Anovadoe

Purpose

Function to perform ANOVA for 2^k factorial model X, Y data.

Synopsis

out = anovadoe(x, y)

out = anovadoe(x, y, column_ID, options)

out = anovadoe(x, y, options)

Description

Performs ANOVA for the model described by the submitted X data relative to the Y data. Each column of X is design in a specific way to allow the calculation of an effect due to the specific main effect or interaction used to design each specific column. The main output is a statistical test of the significance of each term (eg., column) of the X matrix, a test of the overall model, and a test for lack-of-fit. There are additional statistical values supplied that support the above test metrics that might be used to construct a typical ANOVA table if desired.

Inputs

• x = matrix describing the settings of each X variable (cols) for each sample (row). Typically this would be a 2^k DOE design matrix. The origin/identity of each column is described in the 'column_ID' var.
• y = the experimental Y value determined for each experiment/row of X.

Optional Inputs

• second = optional second input is this.

Outputs

• column_ID = a cell array of numerical values which describes the multiplicative origin of each column of X. If x is a DOE dataset object, this input can be omitted (the information will be included in the dataset object) If omitted, each column of X is assumed to be a unique (non-interation) factor.

NOTE: The number of columns in x must = number of cells in column_ID.

NOTE: If an intercept is to be explicitly included as a column of

% 'ones' in the x matrix (as in a regression), then that column % must be represented as a '0' in column_ID. A DOE design does % not typically have an explicit intercept. % *** Further, the numbers in column_ID must be in increasing order by % single digits, 2 digits, 3 digits, etc. (and obviously must % match the x matrix). % Examples: % column_ID = {[1] [2] [1 2]}; % Indicates how columns were derived. Multiple numbers indicate % interaction and which original columns were used to calculate % design vars. Here, 1 and 2 are independent columns, but column 3 % is a dependent column derived from the product of 1 and 2. Column % 3 will be used to calculate the interaction of factors 1 and 2. % The term [1 2] must appear after the term [1]. Further, a term [1 % 2 3] must appear after a term [1 2], which must likewise appear % after a term [1]. % column_ID = {[1] [2] [1 2] [1 3] [2 3] [1 2 3]}; % % If you include an interaction term e.g. [1 2 3] in a model, all % subterms encompassed by the highest order term must also be % included. So subterms [1] [2] [1 2] [1 3] [2 3] all be included.

Options

options = a structure array with the following fields:

• plots: [ {'none'} | 'final' ] governs plotting of results, and
• order: positive integer for polynomial order {default = 1}.

Example

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See Also

% % OPTIONAL INPUTS: % column_ID = a cell array of numerical values which describes the % multiplicative origin of each column of X. If x is a % DOE dataset object, this input can be omitted (the % information will be included in the dataset object) % If omitted, each column of X is assumed to be a unique % (non-interation) factor. % * The number of columns in x must = number of cells in column_ID. % ** If an intercept is to be explicitly included as a column of % 'ones' in the x matrix (as in a regression), then that column % must be represented as a '0' in column_ID. A DOE design does % not typically have an explicit intercept. % *** Further, the numbers in column_ID must be in increasing order by % single digits, 2 digits, 3 digits, etc. (and obviously must % match the x matrix). % Examples: % column_ID = {[1] [2] [1 2]}; % Indicates how columns were derived. Multiple numbers indicate % interaction and which original columns were used to calculate % design vars. Here, 1 and 2 are independent columns, but column 3 % is a dependent column derived from the product of 1 and 2. Column % 3 will be used to calculate the interaction of factors 1 and 2. % The term [1 2] must appear after the term [1]. Further, a term [1 % 2 3] must appear after a term [1 2], which must likewise appear % after a term [1]. % column_ID = {[1] [2] [1 2] [1 3] [2 3] [1 2 3]}; % % If you include an interaction term e.g. [1 2 3] in a model, all % subterms encompassed by the highest order term must also be % included. So subterms [1] [2] [1 2] [1 3] [2 3] all be included. % % options = Options structure with one or more of the following fields. % Options can be passed in place of column_ID. % % display : [{'off'}| 'on' ] governs output to the command window. % % OUTPUTS: % out = a structure containing the sum of squares, mean square values, % F-test values, F-critical values, and p-values for each % column/treatment, for the model as a whole, for overall residual % error, and for lack-of-fit and pure error (if replication was % present). % %I/O: out = anovadoe(x, y); %I/O: out = anovadoe(x, y, column_ID, options); %I/O: out = anovadoe(x, y, options);