Random Effects Analysis of Variance

Analysis of variance (ANOVA) is generally used to explore the influence of one or more categorical variables upon a continuous response. Random effects ANOVA is used in balanced designed experiments in which the treatment effects are taken to be random.

For mixed effects models, use the linear mixed effects (lme) function.

To perform random effects analysis of variance

Choose Statistics __image\arrow5.gif Analysis of Variance __image\arrow5.gif Random Effects. The dialog shown below appears.

Model page

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In the Random Effects Analysis of Variance dialog, the Model page has the following options:

Data

Data Set

Select a data set from the dropdown list or type the name of a data set. You can also type into the Data Set edit field any expression that evaluates to a data set.

Weights

Enter the column that specifies weights to be applied to all observations used in the analysis. To weight all rows equally, leave this blank.

Subset Rows

Enter an S-PLUS expression that identifies the rows to use in the analysis. To use all the rows in the data set, leave this field blank.

Omit Rows with Missing Values

Select this box to omit from the analysis any rows in the data set that contain missing values for any of the variables in the model.

Variables

Dependent Variables

Select a variable as the dependent variable in the formula. The variable name will appear in the formula field below, followed by a '~'.

Independent Variables

Select one or more variables as the independent variables, or predictor, in the formula. To select more than one variable, Ctrl-click the variables.

Formula

In the Formula field, enter a formula specifying the desired model. In its simplest form a formula consists of the response variable, a tilde (~), and a list of predictor variables separated by "+"s. An intercept is automatically included by default.

Create Formula

Click the Create Formula button to open a formula builder dialog used to construct a formula specifying the desired model. See the online Help section Building Formulas for more information.

Save Model Object

In the Save As field, enter the name for the object in which to save the results of the analysis. If an object with this name already exists, its contents are overwritten. The model object can be used in later functions such as plotting.

Options page

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In the Random Effects Analysis of Variance dialog, the Options page has the following options:

Contrasts

Assign Contrast Choose contrasts for the factors; by default, the Helmert contrasts are assigned to unordered factors and polynomial contrasts are assigned to ordered factors.

to Variable(s) Select one or more variables to which the selected contrast in Assign Contrast will be assigned.

Contrasts This field displays the selection and assignment chosen in Assign Contrast and to Variable(s).

Results page

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In the Random Effects Analysis of Variance dialog, the Results page has the following options:

Printed Results

Short Output for ANOVA

This option is selected by default.

Type I Sums of Squares

This option is selected by default. The sums of squares decomposition reflects the amount of variance each term contributes to the overall model variation.

Type III Sums of Squares

Select to print the Type III Sums of Squares.

Estimated Coefficients

Select this to print the estimated coefficients. There are K-1 such coefficients for each K-level factor.

Estimated K Coef for K-Level Factor

Print K coefficients for each K-level factor.

Means

Select to print the mean values.

Adjusted Means

Select to print the adjusted mean values.

Saved Results

Save In

Enter the name of a data set in which a part of the analysis, such as fitted values and residuals, predictions, confidence intervals, or standard errors, is saved. If an object with the name you enter does not already exist (in database 1), then it is created

Fitted Values

Save the fitted values from the model in the object specified in Save In.

Residuals

Save the residuals from the model in the object specified in Save In. These are the ordinary residuals (the response minus the fitted value).

Plot page

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In the Random Effects Analysis of Variance dialog, the Plot page has the following options:

Plots

Residuals vs Fit

Select this to display a plot of the residuals versus the fitted values.

Sqrt Abs Residuals vs Fit

Display a plot of the square root of the absolute values of the residuals versus the fitted values. This plot is useful for checking for the constant variance assumption of the model.

Response vs Fit

Display a plot of the response variable versus the fitted values. The line y = x is also drawn on the graph.

Residuals Normal QQ

Display a normal quantile-quantile plot of the residuals.

Residual-Fit Spread

Display a residual-fit spread plot. This is a visual analog of the multiple R-squared statistic. It compares the spread of the fitted values to the spread of the residuals.

Cook's Distance

Display a plot of Cook's distance values versus the observation number.

Partial Residuals

Display partial residual plots for all the terms in the model.

Options

Include Smooth

Display a smooth curve, computed with loess.smooth, on the Residuals vs Fit, Sqrt Abs Residuals vs Fit, and Response vs Fit plots. See the online Help for loess.smooth for details.

Include Rugplot

Display a rugplot on the Residuals vs Fit, Sqrt Abs Residuals vs Fit, and Response vs Fit plots. A rugplot is a sequence of vertical bars along the x-axis that mark the "observed" x values.

Number of Extreme Points to Identify

Enter the number of extreme points that are identified on the Residuals vs Fit, Sqrt Abs Residuals vs Fit, Residuals Normal QQ, and Cook's Distance plots. The row names from the data set specified on the model page are used to identify the points.

Partial Residual Plot Options

Include Partial Fit

Include the partial fit for the term on the plot.

Include Rugplot

Display rugplots on the partial residual plots. A rugplot is a sequence of vertical bars along the x-axis that mark the "observed" x values.

Common Y-Axis Scale

Give all the partial residual plots the same vertical units. This is essential for comparing the importance of fitted terms in additive models.

S-Plus language functions related to Analysis of Variance

aov, summary.aov, plot.lm, coef, dummy.coef, raov

Other related S-Plus functions

lm, manova, multicomp