Description Usage Arguments Details References
Computes a oneway analysis of variance with post hoc tests.
1 2 3 4 5 6  OneWayANOVA(outcome, predictor, subset = NULL, weights = NULL,
compare = "Pairwise", correction = "Tukey Range",
alternative = "Twosided", robust.se = FALSE,
missing = "Exclude cases with missing data", show.labels = TRUE,
outcome.name = NULL, predictor.name = NULL, p.cutoff = 0.05,
seed = 1223, return.all = FALSE, ...)

outcome 
The outcome variable. 
predictor 
The factor representing the groups. 
subset 
An optional vector specifying a subset of observations to be
used in the fitting process, or, the name of a variable in 
weights 
An optional vector of sampling weights, or, the name or, the
name of a variable in 
compare 
One of 
correction 
The multiple comparison adjustment method: 
alternative 
The alternative hypothesis: "Two sided", "Greater", or "Less". The main application of this is when Compare us set 'To first' (e.g., if testing a new product, where the purpose is to work out of the new product is superior to an existing product, "Greater" would be chosen). 
robust.se 
If 
missing 
How missing data is to be treated in the ANOVA. Options:

show.labels 
Shows the variable labels, as opposed to the labels, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")). 
outcome.name 
The name of the outcome variable. Only used when 
predictor.name 
The name of the predictor variable. Only used when 
p.cutoff 
The alpha level to be used in testing. 
seed 
The random number seed used when evaluating the multivariate tdistribution. 
return.all 
If 
... 
Other parameters to be passed to wrapped functions. 
When 'Tukey Range' is selected, pvalues are computed using t'tests, with a correction for the familywise error rate such that the pvalues are correct for the largest range of values being compared (i.e., the biggest difference between the smallest and largest means). This is a singlestep test. The method of calculation is valid for both balanced and unbalanced samples (Bretz et al. 2011), and consequently the results may differ for unbalanced samples to those that appear in most software and books (which instead employee an approximation when the samples are unbalanced).
When missing = "Imputation (replace missing values with estimates)"
, all selected
outcome and predictor variables are included in the imputation, along with
all auxiliary.data
, excluding cases that are excluded via subset or
have invalid weights, but including cases with missing values of the outcome variable.
Then, cases with missing values in the outcome variable are excluded from
the analysis (von Hippel 2007). See Imputation
.
Bretz,Frank, Torsten Hothorn and Peter Westfall (2011), Multiple Comparisons Using R, CRC Press, Boca Raton. Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57, 289300. Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 11651188. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 6570. Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75, 800803. Hommel, G. (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75, 383386. Hothorn, Torsten, Frank Bretz and Peter Westfall (2008), Simultaneous Inference in General Parametric Models. Biometrical Journal, 50(3), 346363. Long, J. S. and Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in the linear regression model. The American Statistician, 54(3): 217224. Shaffer, Juliet P. (1986), Modified sequentially rejective multiple test procedures. Journal of the American Statistical Association, 81, 826831. Shaffer, Juliet P. (1995). Multiple hypothesis testing. Annual Review of Psychology 46, 561576. Sarkar, S. (1998). Some probability inequalities for ordered MTP2 random variables: a proof of Simes conjecture. Annals of Statistics 26, 494504. Sarkar, S., and Chang, C. K. (1997). Simes' method for multiple hypothesis testing with positively dependent test statistics. Journal of the American Statistical Association 92, 16011608. Tukey, John (1949). "Comparing Individual Means in the Analysis of Variance". Biometrics. 5 (2): 99114. Peter H. Westfall (1997), Multiple testing of general contrasts using logical constraints and correlations. Journal of the American Statistical Association, 92, 299306. P. H. Westfall, R. D. Tobias, D. Rom, R. D. Wolfinger, Y. Hochberg (1999). Multiple Comparisons and Multiple Tests Using the SAS System. Cary, NC: SAS Institute Inc. von Hippel, Paul T. 2007. "Regression With Missing Y's: An Improved Strategy for Analyzing Multiply Imputed Data." Sociological Methodology 37:83117. Wright, S. P. (1992). Adjusted Pvalues for simultaneous inference. Biometrics 48, 10051013. White, H. (1980), A heteroskedasticconsistent covariance matrix estimator and a direct test of heteroskedasticity. Econometrica, 48, 817838.
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