plotResiduals
Syntax
Description
plotResiduals(
generates a probability
density plot of the deviance residuals for the multinomial regression model object
mdl
)mdl
.
plotResiduals(
plots on the
axes specified by ax
,___)ax
instead of the current axes, using any of the
input argument combinations in previous syntaxes.
plotResiduals(___,
specifies additional options using one or more namevalue arguments. For example, you can
specify the type of residuals to plot and the colors of the plotted objects.Name=Value
)
returns
graphics objects for the lines or patch in the plot. Use h
= plotResiduals(___)h
to modify
the properties of a specific line or patch after you create the plot. For a list of
properties, see Line Properties or Patch Properties.
Examples
Generate a Probability Density Plot for Residuals
Load the fisheriris
sample data set.
load fisheriris
The column vector species
contains three iris flower species: setosa, versicolor, and virginica. The matrix meas
contains four types of measurements for the flowers: the length and width of sepals and petals in centimeters.
Fit a multinomial regression model to predict the iris flower species using the measurements. Display the table of residuals for the fitted model.
mdl = fitmnr(meas,species); mdl.Residuals
ans=150×3 table
Raw Pearson Deviance
___________ ___________ ________
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
⋮
The output shows the raw, Pearson, and deviance residuals for the model.
Generate a probability density plot of the deviance residuals.
plotResiduals(mdl)
The histogram shows that most of the deviance residuals are small. However, several residuals are greater than one.
Inspect the observations with larger residuals. Sort the residuals from largest to smallest by using the sort
function. Use the head
function to display the four largest residuals and their corresponding indices.
[r, ind] = sort(mdl.Residuals.Deviance,"descend");
head(r,4)
4.0443 3.1707 1.0378 0.8035
head(ind,4)
84 134 71 139
The outputs show that observations corresponding to rows 84, 134, and 71 of meas
and species
have residuals larger than one. Given that most other residuals are close to zero, observations 84, 134, and 71 are most likely outliers.
Determine Whether Residuals Are Random
Load the carbig
sample data set.
load carbig
The vectors Displacement
, Cylinders
, and Model_Year
contain data for car engine displacement, number of engine cylinders, and year the car was manufactured, respectively.
Fit a multinomial regression model using Displacement
and Cylinders
as predictors variables and Model_Year
as the response.
mdl = fitmnr([Displacement Cylinders],Model_Year);
To determine whether the residuals are random, generate a lagged plot of the deviance residuals.
plotResiduals(mdl,"lagged")
The lagged plot of the deviance residuals shows a cluster of residuals trending toward the top right of the plot. The cluster indicates a mildly positive correlation between the residual for a data point and the residual for the previous data point, suggesting that the residuals might not be random.
Change Probability Plot Line Color
Load the carbig
sample data set.
load carbig
The vectors Displacement
, Cylinders
, and Model_Year
contain of data for car engine displacement, number of engine cylinders, and year the car was manufactured, respectively.
Fit a nominal multinomial regression model using Displacement
and Cylinders
as predictors variables and Model_Year
as the response.
mdl = fitmnr([Displacement Cylinders],Model_Year);
mdl
is a multinomial regression model object that contains the results of fitting a nominal multinomial regression model to the data.
Display the name of the first response category.
mdl.ClassNames(1)
ans = 70
The output shows that the first response category corresponds to cars manufactured in 1970.
To determine whether the raw residuals for cars manufactured in 1970 are randomly distributed, create a probability plot and return a graphics array of the plotted objects. By default, plotResiduals
uses the raw residuals for the first response category to create the probability plot.
h = plotResiduals(mdl,"probability",ResidualType="raw")
h = 2×1 graphics array: Line (main) FunctionLine
The output shows the data types for the elements in the graphics array h
. The Line
element corresponds to the raw residuals. The FunctionLine
element corresponds to the line representing the theoretical normal distribution.
Specify the color red for the normal distribution line by modifying the Color
property of the second element in h
.
h(2).Color = "r"
h = 2×1 graphics array: Line (main) FunctionLine
The plot shows that the residuals do not follow the red line, indicating that the raw residuals for cars manufactured in 1970 are not normally distributed.
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
— Plot type
"histogram"
(default)  "caseorder"
 "lagged"
 "probability"
 "symmetry"
Plot type, specified as one of the following values.
Value  Description 

"histogram"  Probability density plot for residuals (default) 
"caseorder"  Residuals versus case (row) order 
"lagged"  Residuals versus lagged residuals—that is, r(i) versus r(i – 1), where r(i) is the residual for the ith data point 
"probability"  Normal probability plot 
"symmetry"  Symmetry plot 
Example: "lagged"
Data Types: char
 string
ax
— Target axes
Axes
object
Target axes, specified as an Axes
object. If you do not specify the
axes, then plotResiduals
uses the current axes (gca
).
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: plotResiduals(mdl,ResidualType="Pearson",ClassToPlot="virginica",FaceColor="m")
generates a magenta histogram for the Pearson residuals of the virginica
response category.
ResidualType
— Type of residual to plot
"deviance"
(default)  "Pearson"
 "raw"
Type of residual to plot, specified as one of the options in the following table.
Option  Description 

