misty (version 0.3.2)

z.test: z-Test

Description

This function computes one sample, two sample, and paired sample z-test.

Usage

z.test(x, ...)
# S3 method for default
z.test(x, y = NULL, sigma = NULL, sigma2 = NULL, mu = 0, paired = FALSE,
       alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
       digits = 2, p.digits = 3, as.na = NULL, check = TRUE,
       output = TRUE, …)
# S3 method for formula
z.test(formula, data, as.na = NULL, check = TRUE, output = TRUE, …)

Arguments

x

a numeric vector of data values.

y

a numeric vector of data values.

sigma

a numeric vector indicating the population standard deviation(s). In case of two sample z-test, equal standard deviations are assumed when specifying one value for the argument sigma; when specifying two values for the argument sigma, unequal standard deviations are assumed. Note that either argument sigma or argument sigma2 is specified.

sigma2

a numeric vector indicating the population variance(s). In case of two sample z-test, equal variances are assumed when specifying one value for the argument sigma2; when specifying two values for the argument sigma, unequal variance are assumed. Note that either argument sigma or argument sigma2 is specified.

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 z-test.

paired

logical: if TRUE, paired sample z-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.

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 z-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

t.test, ci.mean.diff, ci.mean

Examples

# NOT RUN {
dat.bs <- 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, 4, 3, NA))

#--------------------------------------
# Between-Subject Design

# Two-sided one sample z-test with 95% confidence interval
# population mean = 3, population standard deviation = 1.2
z.test(dat.bs$x, sigma = 1.2, mu = 3)

# Two-sided one sample z-test with 95% confidence interval
# population mean = 3, population variance = 1.44
z.test(dat.bs$x, sigma2 = 1.44, mu = 3)

# One-sided one sample z-test with 95% confidence interval
# population mean = 3, population standard deviation = 1.2
z.test(dat.bs$x, sigma = 1.2, mu = 3, alternative = "greater")

# Two-sided one sample z-test with 95% confidence interval
# population mean = 3, population standard deviation = 1.2
# # convert value 3 to NA
z.test(dat.bs$x, sigma = 1.2, mu = 3, as.na = 3)

# Two-sided one sample z-test with 99% confidence interval
# population mean = 3, population standard deviation = 1.2
z.test(dat.bs$x, sigma = 1.2, mu = 3, conf.level = 0.99)

# Two-sided one sample z-test with 95% confidence interval
# population mean = 3, population standard deviation = 1.2
# print descriptive statistics with 3 digits and p-value with 5 digits
z.test(dat.bs$x, sigma = 1.2, mu = 3, digits = 3, p.digits = 5)

# Two-sided two sample z-test with 95% confidence interval
# population standard deviation (SD) = 1.2, equal SD assumption
z.test(x ~ group, sigma = 1.2, data = dat.bs)

# Two-sided two sample z-test with 95% confidence interval
# population standard deviation = 1.2 and 1.5
z.test(x ~ group, sigma = c(1.2, 1.5), data = dat.bs)

# Two-sided two sample z-test with 95% confidence interval
# population variance = 1.44 and 2.25
z.test(x ~ group, sigma = c(1.44, 2.25), data = dat.bs)

# One-sided two sample z-test with 95% confidence interval
# population standard deviation (SD) = 1.2, equal SD assumption
z.test(x ~ group, sigma = 1.2, data = dat.bs, alternative = "less")

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

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

# Two-sided two sample z-test with 95% confidence interval
# population standard deviation (SD) = 1.2, equal SD assumption
z.test(group1, group2, sigma = 1.2, data = dat.bs)

#--------------------------------------
# Within-Subject Design
dat.ws <- data.frame(pre = c(1, 3, 2, 5, 7),
                     post = c(2, 2, 1, 6, 8), stringsAsFactors = FALSE)

# Two-sided paired sample z-test with 95% confidence interval
# population standard deviation of difference score = 1.2
z.test(dat.ws$pre, dat.ws$post, sigma = 1.2, paired = TRUE)

# Two-sided paired sample z-test with 95% confidence interval
# population variance of difference score = 1.44
z.test(dat.ws$pre, dat.ws$post, sigma2 = 1.44, paired = TRUE)

# One-sided paired sample z-test with 95% confidence interval
# population standard deviation of difference score = 1.2
# }