quest (version 0.2.0)

n_compare: Test for Equal Frequency of Values (chi-square test of goodness of fit)

Description

n_compare tests whether all the values for a variable have equal frequency with a chi-square test of goodness of fit. n_compare does not currently allow for user-specified unequal frequencies of values; this is possible with chisq.test. The function also calculates the counts and overall percentages for the value frequencies. prop_test is simply a wrapper for chisq.test plus some extra calculations.

Usage

n_compare(x, simulate.p.value = FALSE, B = 2000)

Value

list of numeric vectors containing statistical information about the frequency comparison: 1) nhst = chi-square test of goodness of fit stat info in a numeric vector, 2) count = numeric vector of length 3 with table of counts, 3) percent = numeric vector of length 3 with table of overall percentages

1) nhst = chi-square test of goodness of fit stat info in a numeric vector

diff_avg

average difference in subsample sizes (i.e., |ni - nj|)

se

NA (to remind the user there is no standard error for the test)

X2

chi-square value

df

degrees of freedom (# of unique values = 1)

p

two-sided p-value

2) count = numeric vector of length 3 with table of counts with an additional element for the total. The names are 1. "n_`lvl[k]`", 2. "n_`lvl[j]`", 3. "n_`lvl[i]`", ..., X = "total"

3) percent = numeric vector of length 3 with table of overall percentages with an additional element for the total. The names are 1. "n_`lvl[k]`", 2. "n_`lvl[j]`", 3. "n_`lvl[i]`", ..., X = "total"

Arguments

x

atomic vector. Probably makes sense to contain relatively few unique values.

simulate.p.value

logial vector of length 1 specifying whether the p-value should be based on a Monte Carlo simulation rather than the classic formula. See chisq.test for details.

B

integer vector of length 1 specifying how much Monte Carlo simulations run. Only used if simulate.p.value = TRUE. See chisq.test for details.

See Also

chisq.test the workhorse for n_compare, props_test for multiple dummy variables, prop_diff for chi-square test of independence,

Examples

n_compare(mtcars$"cyl")
n_compare(mtcars$"gear")
n_compare(mtcars$"cyl", simulate.p.value = TRUE)

# compare to chisq.test()
n_compare(mtcars$"cyl")
chisq.test(table(mtcars$"cyl"))