IMSL_ANOVABALANCED
Syntax | Return Value | Arguments | Keywords | Discussion | Example | Version History
The IMSL_ANOVABALANCED function analyzes a balanced complete experimental design for a fixed, random, or mixed model.
Note
This routine requires an IDL Advanced Math and Stats license. For more information, contact your ITT Visual Information Solutions sales or technical support representative.
Syntax
Result = IMSL_ANOVABALANCED(n_levels, y, n_random, idx_rand_fct, n_fct_per_eff, idx_fct_per_eff [, ANOVA_TABLE=variable]
[, CONFIDENCE=value] [, /DOUBLE] [, MODEL=value]
[, VAR_COMP=variable] [, Y_MEANS=variable])
Return Value
The p-value for the F-statistic.
Arguments
idx_fct_per_eff
One-dimensional index array of length N_ELEMENTS(n_fct_per_effect). The first n_fct_per_eff(0) elements give the factor numbers in the first effect. The next n_fct_per_eff(1) elements give the factor numbers in the second effect. The last n_fct_per_eff(N_ELEMENTS(n_fct_per_eff)) elements give the factor numbers in the last effect. Main effects must appear before their interactions. In general, an effect E cannot appear after an effect F if all of the indices for E appear also in F.
idx_rand_fct
One-dimensional index array of length |n_random| containing either the factor numbers to be considered random (for n_random positive) or containing the effect numbers to be considered random (for n_random negative).
n_fct_per_eff
One-dimensional array containing the number of factors associated with each effect in the model.
n_levels
One-dimensional array containing the number of levels for each of the factors.
n_random
For positive n_random, |n_random| is the number of random factors. For negative n_random, |n_random| is the number of random effects (sources of variation).
y
One-dimensional array containing the responses. y must not contain NaN (not a number) for any of its elements, i.e., missing values are not allowed.
Keywords
ANOVA_TABLE
Named variable into which an array of size 15 containing the analysis of variance table is stored. The analysis of variance statistics are as follows:
- 0—Degrees of freedom for the model
- 1—Degrees of freedom for error
- 2—Total (corrected) degrees of freedom
- 3—Sum of squares for the model
- 4—Sum of squares for error
- 5—Total (corrected) sum of squares
- 6—Model mean square
- 7—Error mean square
- 8—Overall F-statistic
- 9—p-value
- 10—R2 (in percent)
- 11—adjusted R2 (in percent)
- 12—estimate of the standard deviation
- 13—overall mean of y
- 14—coefficient of variation (in percent)
CONFIDENCE
Confidence level for two-sided interval estimates on variance components, in percent. Confidence percent confidence intervals are computed, hence, Confidence must be in the interval [0.0, 100.0]. Confidence is often 90.0, 95.0, or 99.0. For one-sided intervals with confidence level α, α in the interval [50.0, 100.0], set Confidence = 100.0 – 2.0 * (100.0 – α). Default: Confidence = 95.0
DOUBLE
If present and nonzero, then double precision is used.
MODEL
Model Option
For Scheffe model, effects corresponding to interactions of fixed and random factors have their sum over the subscripts corresponding to fixed factors equal to zero. Also, the variance of a random interaction effect involving some fixed factors has a multiplier for the associated variance component that involves the number of levels in the fixed factors. The Searle model has no summation restrictions on the random interaction effects and has a multiplier of one for each variance component.
VAR_COMP
Named variable into which an array of length N_ELEMENTS(n_fct_per_eff) + 1, by 9 array containing statistics relating to the particular variance components or effects in the model and the error is stored. Rows of Var_Comp correspond to the rows of N_ELEMENTS(n_fct_per_eff) effects plus error.
- 1—Degrees of freedom
- 2—Sum of squares
- 3—Mean squares
- 4—F -statistic
- 5—p-value for F test
- 6—Variance component estimate
- 7—Percent of variance of y explained by random effect
- 8—Lower endpoint for confidence interval on the variance component
- 9—Upper endpoint for confidence interval on the variance component
Columns 6 through 9 contain NaN (not a number) if the effect is fixed, i.e., if there is no variance component to be estimated. If the variance component estimate is negative, columns 8 and 9 contain NaN.
