plotprofile
Description
 returns
        an array of lines = plotprofile(___)Line objects for the lines in the plot.
Examples
Load the fisheriris data set.
load fisheririsThe column vector species contains iris flowers of three different species: setosa, versicolor, and virginica. The matrix meas contains four types of measurements for the flower: the length and width of sepals and petals in centimeters.
Perform a one-way MANOVA with species as the factor and the measurements in meas as the response variables.
maov = manova(species,meas,FactorNames="species",ResponseNames=["SepalLength" "SepalWidth"... "PetalLength" "PetalWidth"])
maov = 
1-way manova
SepalLength,SepalWidth,PetalLength,PetalWidth ~ 1 + species
    Source     DF     TestStatistic    Value       F       DFNumerator    DFDenominator      pValue  
    _______    ___    _____________    ______    ______    ___________    _____________    __________
    species      2       pillai        1.1919    53.466          8             290         9.7422e-53
    Error      147                                                                                   
    Total      149                                                                                   
  Properties, Methods
maov is a manova object that contains the results of the one-way MANOVA. The small p-value for species indicates that the flower species has a statistically significant effect on at least one of the flower measurements.
Create a profile plot of the mean measurements for each flower species.
plotprofile(maov) legend

The profile plot shows that the means are the most spread out for the petal length measurement, and that setosa has smaller measurements, on average, than the other two flower species.
Load the carsmall data set.
load carsmallThe variable Model_Year contains data for the year a car was manufactured, and the variable Cylinders contains data for the number of engine cylinders in the car. The Acceleration and Displacement variables contain data for car acceleration and displacement.
Use the table function to create a table from the data in Model_Year, Cylinders, Acceleration, and Displacement.
tbl = table(Model_Year,Cylinders,Acceleration,Displacement,VariableNames=["Year" "Cylinders" "Acceleration" "Displacement"]);
Perform a two-way MANOVA using the table variables Year and Cylinders as factors, and the Acceleration and Displacement variables as response variables.
maov = manova(tbl,"Acceleration,Displacement ~ Cylinders + Year")maov = 
2-way manova
Acceleration,Displacement ~ 1 + Year + Cylinders
     Source      DF    TestStatistic     Value        F       DFNumerator    DFDenominator      pValue  
    _________    __    _____________    ________    ______    ___________    _____________    __________
    Year          2       pillai        0.084893    2.1056          4             190           0.081708
    Cylinders     2       pillai         0.94174     42.27          4             190         2.5049e-25
    Error        95                                                                                     
    Total        99                                                                                     
  Properties, Methods
maov is a manova object that contains the results of the two-way MANOVA. The table output shows that the p-value for the MANOVA model term Year is too large to conclude that Year has a statistically significant effect on the mean response vector. However, the small p-value for Cylinders indicates that enough evidence exists to conclude that Cylinders has a statistically significant effect on the mean response vector.
Create a profile plot of the means for Acceleration and Displacement grouped by the combinations of values for Year and Cylinders. Use the axes function to create axes that plot lines in magenta, green, and black.
lineColors = [1 0 1; 0 1 0; 0 0 0]; ax = axes(ColorOrder=lineColors); plotprofile(ax,maov,["Year" "Cylinders"]) legend

The profile plot shows that the means for Acceleration are similar. However, the color coding shows that the means for Displacement are clustered by their corresponding values in Cylinders. The top cluster, shown in black, corresponds to cars with eight-cylinder engines. The middle cluster, shown in green, corresponds to cars with six-cylinder engines. The bottom cluster, shown in magenta, corresponds to cars with four-cylinder engines. This result supports the conclusion that Cylinders has a statistically significant effect on the mean response vector, but Year does not.
Input Arguments
MANOVA results, specified as a manova object.
            The properties of maov contain the factor values and response data
            used by plotprofile to create the profile plot.
Factors used to group the response data, specified as a string vector or a cell
            array of character vectors. The plotprofile function groups the
            response data by the combinations of values for the factors in
              factors. The factors argument must be one or
            more categorical factor names in maov.FactorNames.
Example: ["Factor1","Factor2"]
Data Types: string | cell
Target axes, specified as an Axes object. If you do not specify the
            axes, then plotprofile uses the current axes (gca).
Output Arguments
Profile plot lines, returned as an array of Line object handles.
            You can modify the properties of the Line objects to customize the
            profile plot. For a complete list of Line properties, see Line Properties.
Version History
Introduced in R2023b
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