misty (version 0.4.5)

test.t: t-Test

Description

This function performs one-sample, two-sample, and paired-sample t-tests.

Usage

test.t(x, ...)
# S3 method for default
test.t(x, y = NULL, mu = 0, paired = FALSE,
       alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
       hypo = TRUE, descript = TRUE, effsize = FALSE, weighted = TRUE,
       cor = TRUE, ref = NULL, correct = FALSE, digits = 2, p.digits = 4,
       as.na = NULL, check = TRUE, output = TRUE, ...)
# S3 method for formula
test.t(formula, data, alternative = c("two.sided", "less", "greater"),
       conf.level = 0.95, hypo = TRUE, descript = TRUE, effsize = FALSE,
       weighted = TRUE, cor = TRUE, ref = NULL, correct = FALSE, digits = 2,
       p.digits = 4, as.na = NULL, check = TRUE, output = TRUE, ...)

Arguments

x

a numeric vector of data values.

y

a numeric vector of data values.

mu

a numeric value indicating the population mean under the null hypothesis. Note that the argument mu is only used when computing a one sample t-test.

paired

logical: if TRUE, paired-samples t-test is computed.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

conf.level

a numeric value between 0 and 1 indicating the confidence level of the interval.

hypo

logical: if TRUE, null and alternative hypothesis are shown on the console.

descript

logical: if TRUE, descriptive statistics are shown on the console.

effsize

logical: if TRUE, effect size measure Cohen's d is shown on the console, see cohens.d function.

weighted

logical: if TRUE (default), the weighted pooled standard deviation is used to compute Cohen's d for a two-sample design (i.e., paired = FALSE), while standard deviation of the difference scores is used to compute Cohen's d for a paired-sample design (i.e., paired = TRUE).

cor

logical: if TRUE (default), paired = TRUE, and weighted = FALSE, Cohen's d for a paired-sample design while controlling for the correlation between the two sets of measurement is computed. Note that this argument is only used in a paired-sample design (i.e., paired = TRUE) when specifying weighted = FALSE.

ref

character string "x" or "y" for specifying the reference reference group when using the default test.t() function or a numeric value or character string indicating the reference group in a two-sample design when using the formula test.t() function. The standard deviation of the reference variable or reference group is used to standardized the mean difference to compute Cohen's d. Note that this argument is only used in a two-sample design (i.e., paired = FALSE).

correct

logical: if TRUE, correction factor to remove positive bias in small samples is used.

digits

an integer value indicating the number of decimal places to be used for displaying descriptive statistics and confidence interval.

p.digits

an integer value indicating the number of decimal places to be used for displaying the p-value.

as.na

a numeric vector indicating user-defined missing values, i.e. these values are converted to NA before conducting the analysis.

check

logical: if TRUE, argument specification is checked.

output

logical: if TRUE, output is shown on the console.

formula

in case of two sample t-test (i.e., paired = FALSE), a formula of the form y ~ group where group is a numeric variable, character variable or factor with two values or factor levels giving the corresponding groups.

data

a matrix or data frame containing the variables in the formula formula.

...

further arguments to be passed to or from methods.

Value

Returns an object of class misty.object, which is a list with following entries: function call (call), type of analysis type, list with the input specified in x (data), specification of function arguments (args), and result table (result).

References

Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. John Wiley & Sons.

See Also

test.welch, test.z, test.levene, cohens.d, ci.mean.diff, ci.mean

Examples

# NOT RUN {
dat1 <- data.frame(group = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2),
                   x = c(3, 1, 4, 2, 5, 3, 2, 3, 6, 6, 3, NA))

#--------------------------------------
# One-Sample Design

# Two-sided one-sample t-test
# population mean = 3
test.t(dat1$x, mu = 3)

# One-sided one-sample t-test
# population mean = 3, population standard deviation = 1.2
test.t(dat1$x, mu = 3, alternative = "greater")

# Two-sided one-sample t-test
# population mean = 3, convert value 3 to NA
test.t(dat1$x, mu = 3, as.na = 3)

# Two-sided one-sample t-test
# population mean = 3, print Cohen's d
test.t(dat1$x, sigma = 1.2, mu = 3, effsize = TRUE)

# Two-sided one-sample t-test
# population mean = 3, print Cohen's d with small sample correction factor
test.t(dat1$x, sigma = 1.2, mu = 3, effsize = TRUE, correct = TRUE)

