proptest: Test of proportions with improved layout

Description

Performs a one- or two-sample test of proportions using data. This test can be approximate or exact.

Usage

proptest(
  var1,
  var2 = NULL,
  by = NULL,
  exact = FALSE,
  null.hypoth = ifelse(is.null(var2) && is.null(by), 0.5, 0),
  alternative = "two.sided",
  conf.level = 0.95,
  correct = FALSE,
  more.digits = 0
)

Value

A list of class proptest. The print method lays out the information in an easy-to-read format.

tab: A formatted table of descriptive and inferential results (total number of observations, number of missing observations, sample proportion, standard error of the proportion estimate), along with a confidence interval for the underlying proportion.
zstat: the value of the test statistic, if using an approximate test.
pval: the p-value for the test
var1: The user-supplied first data vector.
var2: The user-supplied second data vector.
by: The user-supplied stratification variable.
par: A vector of information about the type of test (null hypothesis, alternative hypothesis, etc.)

Arguments

var1: a (non-empty) vector of binary numeric (0-1), binary factor, or logical data values
var2: an optional (non-empty) vector of binary numeric (0-1), binary factor, or logical data values
by: a variable of equal length to that of var1 with two outcomes (numeric or factor). This will be used to define strata for a prop test on var1.
exact: If true, performs a test of equality of proportions using exact binomial probabilities.
null.hypoth: a number specifying the null hypothesis for the mean (or difference in means if performing a two-sample test). Defaults to 0.5 for a one-sample test and 0 for a two-sample test.
alternative: a string: one of "less", "two.sided", or "greater" specifying the form of the test. Defaults to a two-sided test.
conf.level: confidence level of the test. Defaults to 0.95.
correct: a logical indicating whether to perform a continuity correction
more.digits: a numeric value specifying whether or not to display more or fewer digits in the output. Non-integers are automatically rounded down.

Details

Missing values must be given by "NA"s to be recognized as missing values. Numeric data must be given in 0-1 form. This function also accepts binary factor variables, treating the higher level as 1 and the lower level as 0, or logical variables.

Examples

Run this code


# Read in data set
data(psa)
attach(psa)

# Define new binary variable as indicator
# of whether or not bss was worst possible
bssworst <- bss
bssworst[bss == 1] <- 0
bssworst[bss == 2] <- 0
bssworst[bss == 3] <- 1


# Perform test comparing proportion in remission
# between bss strata
proptest(factor(inrem), by = bssworst)