CNL Stat : Analysis of Variance and Designed Experiments
Analysis of Variance and Designed Experiments
Functions
General Analysis of Variance
One-way analysis of variance, anova_oneway
Analyzes a one-way classification model with covariates, ancovar
Analysis of variance for fixed effects balanced factorial designs, anova_factorial
Nested random effects analysis of variance, anova_nested
Analysis of variance for balanced fixed, random, or mixed models, anova_balanced
Designed Experiments
Analysis of balanced and unbalanced completely randomized factorial experiments, crd_factorial
Analysis of balanced and unbalanced randomized
complete block factorial experiments, rcbd_factorial
Analysis of latin-square experiments, latin_square
Analysis of balanced and partially-balanced data from lattice experiments, lattice
Analysis of split-plot experiments, split_plot
Analysis of split-split-plot experiments, split_split_plot
Analysis of strip-plot experiments, strip_plot
Analysis of strip-split-plot experiments, strip_split_plot
Utilities
Bartlett’s and Levene’s tests of the homogeneity of variance assumption in analysis of variance, homogeneity
Multiple comparisons of means, multiple_comparisons
False discovery rates, false_discovery_rates
Yates’ method for estimating missing observations in designed experiments, yates