Prop_test {lessR}

Analysis of Prop_test

Description

Abbreviation: prop

Analyze proportions, either of a single proportion against a fixed alternative, a set of proportions evaluated for equality, or a goodness-of-fit test for a single categorical variable or a test of independence for multiple variables.

Usage

Prop_test(variable=NULL, success=NULL, by=NULL, data=d,
          n_succ=NULL, n_fail=NULL, n_tot=NULL, n_table=NULL,
          Yates=FALSE, pi=NULL, digits_d=3, ...)

prop(...)

Arguments

variable

Numerical variable to analyze.

success

Value of variable considered a success.

by

Compare proportions over groups, the values of this categorical variable.

data

Data frame that contains the variable to analyze.

n_succ

Number of successes.

n_fail

Number of trials, either provide this or n.

n_tot

Number of trials, either provide this or q.

n_table

Path name of the file that contains a frequency table.

Yates

Set to TRUE to implement Yate's correction factor where applicable.

pi

Value of null hypothesized probability.

digits_d

Number of significant digits for each of the displayed summary statistics.

...

Parameter values passed to Prop_test.

Details

The analysis of proportions is of two primary types.

For one or more samples of data, focus on a single value of a categorical variable, traditionally called a success. Analyze the resulting proportion of occurrence for a single sample or compare proportions of occurrence of a success across distinct samples of data, what is called a test of homogeneity.

For a single sample, compare proportions from a contingency table. These tests are called a goodness-of-fit test for a single variable and a test of independence for multiple variables.

From standard base R functions, the lessR function Prop_test(), abbreviated prop(), provides for either type of the analysis for proportions. To use, enter either the original data from which the sample proportions are computed, or directly enter already computed sample frequencies from which the proportions are computed.

TEST OF HOMOGENEITY
When analyzing the original data, an entered value for the parameter success for the categorical variable of interest, indicated by parameter variable, triggers the test of homogeneity. For a single proportion the analysis is the exact binomial test. If the proportions are entered directly, indicate the number of successes and the total number of trials with the n_succ and n_tot parameters, each as a single value for a single sample or as vectors of multiple values for multiple samples.

TEST OF UNIFORM GOODNESS-OF-FIT
To test for goodness-of-fit from the original data, just enter the name of the categorical variable. To test from the proportions, specify the proportions as a vector with the n_tot parameter.

TEST OF INDEPENDENCE
Without a value for success or n_succ the analysis is of goodness-of-fit or independence. For the test of independence, to enter the joint frequency table directly, store the frequencies in a file accessible from your computer system. One possibility is to enter the numbers into a text file with file type '.csv' or '.txt'. Enter the numbers with a text editor, or with a word processor saving the file as a text file. With this file format, separate the adjacent values in each row with a comma, as indicated below. Or, enter the numbers into an MS Excel formatted file with file type '.xlsx'. Enter only the numeric frequencies, no labels. Use the parameter n_table to indicate the path name to the file, enclosed in quotes. Or, leave the quotes empty to browse for the joint frequency table.

To conduct the test from the data, enter the names of the two categorical variables. The variable listed first is the parameter 'variable'. The second listed variable is for the parameter 'by', the name of which must be included in the function call.

See the corresponding vignette for more detail and examples.
Enter browseVignettes("lessR").

Author(s)

David W. Gerbing (Portland State University; [e-mail])

See Also

binom.test.

Examples

# generate data
Classvalues <- c("Freshman", "Sophomore", "Junior", "Senior")
Goodvalues <- c("Nice", "OK", "Mean")
Class <- sample(Classvalues, size=250, replace=TRUE)
Goodness <- sample(Goodvalues, size=250, replace=TRUE)
d <- data.frame(Class, Goodness)

# Test a single proportion
Prop_test(variable=Goodness, success="Nice")

# Test multiple proportions, one each for each level of Plan
Prop_test(Goodness, "Nice", by=Class)

# Test of independence
Prop_test(Goodness, by=Class)

# Same example as for the base R binom.test
Prop_test(n_succ=682, n_fail=243, p=.75, digits_d=2)