aldex.glm.effect: calculate effect sizes and differences between all constrasts...
In ALDEx2: Analysis Of Differential Abundance Taking Sample Variation Into Account

Description Usage Arguments Details Value Author(s) References See Also Examples

data for this function is saved in a list with entries named by contrast determines the median clr abundance of the feature in all samples and in groups determines the median difference between the two groups determines the median variation within each two group determines the effect size, which is the median of the ratio of the between group difference and the larger of the variance within groups

1	aldex.glm.effect(clr, verbose = TRUE, include.sample.summary = FALSE, useMC=FALSE, CI=FALSE)

`clr`	`clr` is the data output of `aldex.clr`
`verbose`	Print diagnostic information while running. Useful only for debugging if fails on large datasets
`include.sample.summary`	include median clr values for each sample, defaults to FALSE
`useMC`	use multicore by default (FALSE)
`CI`	give effect 95

An explicit example for two conditions is shown in the ‘Examples’ below.

returns a dataframe with the following information:

`rab.all`	a vector containing the median clr value for each feature
`rab.win.conditionA`	a vector containing the median clr value for each feature in condition A
`rab.win.conditionB`	a vector containing the median clr value for each feature in condition B
`diff.btw`	a vector containing the per-feature median difference between condition A and B
`diff.win`	a vector containing the per-feature maximum median difference between Dirichlet instances within conditions
`effect`	a vector containing the per-feature effect size
`overlap`	a vector containing the per-feature proportion of effect size that is 0 or less

Greg Gloor, Andrew Fernandes, Matt Links

Please use the citation given by citation(package="ALDEx").

aldex.clr, aldex.effect, aldex.ttest, aldex.glm, selex

    # x is the output of the \code{x <- clr(data, mc.samples)} function
    # conditions is a description of the data
    # for the selex dataset, conditions <- c(rep("N", 7), rep("S", 7))
    data(selex)
    #subset for efficiency
    selex <- selex[1201:1600,]
    covariates <- data.frame("A" = sample(0:1, 14, replace = TRUE),
                  "B" = c(rep(0, 7), rep(1, 7)),
                  "Z" = sample(c(1,2,3), 14, replace=TRUE))
    mm <- model.matrix(~ A + Z + B, covariates)
    x <- aldex.clr(selex, mm, mc.samples=8, denom="all")
    glm.effect <- aldex.glm.effect(x)