"	"c"	[0 1 1]	"#00FFFF"
"magenta"	"m"	[1 0 1]	"#FF00FF"
"yellow"	"y"	[1 1 0]	"#FFFF00"
"black"	"k"	[0 0 0]	"#000000"
"white"	"w"	[1 1 1]	"#FFFFFF"
"none"	Not applicable	Not applicable	Not applicable	No color

Here are the RGB triplets and hexadecimal color codes for the default colors MATLAB uses in many types of plots.

RGB Triplet	Hexadecimal Color Code	Appearance
[0 0.4470 0.7410]	"#0072BD"
[0.8500 0.3250 0.0980]	"#D95319"
[0.9290 0.6940 0.1250]	"#EDB120"
[0.4940 0.1840 0.5560]	"#7E2F8E"
[0.4660 0.6740 0.1880]	"#77AC30"
[0.3010 0.7450 0.9330]	"#4DBEEE"
[0.6350 0.0780 0.1840]	"#A2142F"

Example: EdgeColor="red"

Example: EdgeColor=[0.6350 0.0780 0.1840]

Example: EdgeColor="#D2F9A7"

Data Types: single | double | char | string

LineStyle — Line style

"-" (default) | "--" | ":" | "-." | "none"

Line style, specified as one of the options listed in this table.

Line Style	Description	Resulting Line
"-"	Solid line
"--"	Dashed line
":"	Dotted line
"-."	Dash-dotted line
"none"	No line	No line

Example: LineStyle="--"

Data Types: char | string

Note

The graphics properties for stacked histograms listed here are only a subset. For a complete list, see Bar Properties.

Output Arguments

h — Handle for plotted objects

Figure object | Histogram array | Bar array

Handle for the plotted objects, returned as a Figure object, Histogram array, or Bar array.

• When the input argument plotType is "interactive", plotSlice returns h as a Figure object. For more information about Figure properties, see Figure Properties.

• When plotType is "histogram", plotSlice returns h as a Histogram array with entries for each response variable category. For more information about Histogram properties, see Histogram Properties.

• When plotType is "stackedhist", plotSlice returns h as a Bar array with entries for each response variable category. For more information about Bar properties, see Bar Properties.

Version History

Introduced in R2023a