Test of proportions with improved layout

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
)

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.

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.)

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.

See also

prop.test

Examples

# 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)
#> 
#> Call:
#> proptest(var1 = factor(inrem), by = bssworst)
#> 
#> Two-sample proportion test (approximate) :
#>  
#>           Group Obs Missing      Mean Std. Err.         95% CI
#>    bssworst = 0  18       0       0.5     0.118 [0.269, 0.731]
#>    bssworst = 1  30       0 0.1666667     0.068  [0.0333, 0.3]
#>      Difference  48       0 0.3333333     0.136  [0.0666, 0.6]
#> Summary:
#> 
#>  Ho: Difference in proportions = 0 
#>  Ha: Difference in proportions != 0 
#>  Z = 2.46 
#>  p.value = 0.0139