Ems—Named variable into which a one-dimensional array of length ((N_ELEMENTS(n_fct_per_eff) + 1)*(N_ELEMENTS(n_fct_per_eff) + 2))/2 containing expected mean square coefficients is stored. Suppose the effects are A, B, and AB. The ordering of the coefficients in Ems is as follows:
Y_MEANS
Named variable into which a one-dimensional array of length (n_levels(0) + 1) * (n_levels (1) + 1) * ... * (n_levels (n-1) + 1) containing the subgroup means is stored. Suppose the factors are A, B, and C. The ordering of the means is grand mean, A means, B means, C means, AB means, AC means, BC means, and ABC means.
Discussion
The IMSL_ANOVABALANCED function analyzes a balanced complete experimental design for a fixed, random, or mixed model. The analysis includes an analysis of variance table, and computation of subgroup means and variance component estimates. A choice of two parameterizations of the variance components for the model can be made.
Scheffé (1959, pages 274-289) discusses the parameterization for Model = 1. For example, consider the following model equation with fixed factor A and random factor B:
yijk = μ + αi + bj + cij + eijk i = 1, 2, ... , a; j = 1, 2, ... , b; k = 1, 2, ... , n
The fixed effects αi's are subject to the restriction:
the bj's are random effects identically and independently distributed:
cij are interaction effects each distributed:
and are subject to the restrictions:
and the eijk's are errors identically and independently distributed N(0, σ2). In general, interactions of fixed and random factors have sums over subscripts corresponding to fixed factors equal to zero. Also in general, the variance of a random interaction effect is the associated variance component times a product of ratios for each fixed factor in the random interaction term. Each ratio depends on the number of levels in the fixed factor. In the earlier example, the random interaction AB has the ratio (a – 1)/a as a multiplier of:
and:
In a three-way crossed classification model, an ABC interaction effect with A fixed, B random, and C fixed would have variance:
Searle (1971, pages 400-401) discusses the parameterization for Model = 0. This parameterization does not have the summation restrictions on the effects corresponding to interactions of fixed and random factors. Also, the variance of each random interaction term is the associated variance component, i.e., without the multiplier. This parameterization is also used with unbalanced data, which is one reason for its popularity with balanced data also. In the earlier example:
Searle (1971, pages 400-404) compares these two parameterizations. Hocking (1973) considers these different parameterizations and concludes they are equivalent because they yield the same variance-covariance structure for the responses. Differences in covariances for individual terms, differences in expected mean square coefficients and differences in F tests are just a consequence of the definition of the individual terms in the model and are not caused by any fundamental differences in the models. For the earlier two-way model, Hocking states that the relations between the two parameterizations of the variance components are:
where:
are the variance components in the parameterization with Model = 0.
Computations for degrees of freedom and sums of squares are the same regardless of the Model option. IMSL_ANOVABALANCED first computes degrees of freedom and sum of squares for a full factorial design. Degrees of freedom for effects in the factorial design that are missing from the specified model are pooled into the model effect containing the fewest subscripts but still containing the factorial effect. If no such model effect exists, the factorial effect is pooled into error. If more than one such effect exists, a terminal error message is issued indicating a misspecified model.
The analysis of variance method is used for estimating the variance components. This method solves a linear system in which the mean squares are set to the expected mean squares. A problem that Hocking (1985, pages 324-330) discusses is that this method can yield a negative variance component estimate. Hocking suggests a diagnostic procedure for locating the cause of the negative estimate. It may be necessary to re-examine the assumptions of the model.
The percentage of variation explained by each random effect is computed (output in Var_Comp element 7) as variance of the associated random effect divided by variance of y. The two parameterizations can lead to different values because of the different definitions of the individual terms in the model. For example, the percentage associated with the AB interaction term in the earlier two-way mixed model is computed for Model = 1 using:
while for the parameterization Model = 0, the percentage is computed using the formula:
In each case, the variance components are replaced by their estimates (stored in Var_Comp element 6).
Confidence intervals on the variance components are computed using the method discussed by Graybill (1976, Theorem 15.3.5, page 624, and Note 4, page 620).
Example
An analysis of a generalized randomized block design is performed using data discussed by Kirk (1982, Table 6.10-1, pages 293-297). The model is:
yijk = μ + αi + bj + cij + eijk i = 1, 2, 3, 4; j = 1, 2, 3, 4; k = 1, 2
where yijk is the response for k-th experimental unit in block j with treatment i; the αi's are the treatment effects and are subject to the restriction:
the bj's are block effects identically and independently distributed:
cij are interaction effects each distributed:
and are subject to the restrictions:
and the eijk's are errors, identically and independently distributed N(0, σ2). The interaction effects are assumed to be distributed independently of the errors. The data is given in Table 16-8.
