plotSlice
Syntax
Description
plotSlice(
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.mdl
)
plotSlice(___,
specifies additional options using one or more namevalue arguments. For example, you can
specify the predictor variables for a histogram.Name=Value
)
returns a
handle for the h
= plotSlice(___)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. The 95% confidence intervals for the predictions are represented by blue shaded regions. However, in the above plots they are very thin. In the left plot, the Weight
predictor is fixed at 3376
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
.
In the Class menu, select 5
then click Apply.
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
.
Specify Predictor Data for Slice Plot
Load the carbig
sample data set.
load carbig
The variables Horsepower
and Weight
contain data for the horsepower and weight of each car. The variable Cylinders
contains data for the number of cylinders in each car engine.
Create a table from the data in Horsepower
, Weight
, and Cylinders
by using the table
function. Fit a multinomial regression model using Horsepower
and Weight
as predictors and Cylinders
as the response.
tbl = table(Horsepower, Weight, Cylinders, VariableNames=["Horsepower","Weight","Cylinders"]); mdl = fitmnr(tbl,"Cylinders");
mdl
is a MultinomialRegression
object that contains the results of fitting a multinomial regression model to the data.
Generate new predictor data.
Horsepower_new = 100:200; Weight_new = 3000:10:4000; Xnew = [Horsepower_new' Weight_new'];
Create a slice plot for mdl
using the new predictor data for car horsepower and weight.
plotSlice(mdl,Xnew)
The left plot shows Cylinders
as a function of the Horsepower
with Weight
fixed at 3500
. The right plot shows Cylinders
as a function of Weight
with Horsepower
fixed at 150
. By default, plotSlice
displays the probability for the first response category in ClassNames
. You can select a different response category by selecting a different class from the Class menu. The Confidence Interval Type options indicate that the confidence intervals are simultaneous and for the fitted responses. For more information, see Tips.
Plot Overlaid 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 = 2×1 cell
{'Displacement'}
{'Weight' }
The output shows that the first predictor in the vector of predictor names corresponds to Displacement
.
Generate overlaid 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.
Xnew
— New predictor input values
table  numeric matrix
New predictor input values, specified as a table or numeric matrix. If
Xnew
is a table, its columns must have the same names as the
predictors in mdl.VariableNames
. If Xnew
is a
matrix, its columns must correspond to the predictors in
mdl.VariableNames
. Xnew
cannot contain
categorical predictor values not used during training.
Data Types: single
 double
 table
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 namevalue
argument. 
"histogram"  Plots overlaid 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 namevalue 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 namevalue argument. 
Example: "histogram"
Data Types: char
 string
fig
— Target figure
Figure
object
Since R2024a
Target figure, specified as a Figure
object. You must create
fig
using the uifigure
function. If you do not specify fig
, then
plotSlice
creates a new figure.
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue 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.
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
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 threeelement 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 from0
toF
. 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.
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 threeelement 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 from0
toF
. 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 

"."  Dashdotted 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
returnsh
as aFigure
object. For more information aboutFigure
properties, see Figure Properties.When
plotType
is"histogram"
,plotSlice
returnsh
as aHistogram
array with entries for each response variable category. For more information aboutHistogram
properties, see Histogram Properties.When
plotType
is"stackedhist"
,plotSlice
returnsh
as aBar
array with entries for each response variable category. For more information aboutBar
properties, see Bar Properties.
Version History
Introduced in R2023aR2024a: Specify a target figure
Specify a target figure for the plot by using the fig
input
argument.
See Also
MultinomialRegression
 fitmnr
 Figure Properties  Histogram Properties  Bar Properties
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