limma_mrlm: function to retrieve the contrasts stored in a dataset and...
In geantonicelli/GLM: set of wrapper and adapter functions for statistical analysis

Description Usage Arguments Value Author(s) See Also Examples

this function is a wrapper and an adapter around 'aov', it retrieves the contrasts for each independent variable specified in an aov() model and stored in the original dataset, and fits an ANOVA model on all contrasts for each independent variable, in order to obtained the computed SS and MS for further calculations, otherwise not available from the original ANOVA model as displayed by summary() or summary.lm(), the model fitted for all contrasts of one variable is the same as displayed by summary.lm() on the original ANOVA model

1	limma_mrlm(M, design = NULL, ndups = 1, spacing = 1, weights = NULL, ...)

anova

an object of class anova data.frame with the ANOVA model created by aov(), in the original dataset each variable must be in only one column, and all contrasts for each variable should be loaded as contrasts for that variable

a list with components

coefficients	numeric matrix containing the estimated coefficients for each linear model (same number of rows as M, same number of columns as design)
stdev.unscaled	numeric matrix conformal with coef containing the unscaled standard deviations for the coefficient estimators, the standard errors are given by stdev.unscaled*sigma
sigma	numeric vector containing the residual standard deviation for each gene
df.residual	numeric vector giving the degrees of freedom corresponding to sigma
correlation	inter-duplicate or inter-block correlation
qr	QR decomposition of the generalized linear squares problem, i.e. the decomposition of design standardized by the Choleski-root of the correlation matrix defined by correlation
effects	numeric matrix containing the estimated effects for each linear model (same number of rows and columns as M)
residuals	numeric matrix containing the estimated residuals for each linear model (same number of rows and columns as M)

gerardo esteban antonicelli

'check_contrasts' 'omega_factorial'

data(gogglesData)
data(depressionData)
goggles.model <- aov(attractiveness ~ gender + alcohol + gender:alcohol, data=gogglesData)
simple.model <- aov(attractiveness ~ simple, data=gogglesData)
depression.model <- aov(diff ~ treat, data=depressionData)
fit_aov(goggles.model)
fit_aov(simple.model)
fit_aov(depression.model)