VCA (version 1.5.1)

summarize.VCA: Summarize Outcome of a Variance Component Analysis.

Description

If a single 'VCA'-object is passed, the first step is to call 'VCAinference' for CI estimation. For each variance component (VC) the result of the VCA is summarized and can be configured by arguments 'type', 'tail', 'ends', and 'conf.level'. These define which information is returned by this summary function. In case of passing a list of 'VCA'- or 'VCAinference'-objects, a matrix will be returned where columns correspond to list-elements, usually samples, and rows to estimated values. This is done as the number of estimated values usually exceeds the number of samples.

Usage

summarize.VCA(
  object,
  type = c("sd", "cv"),
  tail = "one-sided",
  ends = "upper",
  conf.level = 0.95,
  DF = TRUE,
  as.df = FALSE,
  print = TRUE
)

Value

(matrix, data.frame) with VCA-results either with estimates in rows and sample(s) in columns, or vice versa

Arguments

object

(object) of class VCA or VCAinference or a list of these objects to be summarized.

type

(character) "sd" for standard devation, "cv" for coefficient of variation, and "vc" for variance defining on which scale results shall be returned. Multiple can be specified.

tail

(character) "one-sided" for one-sided CI, "two-sided" for two-sided CI, can be abbreviated

ends

(character) "upper" or "lower" bounds of a e.g. 95% CI, can be both

conf.level

(numeric) confidence level of the CI

DF

(logical) TRUE to include degrees of freedom, FALSE to omit them

as.df

(logical) TRUE to transpose the returned object and convert into a data.frame, FALSE leve

print

(logical) TRUE print summary, FALSE omit printing and just return matrix or data.frame

Author

Andre Schuetzenmeister

Examples

if (FALSE) {
data(CA19_9)
fit.all <- anovaVCA(result~site/day, CA19_9, by="sample")
summarize.VCA(fit.all)
# complete set of results
summarize.VCA(	fit.all, type=c("vc", "sd", "cv"), tail=c("one", "two"),
				ends=c("lower", "upper"))
# summarizing a single VCA-object
summarize.VCA(fit.all[[1]])

### summarizing list of 'VCAinference' objects
infs <- VCAinference(fit.all)
summarize.VCAinference(infs)
}