relevance (version 2.1)

twosamples: Relevance and Significance for One or Two Samples

Description

Inference for a difference between two independent samples or for a single sample: Collect quantities for inference, including Relevance and Significance measures

Usage

twosamples(x, ...)
onesample(x, ...)
# S3 method for default
twosamples(x, y = NULL, paired = FALSE, table = NULL, 
  hypothesis = 0,var.equal = TRUE,
  testlevel=getOption("testlevel"), log = NULL, standardize = NULL, 
  rlv.threshold=getOption("rlv.threshold"), ...)
# S3 method for formula
twosamples(x, data = NULL, subset, na.action, log = NULL, ...)
# S3 method for table
twosamples(x, ...)

Value

an object of class

'inference', a vector with elements

effect:

for quantitative data: estimated difference between expectations of the two samples, or mean in case of a single sample.

For binary data: log odds (for one sample or paired samples) or log odds ratio (for two samples)

se:

standard error of effect

teststatistic:

test statistic

p.value:

p value for test against 0

Sig0:

significance measure for test or 0 effect

ciLow, ciUp:

confidence interval for the effect

Rle, Rls, Rlp:

relevance measures: estimated, secured, potential

Sigth:

significance measure for test of effect == relevance threshold

In addition to the columns/components, it has attributes

type:

type of relevance: simple

method:

problem and inference method

effectname:

label for the effect

hypothesis:

the null effect

n:

number(s) of observations

estimate:

estimated parameter, with standard error or confidence interval, if applicable; in the case of 2 independent samples: their means

teststatistic:

test statistic

V:

single observation variance

df:

degrees of freedom for the t distribution

data:

if paired, vector of differences; if single sample, vector of data; if two independent samples, list containing the two samples

rlv.threshold:

relevance threshold

Arguments

x

a formula or the data for the first or the single sample

y

data for the second sample

table

A table summarizing the data in case of binary (binomial) data. If given, x and y are ignored.

paired

logical: In case x and y are given. are their values paired?

hypothesis

the null effect to be tested, and anchor for the relevance

var.equal

logical: In case of two samples, should the variances be assumed equal? Only applies for quantitative data.

testlevel

level for the test, also determining the confidence level

log

logical...: Is the target variable on log scale? -- or character: either "log" or "log10" (or "logst"). If so, no standardization is applied to it. By default, the function examines the formula to check whether the left hand side of the formula contains a log transformation.

standardize

logical: Should the effect be standardized (for quantiative data)?

rlv.threshold

Relevance threshold, or a vector of thresholds from which the element stand is taken for quantitative data and the element prop, for binary data.

For the formula method:

formula

formula of the form y~x giving the target y and condition x variables. For a one-sample situation, use y~1.

data

data from which the variables are obtained

subset, na.action

subset and na.action to be applied to data

...

further arguments, ignored

Author

Werner A. Stahel

Details

Argument log: If log10 (or logst from package plgraphics) is used, rescaling is done (by log(10)) to obtain the correct relevance. Therefore, log needs to be set appropriately in this case.

References

see those in relevance-package.

See Also

t.test, binom.test, fisher.test, mcnemar.test

Examples

data(sleep)
t.test(sleep[sleep$group == 1, "extra"], sleep[sleep$group == 2, "extra"])
twosamples(sleep[sleep$group == 1, "extra"], sleep[sleep$group == 2, "extra"])

## Two-sample test, wilcox.test example,  Hollander & Wolfe (1973), 69f.
## Permeability constants of the human chorioamnion (a placental membrane)
## at term and between 12 to 26 weeks gestational age
d.permeabililty <-
  data.frame(perm = c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46,
                      1.15, 0.88, 0.90, 0.74, 1.21), atterm = rep(1:0, c(10,5))
             )
t.test(perm~atterm, data=d.permeabililty)
twosamples(perm~atterm, data=d.permeabililty)

## one sample
onesample(sleep[sleep$group == 2, "extra"])

## plot two samples
pltwosamples(extra ~ group, data=sleep)