"deviance"  Deviance residuals (default) 
"raw"  Raw residuals, which are the observed values minus the fitted values 
"Pearson"  Pearson residuals, which are the raw residuals divided by the root mean squared error (RMSE) 
For more information about each residual type, see Residuals.
Example: ResidualType="raw"
Data Types: char
 string
ClassToPlot
— Residual response category to plot
response category name
Residual response category to plot, specified as the name of a response category
used to fit mdl
. You can view the list of response category names
by using the property mdl.ClassNames
. The default value of
ClassToPlot
is mdl.ClassNames(1)
. This
argument is valid only when ResidualType
is
"Pearson"
or "raw"
.
Example: ClassToPlot="virginica"
Data Types: single
 double
 logical
 char
 cell
 categorical
FaceColor
— Face color
[0 0 0]
(default)  "interp"
 "flat"
 RGB triplet  hexadecimal color code  "r"
 "g"
 "b"
 ...
Face color, specified as "interp"
, "flat"
an RGB
triplet, a hexadecimal color code, a color name, or a short name.
To designate a single color for all faces, specify FaceColor
as an RGB
triplet, a hexadecimal color code, a color name, or a short name.
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.
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: FaceColor="g"
Data Types: single
 double
 char
 string
FaceAlpha
— Face transparency
1 (default)  scalar in the range [0,1]
 'flat'
 'interp'
Face transparency, specified as one of these values:
Scalar in the range
[0,1]
— Use uniform transparency across all of the faces. A value of1
is fully opaque and0
is completely transparent. This option does not use the transparency values in theFaceVertexAlphaData
property.'flat'
— Use a different transparency for each face based on the values in theFaceVertexAlphaData
property. First you must specify theFaceVertexAlphaData
namevalue argument as a vector containing one transparency value per face or vertex. The transparency value at the first vertex determines the transparency for the entire face.'interp'
— Use interpolated transparency for each face based on the values in theFaceVertexAlphaData
property. First you must specify theFaceVertexAlphaData
namevalue argument as a vector containing one transparency value per vertex. The transparency varies across each face by interpolating the values at the vertices.
Example: FaceAlpha=0.5
Data Types: single
 double
 char
 string
LineWidth
— Line width
0.5
(default)  positive value
Line width, specified as a positive value in points, where 1 point = 1/72 of an inch. If the line has markers, then the line width also affects the marker edges.
The line width cannot be thinner than the width of a pixel. If you set the line width to a value that is less than the width of a pixel on your system, the line appears as one pixel wide.
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 Patch Properties.
Color
— Line color
[0 0 0]
(default)  RGB triplet  hexadecimal color code  "r"
 "g"
 "b"
 ...
Line color, specified as an RGB triplet, a hexadecimal color code, a color name, or a short name. The default value of [0 0 0]
corresponds to black.
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: Color="blue"
Example: Color=[0 0 1]
Example: Color="#0000FF"
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
Marker
— Marker symbol
"none"
(default)  "o"
 "+"
 "*"
 "."
 ...
Marker symbol, specified as one of the values listed in this table. By default, the object does not display markers. Specifying a marker symbol adds markers at each data point or vertex.
Marker  Description  Resulting Marker 

"o"  Circle 

"+"  Plus sign 

"*"  Asterisk 

"."  Point 

"x"  Cross 

"_"  Horizontal line 

""  Vertical line 

"square"  Square 

"diamond"  Diamond 

"^"  Upwardpointing triangle 

"v"  Downwardpointing triangle 

">"  Rightpointing triangle 

"<"  Leftpointing triangle 

"pentagram"  Pentagram 

"hexagram"  Hexagram 

"none"  No markers  Not applicable 
Example: Marker="x"
Data Types: char
 string
Note
The graphics properties for lines listed here are only a subset. For a complete list, see Line Properties.
Output Arguments
h
— Handle to plotted objects
Patch
object  graphics array
Handle to the plotted objects, returned as a Patch
object or a
graphics array.
When plotType
is "Histogram"
,
h
is a Patch
object. Otherwise,
h
is a graphics array. The contents of the graphics array depend
on the value of plotType
.
plotType Value  Contents of Graphics Array 

"caseorder"  [Line; Line] 
"lagged"  [Line; Line; Line] 
"probability"  [Line; FunctionLine] 
"symmetry"  [Line; Line] 
Version History
Introduced in R2023a
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