apa_print.htest {papaja}

Typeset Statistical Results from Hypothesis Tests

Description

Takes htest objects from various statistical methods (e.g., t.test(), wilcox.test(), cor.test()) to create formatted character strings to report the results in accordance with APA manuscript guidelines.

Usage

## S3 method for class 'htest'
apa_print(
  x,
  stat_name = NULL,
  est_name = NULL,
  n = NULL,
  conf.int = NULL,
  in_paren = FALSE,
  ...
)

Arguments

x

An htest object. See details.

stat_name

Character. If NULL (the default), the name given in x (or a formally correct adaptation, such as \chi^2 instead of "x-squared") is used for the test statistic, otherwise the supplied name is used. See details.

est_name

Character. If NULL (the default), the name given in x (or a formally correct adaptation, such as r_S instead of "rho") is used for the estimate, otherwise the supplied name is used. See details.

n

Numeric. Sample size; required when reporting \chi^2 tests, otherwise this parameter is ignored.

conf.int

Numeric. If NULL (the default), the function tries to obtain confidence intervals from x. Other confidence intervals can be supplied as a vector of length 2 (lower and upper boundary, respectively) with attribute conf.level set, e.g., when calculating bootstrapped confidence intervals.

in_paren

Logical. Whether the formatted string is to be reported in parentheses. If TRUE, parentheses in the formatted string (e.g., those enclosing degrees of freedom) are replaced with brackets.

...

Arguments passed on to apa_num

Details

The function should work on a wide range of htest objects. Due to the large number of functions that produce these objects and their idiosyncrasies, the returned strings should be compared to the original object. If you experience inaccuracies you may report these [here]https://github.com/crsh/papaja/issues (please include a reproducible example in your report).

stat_name and est_name are placed in the output string and are thus passed to pandoc or LaTeX through knitr. Thus, to the extent it is supported by the final document type, you can pass LaTeX-markup to format the final text (e.g., \\tau yields \tau).

Value

apa_print()-methods return a named list of class apa_results containing the following elements:

estimate

One or more character strings giving point estimates, confidence intervals, and confidence level. A single string is returned in a vector; multiple strings are returned as a named list. If no estimate is available the element is NULL.

statistic

One or more character strings giving the test statistic, parameters (e.g., degrees of freedom), and p-value. A single string is returned in a vector; multiple strings are returned as a named list. If no estimate is available the element is NULL.

full_result

One or more character strings comprised 'estimate' and 'statistic'. A single string is returned in a vector; multiple strings are returned as a named list.

table

A data.frame of class apa_results_table that contains all elements of estimate and statistics. This table can be passed to apa_table() for reporting.

Column names in apa_results_table are standardized following the broom glossary (e.g., term, estimate conf.int, statistic, df, df.residual, p.value). Additionally, each column is labelled (e.g., $\hat{\eta}^2_G$ or $t$) using the tinylabels package and these labels are used as column names when an apa_results_table is passed to apa_table().

See Also

Other apa_print: apa_print.BFBayesFactor(), apa_print.aov(), apa_print.emmGrid(), apa_print.glht(), apa_print.list(), apa_print.lme(), apa_print.lm(), apa_print.merMod(), apa_print()

Examples

# Comparisons of central tendencies
t_stat <- t.test(extra ~ group, data = sleep)
apa_print(t_stat)
apa_print(t_stat, stat_name = "tee")

wilcox_stat <- wilcox.test(extra ~ group, data = sleep, exact = FALSE)
apa_print(wilcox_stat)

# Correlations
## Data from Hollander & Wolfe (1973), p. 187f.
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6,  3.1,  2.5,  5.0,  3.6,  4.0,  5.2,  2.8,  3.8)
cor_stat <- cor.test(x, y, method = "spearman")
apa_print(cor_stat)

# Contingency tables
## Data from Fleiss (1981), p. 139.
smokers  <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
prop_stat <- prop.test(smokers, patients)
apa_print(prop_stat, n = sum(patients))