groupmeans

Mean response estimates for analysis of variance (ANOVA)

Since R2022b

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    Syntax

    means = groupmeans(aov)
    means = groupmeans(aov,factors)
    means = groupmeans(___,Alpha=alpha)

    Description

    means = groupmeans(aov) returns a table of mean response estimates, standard errors, and 95% confidence intervals for each value of the factor in a one-way ANOVA.

    example

    means = groupmeans(aov,factors) returns means with information for each unique combination of values of the factors specified in factors.

    means = groupmeans(___,Alpha=alpha) specifies the confidence level of the confidence intervals to be 100(1−α)%.

    example

    Examples

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    Open Live Script

    Load the sample data.

    load carbig.mat

    Create a table that has variables for acceleration and horsepower category. Obtain the horsepower categories by sorting the variable Horsepower into three horsepower ranges.

    tbl = table(Acceleration);
tbl.HorsepowerCats = discretize(Horsepower,[0 100 200 300])
    tbl=406×2 table
    Acceleration    HorsepowerCats
    ____________    ______________

          12              2       
        11.5              2       
          11              2       
          12              2       
        10.5              2       
          10              2       
           9              3       
         8.5              3       
          10              3       
         8.5              2       
        17.5              2       
        11.5              2       
          11              2       
        10.5              2       
          11              2       
          10              2       
      ⋮

    Perform a one-way ANOVA to test the null hypothesis that the mean acceleration time is the same across the three horsepower ranges.

    aov = anova(tbl,"Acceleration")
    aov = 
1-way anova, constrained (Type III) sums of squares.

Acceleration ~ 1 + HorsepowerCats

                      SumOfSquares    DF     MeanSquares      F         pValue  
                      ____________    ___    ___________    ______    __________

    HorsepowerCats       975.93         2      487.96       89.571    7.8471e-33
    Error                2162.8       397      5.4478                           
    Total                3138.7       399                                       


  Properties, Methods

    The small p-value indicates that the mean acceleration time is different for at least one of the horsepower categories. Investigate which horsepower ranges have different mean acceleration times by inspecting the means of the horsepower categories.

    means = groupmeans(aov)
    means=3×5 table
    HorsepowerCats     Mean       SE       MeanLower    MeanUpper
    ______________    ______    _______    _________    _________

          1           16.804    0.15526     16.498        17.11  
          2           13.969    0.18282     13.608        14.33  
          3           11.136    0.70374     9.5683       12.704

    The table means shows that each category has a mean that is outside the 95% confidence intervals of the mean estimates for the other categories. Therefore, the mean acceleration time is significantly different for all three horsepower categories.

    Open Live Script

    Load the car mileage sample data.

    load mileage.mat

    The columns of the 6-by-3 matrix mileage contain mileage data for three car models. The first three rows contain data for cars built at one factory, and the last three rows contain data for cars built at another factory.

    Convert mileage to a vector.

    mileage = mileage(:);

    Create string arrays of factor values for the factory and car model factors using the function repmat.

    factory = repmat(["factory1";"factory1";"factory1";...
    "factory2";"factory2";"factory2"], [3, 1]);
model = [repmat("model1",6,1);...
    repmat("model2",6,1);repmat("model3",6,1)];
factors = {factory,model};

    Perform a two-way ANOVA to test the null hypothesis that car mileage is not affected by the factory or car model factors.

    aov = anova(factors,mileage,FactorNames=["Factory","Model"])
    aov = 
2-way anova, constrained (Type III) sums of squares.

Y ~ 1 + Factory + Model

               SumOfSquares    DF    MeanSquares      F         pValue  
               ____________    __    ___________    ______    __________

    Factory        1.445        1        1.445      14.382     0.0019807
    Model         53.351        2       26.676      265.49    7.3827e-12
    Error         1.4067       14      0.10048                          
    Total         56.203       17                                       


  Properties, Methods

    The small p-values indicate that the model of a car has a more significant effect on car mileage than the factory in which the car was manufactured.

    To investigate which car models have different mileages at the 99% confidence level, inspect the group means.

    means = groupmeans(aov,"Model",Alpha=0.01)
    means=3×5 table
     Model       Mean       SE       MeanLower    MeanUpper
    ________    ______    _______    _________    _________

    "model1"     32.95    0.12941     32.428       33.472  
    "model2"    34.017    0.12941     33.495       34.538  
    "model3"    37.017    0.12941     36.495       37.538

    The table shows that the 99% confidence intervals of all car models do not overlap. Therefore, all three models have statistically significant differences in mean car mileage at the 99% confidence level.

    Input Arguments

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    ANOVA results, specified as an anova object. The properties of aov contain the factors and response data used by groupmeans to compute the mean responses.

    Factors used to group the response data, specified as a string vector or cell array of character vectors. The groupmeans function groups the response data by the combinations of values for the factors in factors. The factors argument must be one or more of the names in aov.FactorNames.

    Example: ["g1","g2"]

    Data Types: string | cell

    Significance level for the estimates, specified as a scalar value in the range (0,1). The confidence level of the confidence intervals is 100(1−α)%. The default value for alpha is 0.05, which returns 95% confidence intervals for the estimates.

    Example: Alpha=0.01

    Data Types: single | double

    Output Arguments

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    Mean response estimates, standard errors, and confidence intervals, returned as a table. The table means has one row per unique combination of factor values. If aov is a one-way anova object, means has a column corresponding to the single factor. If aov is a two- or N-way anova object, means contains a column for each factor specified in factors. In addition to the factor columns, means contains the following:

    • Mean — Estimate of the mean response of the factor value

    • SE — Standard error of the mean estimate

    • MeanLower — 95% lower confidence bound of the mean estimate

    • MeanUpper — 95% upper confidence bound of the mean estimate

    Version History

    Introduced in R2022b

    See Also

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