# Two-sided one-sample t-test
# population mean = 3,
# do not print hypotheses and descriptive statistics
test.t(dat1$x, sigma = 1.2, mu = 3, hypo = FALSE, descript = FALSE)

# Two-sided one-sample t-test
# print descriptive statistics with 3 digits and p-value with 5 digits
test.t(dat1$x,  mu = 3, digits = 3, p.digits = 5)

#--------------------------------------
# Two-Sample Design

# Two-sided two-sample t-test
test.t(x ~ group, data = dat1)

# One-sided two-sample t-test
test.t(x ~ group, data = dat1, alternative = "greater")

# Two-sided two-sample t-test
# print Cohen's d with weighted pooled SD
test.t(x ~ group, data = dat1, effsize = TRUE)

# Two-sided two-sample t-test
# print Cohen's d with unweighted pooled SD
test.t(x ~ group, data = dat1, effsize = TRUE, weighted = FALSE)

# Two-sided two-sample t-test
# print Cohen's d with weighted pooled SD and
# small sample correction factor
test.t(x ~ group, data = dat1, effsize = TRUE, correct = TRUE)

# Two-sided two-sample t-test
# print Cohen's d with SD of the reference group 1
test.t(x ~ group, data = dat1, effsize = TRUE,
       ref = 1)

# Two-sided two-sample t-test
# print Cohen's d with weighted pooled SD and
# small sample correction factor
test.t(x ~ group, data = dat1, effsize = TRUE,
       correct = TRUE)

# Two-sided two-sample t-test
# do not print hypotheses and descriptive statistics,
test.t(x ~ group, data = dat1, descript = FALSE, hypo = FALSE)

# Two-sided two-sample t-test
# print descriptive statistics with 3 digits and p-value with 5 digits
test.t(x ~ group, data = dat1, digits = 3, p.digits = 5)

#-----------------

group1 <- c(3, 1, 4, 2, 5, 3, 6, 7)
group2 <- c(5, 2, 4, 3, 1)

# Two-sided two-sample t-test
test.t(group1, group2)

#--------------------------------------
# Paired-Sample Design

dat2 <- data.frame(pre = c(1, 3, 2, 5, 7),
                   post = c(2, 2, 1, 6, 8), stringsAsFactors = FALSE)

# Two-sided paired-sample t-test
test.t(dat2$pre, dat2$post, paired = TRUE)

# One-sided paired-sample t-test
test.t(dat2$pre, dat2$post, paired = TRUE,
       alternative = "greater")

# Two-sided paired-sample t-test
# convert value 1 to NA
test.t(dat2$pre, dat2$post, as.na = 1, paired = TRUE)

# Two-sided paired-sample t-test
# print Cohen's d based on the standard deviation of the difference scores
test.t(dat2$pre, dat2$post, paired = TRUE, effsize = TRUE)

# Two-sided paired-sample t-test
# print Cohen's d based on the standard deviation of the difference scores
# with small sample correction factor
test.t(dat2$pre, dat2$post, paired = TRUE, effsize = TRUE,
       correct = TRUE)

# Two-sided paired-sample t-test
# print Cohen's d controlling for the correlation between measures
test.t(dat2$pre, dat2$post, paired = TRUE, effsize = TRUE,
       weighted = FALSE)

# Two-sided paired-sample t-test
# print Cohen's d controlling for the correlation between measures
# with small sample correction factor
test.t(dat2$pre, dat2$post, paired = TRUE, effsize = TRUE,
       weighted = FALSE, correct = TRUE)

# Two-sided paired-sample t-test
# print Cohen's d ignoring the correlation between measures
test.t(dat2$pre, dat2$post, paired = TRUE, effsize = TRUE,
       weighted = FALSE, cor = FALSE)

# Two-sided paired-sample t-test
# do not print hypotheses and descriptive statistics
test.t(dat2$pre, dat2$post, paired = TRUE, hypo = FALSE, descript = FALSE)

# Two-sided paired-sample t-test
# population standard deviation of difference score = 1.2
# print descriptive statistics with 3 digits and p-value with 5 digits
test.t(dat2$pre, dat2$post, paired = TRUE, digits = 3,
       p.digits = 5)
# }