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.RUN PRO print_results, p, at, ems, y_means, var_comp anova_labels = ['degrees of freedom for model', $ 'degrees of freedom for error', $ 'total (corrected) degrees of freedom', $ 'sum of squares for model', 'sum of squares for error', $ 'total (corrected) sum of squares', 'model mean square', $ 'error mean square', 'F-statistic', 'p-value',$ 'R-squared (in percent)', $ 'adjusted R-squared (in percent)', $ 'est. standard deviation of within error', $ 'overall mean of y', $ 'coefficient of variation (in percent)'] ems_labels = ['Effect A and Error', $ 'Effect A and Effect AB', 'Effect A and Effect B', $ 'Effect A and Effect A', 'Effect B and Error', $ 'Effect B and Effect AB', 'Effect B and Effect B', $ 'Effect AB and Error', 'Effect AB and Effect AB', $ 'Error and Error'] components_labels = ['degrees of freedom for A', $ 'sum of squares for A', 'mean square of A', $ 'F-statistic for A', 'p-value for A', $ 'Estimate of A', 'Percent Variation Explained by A', $ '95% Confidence Interval Lower Limit for A', $ '95% Confidence Interval Upper Limit for A', $ 'degrees of freedom for B', 'sum of squares for B', $ 'mean square of B', 'F-statistic for B', 'p-value for B', $ 'Estimate of B', 'Percent Variation Explained by B', $ '95% Confidence Interval Lower Limit for B', $ '95% Confidence Interval Upper Limit for B', $ 'degrees of freedom for AB', 'sum of squares for AB', $ 'mean square of AB', 'F-statistic for AB', $ 'p-value for AB', 'Estimate of AB', $ 'Percent Variation Explained by AB', $ '95% Confidence Interval Lower Limit for AB', $ '95% Confidence Interval Upper Limit for AB', $ 'degrees of freedom for Error', $ 'sum of squares for Error', 'mean square of Error', $ 'F-statistic for Error', 'p-value for Error', $ 'Estimate of Error', 'Percent Explained by Error', $ '95% Confidence Interval Lower Limit for Error', $ '95% Confidence Interval Upper Limit for Error'] means_labels = ['Grand mean', ' A means 1', ' A means 2', $ ' A means 3', ' A means 4', ' B means 1', ' B means 2', $ ' B means 3', ' B means 4', 'AB means 1 1', $ 'AB means 1 2', 'AB means 1 3', 'AB means 1 4', $ 'AB means 2 1', 'AB means 2 2', 'AB means 2 3', $ 'AB means 2 4', 'AB means 3 1', 'AB means 3 2', $ 'AB means 3 3', 'AB means 3 4', 'AB means 4 1', $ 'AB means 4 2', 'AB means 4 3', 'AB means 4 4'] PRINT, 'p value of F statistic =', p PRINT PRINT, ' * * * Analysis of Variance * * *' FOR i = 0, 14 DO $ PM, anova_labels(i), at(i), FORMAT = '(A40, F20.5)' PRINT PRINT, ' * * * Expected Mean Square Coefficients * * *' FOR i = 0, 9 DO $ PM, ems_labels(i), ems(i), FORMAT = '(A40, F20.2)' PRINT PRINT, ' * * Analysis of Variance / Variance Components * *' k = 0 FOR i = 0, 3 DO BEGIN FOR j = 0, 8 DO BEGIN PM, components_labels(k), var_comp(i, j), $ FORMAT = '(A45, F20.5)' k = k + 1 ENDFOR ENDFOR PRINT PRINT, 'means', FORMAT = '(A20)' FOR i = 0, 24 DO $ PM, means_labels(i), y_means(i), FORMAT ='(A20, F20.2)' END y = [3.0, 6.0, 3.0, 1.0, 2.0, 2.0, 3.0, 2.0, 4.0, 5.0, 4.0, $ 2.0, 3.0, 4.0, 3.0, 3.0, 7.0, 8.0, 7.0, 5.0, 6.0, 5.0, $ 6.0, 6.0, 7.0, 8.0, 9.0, 10.0, 10.0, 9.0, 8.0, 11.0] n_levels = [4, 4, 2] indrf = [2, 3] nfef = [1, 1, 2] indef = [1, 2, 1, 2] p = IMSL_ANOVABALANCED(n_levels, y, 2, indrf, nfef, indef, $ Anova_Table = at, Ems = ems, Y_Means = y_means, $ Var_Comp = var_comp) print_results, p, at, ems, y_means, var_comp p value of F statistic = 4.94719e-06 * * * Analysis of Variance * * * degrees of freedom for model 15.00000 degrees of freedom for error 16.00000 total (corrected) degrees of freedom 31.00000 sum of squares for model 216.50000 sum of squares for error 19.00000 total (corrected) sum of squares 235.50000 model mean square 14.43333 error mean square 1.18750 F-statistic 12.15439 p-value 0.00000 R-squared (in percent) 91.93206 adjusted R-squared (in percent) 84.36836 est. standard deviation of within error 1.08972 overall mean of y 5.37500 coefficient of variation (in percent) 20.27395 * * * Expected Mean Square Coefficients * * * Effect A and Error 1.00 Effect A and Effect AB 2.00 Effect A and Effect B 0.00 Effect A and Effect A 8.00 Effect B and Error 1.00 Effect B and Effect AB 2.00 Effect B and Effect B 8.00 Effect AB and Error 1.00 Effect AB and Effect AB 2.00 Error and Error 1.00 * * Analysis of Variance / Variance Components * * degrees of freedom for A 3.00000 sum of squares for A 194.50000 mean square of A 64.83334 F-statistic for A 32.87324 p-value for A 0.00004 Estimate of A NaN Percent Variation Explained by A NaN 95% Confidence Interval Lower Limit for A NaN 95% Confidence Interval Upper Limit for A NaN degrees of freedom for B 3.00000 sum of squares for B 4.25000 mean square of B 1.41667 F-statistic for B 0.71831 p-value for B 0.56566 Estimate of B -0.06944 Percent Variation Explained by B 0.00000 95% Confidence Interval Lower Limit for B NaN 95% Confidence Interval Upper Limit for B NaN degrees of freedom for AB 9.00000 sum of squares for AB 17.75000 mean square of AB 1.97222 F-statistic for AB 1.66082 p-value for AB 0.18016 Estimate of AB 0.39236 Percent Variation Explained by AB 24.83516 95% Confidence Interval Lower Limit for AB 0.00000 95% Confidence Interval Upper Limit for AB 2.75803 degrees of freedom for Error 16.00000 sum of squares for Error 19.00000 mean square of Error 1.18750 F-statistic for Error NaN p-value for Error NaN Estimate of Error 1.18750 Percent Explained by Error 75.16483 95% Confidence Interval Lower Limit for Error 0.65868 95% Confidence Interval Upper Limit for Error 42.75057 means Grand mean 5.38 A means 1 2.75 A means 2 3.50 A means 3 6.25 A means 4 9.00 B means 1 6.00 B means 2 5.12 B means 3 5.12 B means 4 5.25 AB means 1 1 4.50 AB means 1 2 2.00 AB means 1 3 2.00 AB means 1 4 2.50 AB means 2 1 4.50 AB means 2 2 3.00 AB means 2 3 3.50 AB means 2 4 3.00 AB means 3 1 7.50 AB means 3 2 6.00 AB means 3 3 5.50 AB means 3 4 6.00 AB means 4 1 7.50 AB means 4 2 9.50 AB means 4 3 9.50 AB means 4 4 9.50 ; Add Outliners x(0, 1) = 100.0 x(3, 4) = 100.0 x(99, 2) = -100.0 p_cov = IMSL_POOLED_COV(x, n_groups, Idx_Vars = idxv, $ Idx_Cols = idxc) PM, p_cov, Title = 'Pooled Cavariance with Outliners' r_cov = IMSL_ROBUST_COV(x, n_groups, Idx_Vars = idxv, $ Idx_Cols = idxc, Percentage = percentage) PM, r_cov, Title = 'Robust Covariance with Outliners' Pooled Cavariance with Outliners 60.4264 0.304244 0.127488 -1.55551 0.304244 70.5257 0.167135 -0.171791 0.127488 0.167135 0.185188 0.0684639 -1.55551 -0.171791 0.0684639 66.3798 Robust Covariance with Outliners 0.255521 0.0876029 0.155279 0.0359198 0.0876029 0.112674 0.0545391 0.0322426 0.155279 0.0545391 0.172263 0.0412149 0.0359198 0.0322426 0.0412149 0.0424182
Version History
