t_test {mixlm}

Text book versions of t-tests and z-tests.

Description

Adaptations of base t.test to better confrom to text book standards. t_test_sum and z_test_sum takes summarized data as input.

Usage

t_test(x, ...)
z_test(x, ...)

## Default S3 method:
t_test(x, y = NULL, alternative = c("two.sided", "less", "greater"),
	mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95, ...)
## Default S3 method:
z_test(x, y = NULL, alternative = c("two.sided", "less", "greater"),
	mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95, sds = NULL, ...)

## S3 method for class 'formula'
t_test(formula, data, subset, na.action, ...)
## S3 method for class 'formula'
z_test(formula, data, subset, na.action, ...)

## Function for summarized data:
t_test_sum(means, sds, ns, alternative = c("two.sided", "less", "greater"),
	mu = 0, var.equal = FALSE, conf.level = 0.95, z.test = FALSE, ...)
z_test_sum(means, sds, ns, alternative = c("two.sided", "less", "greater"),
	mu = 0, var.equal = FALSE, conf.level = 0.95, z.test = TRUE, ...)

Arguments

x

a (non-empty) numeric vector of data values.

y

an optional (non-empty) numeric vector of data values.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

mu

a number indicating the true value of the mean (or difference in means if you are performing a two sample test).

paired

a logical indicating whether you want a paired t-test.

var.equal

a logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.

conf.level

confidence level of the interval.

formula

a formula of the form lhs ~ rhs where lhs is a numeric variable giving the data values and rhs a factor with two levels giving the corresponding groups.

data

an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).

subset

an optional vector specifying a subset of observations to be used.

na.action

a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action").

means

means of groups.

sds

standard deviations of groups.

ns

number of objects in groups.

z.test

normal approximation.

...

further arguments to be passed to or from methods.

Details

The formula interface is only applicable for the 2-sample tests.

alternative = "greater" is the alternative that x has a larger mean than y.

If paired is TRUE then both x and y must be specified and they must be the same length. Missing values are silently removed (in pairs if paired is TRUE). If var.equal is TRUE then the pooled estimate of the variance is used. By default, if var.equal is FALSE then the variance is estimated separately for both groups and the Welch modification to the degrees of freedom is used.

If the input data are effectively constant (compared to the larger of the two means) an error is generated.

Value

A list with class "htest" containing the following components:

statistic

the value of the t-statistic.

parameter

the degrees of freedom for the t-statistic.

p.value

the p-value for the test.

conf.int

a confidence interval for the mean appropriate to the specified alternative hypothesis.

estimate

the estimated mean or difference in means depending on whether it was a one-sample test or a two-sample test.

null.value

the specified hypothesized value of the mean or mean difference depending on whether it was a one-sample test or a two-sample test.

alternative

a character string describing the alternative hypothesis.

method

a character string indicating what type of t-test was performed.

data.name

a character string giving the name(s) of the data.

See Also

prop.test

Examples

t.test(1:10,y=c(7:20))      # P = .00001855
t.test(1:10,y=c(7:20, 200)) # P = .1245    -- NOT significant anymore

## Classical example: Student's sleep data
plot(extra ~ group, data = sleep)
## Traditional interface
with(sleep, t.test(extra[group == 1], extra[group == 2]))
## Formula interface
t_test(extra ~ group, data = sleep)