apa_print.lm {papaja}

Typeset Statistical Results from GLM

Description

These methods take (generalized) linear model objects to create formatted character strings to report the results in accordance with APA manuscript guidelines.

Usage

## S3 method for class 'lm'
apa_print(
  x,
  est_name = NULL,
  standardized = FALSE,
  conf.int = 0.95,
  observed = TRUE,
  in_paren = FALSE,
  ...
)

## S3 method for class 'glm'
apa_print(
  x,
  est_name = NULL,
  standardized = FALSE,
  conf.int = 0.95,
  observed = TRUE,
  in_paren = FALSE,
  ...
)

## S3 method for class 'summary.glm'
apa_print(x, ...)

## S3 method for class 'summary.lm'
apa_print(x, ...)

Arguments

x

glm or lm object.

est_name

Character. If NULL (the default) the name given in x (or a formally correct adaptation, such as "b^*" instead of "b" for standardized regression coefficients) is used. Otherwise the supplied name is used. See details.

standardized

Logical. Indicates if coefficients were standardized (e.g., using scale()), and leading zeros should be omitted if appropriate. See details.

conf.int

Numeric. Either a single value (range [0, 1]) giving the confidence level or a two-column matrix with confidence region bounds as column names (e.g. "2.5 %" and "97.5 %") and coefficient names as row names (in the same order as they appear in summary(x)$coefficients. See details.

observed

Logical. Indicates whether predictor variables were observed. See details.

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 coefficient names are sanitized to facilitate their use as list names. Parentheses are omitted and other non-word characters are replaced by ⁠_⁠ (see sanitize_terms()).

est_name is placed in the output string and is then 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., "\\\\beta" yields \beta).

If standardized = TRUE, scale() is removed from coefficient names (see examples). This option is currently ignored for glm-objects.

If conf.int is a single value, confidence intervals are calculated using stats::confint().

If x is an lm object and the MBESS package is available, confidence intervals for R^2 are computed using MBESS::ci.R2() to obtain a confidence region that corresponds to the \alpha-level chosen for the confidence intervals of regression coefficients (e.g., 95% CI or \alpha = 0.05 for regression coefficients yields a 90% CI for R^2, see Steiger, 2004). If observed = FALSE, it is assumed that predictors are fixed variables, i.e., "the values of the [predictors] were selected a priori as part of the research design" (p. 15, Kelly, 2007); put differently, it is assumed that predictors are not random.

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().

References

Steiger (2004). Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis. Psychological Methods, 9(2), 164-182. doi: doi:10.1037/1082-989X.9.2.164

Kelley, K. (2007). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1-24. doi: doi:10.18637/jss.v020.i08

See Also

stats::confint(), MBESS::ci.pvaf()

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

Examples

# Data from Dobson (1990), p. 9.
ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61, 5.17, 4.53, 5.33, 5.14)
trt <- c(4.81, 4.17, 4.41, 3.59, 5.87, 3.83, 6.03, 4.89, 4.32, 4.69)
group <- gl(2, 10, 20, labels = c("Ctl", "Trt"))
weight <- c(ctl, trt)
lm_fit <- lm(weight ~ group)

apa_print(lm_fit)

trt <- rep(trt, 2) # More data is always better
ctl <- rep(ctl, 2)
lm_fit2 <- lm(scale(trt) ~ scale(ctl))

apa_print(lm_fit2, standardized = TRUE)

# It is possible to simplify the regression table with transmute_df_into_label():
transmute_df_into_label(apa_print(lm_fit2, standardized = TRUE))


# Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
d.AD <- data.frame(treatment, outcome, counts)
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())

apa_print(glm.D93)