Getting Started with apastats2

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apastats2 provides helpers for formatting statistical results in APA style. This vignette shows a minimal workflow using base datasets and core functions.

Format test results

library(apastats2)
#> Loading required package: ggplot2
#> Loading required package: data.table
#> 
#> Attaching package: 'data.table'
#> The following object is masked from 'package:base':
#> 
#>     %notin%

set.seed(1)
t_res <- t.test(rnorm(20, mean = 10, sd = 2))
t_str <- apa(t_res)
t_str
#> [1] "_t_(19.0) = 25.42, _p_ < .001"

Inline rendering example: One-sample test result was t(19.0) = 25.42, p < .001.

data("sleep")
mt_str <- apa_mean_and_t(sleep$extra, sleep$group, which.mean = 3, paired = TRUE)
mt_str
#> [1] "_M_ = 0.75 [-0.18, 1.74] vs. _M_ = 2.33 [1.24, 3.54], _t_(9.0) = -4.06, _p_ = .003"

Inline rendering example: Sleep increase comparison was M = 0.75 [-0.18, 1.74] vs. M = 2.33 [1.24, 3.54], t(9.0) = -4.06, p = .003.

set.seed(2)
x <- rnorm(40)
y <- 0.5 * x + rnorm(40, sd = 0.8)
cor_str <- apa(cor.test(x, y))
cor_str
#> [1] "_r_(38) = 0.41, _p_ = .009"

Inline rendering example: Correlation result was r(38) = 0.41, p = .009.

tbl <- matrix(c(30, 20, 15, 35), nrow = 2, byrow = TRUE)
chi_str <- apa(chisq.test(tbl))
chi_str
#> [1] "$\\chi^2$(1, _N_ = 100) = 7.92, _p_ = .005, _V_ = .28"

Inline rendering example: Chi-square result was \(\chi^2\)(1, N = 100) = 7.92, p = .005, V = .28.

Format model terms

You can format selected terms from linear or generalized models.

glm_res <- glm(Freq ~ (Age + Sex) * Survived, family = poisson, data = data.frame(Titanic))
glm_str <- apa(glm_res, term = "SexFemale:SurvivedYes", dtype = 3)
glm_str
#> [1] "_B_ = 2.32, _SE_ = 0.12, _Z_ =  19.38, _p_ < .001"

Inline rendering example: GLM term result was B = 2.32, SE = 0.12, Z = 19.38, p < .001.

Adjusted confidence intervals

get_adjusted_ci() computes summary statistics and interval columns for between-subject, within-subject, and mixed designs. The within-subject Cousineau-Morey implementation is adapted from the superb framework, but apastats2 implements it internally and does not require superb.

data(memory_noise)

# Between-subject intervals: standard vs high-variability sample
target_higher <- memory_noise[
  memory_noise$relative_noise == "target more noisy",
]
between_ci <- get_adjusted_ci(
  target_higher,
  value_var = "bias_percent",
  between = "experiment",
  wid = "participant"
)
between_ci[, c("experiment", "descr")]
#>   experiment                      descr
#> 1    Exp. 1A  _M_ = 2.18 [-8.82, 13.18]
#> 2    Exp. 1B _M_ = -9.33 [-19.11, 0.44]

# Within-subject Cousineau-Morey intervals
within_ci <- get_adjusted_ci(
  memory_noise[memory_noise$experiment == "Exp. 1A", ],
  value_var = "bias_percent",
  within = "relative_noise",
  wid = "participant"
)
within_ci[, c("relative_noise", "descr")]
#>      relative_noise                     descr
#> 1 target less noisy _M_ = 3.08 [-6.60, 12.77]
#> 2 target more noisy _M_ = 2.18 [-7.51, 11.86]

# Mixed design
mixed_ci <- get_adjusted_ci(
  memory_noise,
  value_var = "bias_percent",
  within = "relative_noise",
  between = "experiment",
  wid = "participant"
)
mixed_ci[, c("experiment", "relative_noise", "descr")]
#>   experiment    relative_noise                       descr
#> 1    Exp. 1A target less noisy   _M_ = 3.08 [-6.60, 12.77]
#> 2    Exp. 1B target less noisy  _M_ = -1.77 [-10.01, 6.47]
#> 3    Exp. 1A target more noisy   _M_ = 2.18 [-7.51, 11.86]
#> 4    Exp. 1B target more noisy _M_ = -9.33 [-17.57, -1.09]

Point-range plotting

plot_pointrange() can be used directly for quick summaries.

plot_pointrange(
  memory_noise,
  aes(x = relative_noise, color = experiment, y = bias_percent),
  wid = "participant",
  within_subj = TRUE,
  withinvars = "relative_noise",
  betweenvars = "experiment",
  connecting_line = TRUE
) +
  labs(
    x = "Relative noise",
    y = "Bias toward non-target (%)",
    color = "Experiment"
  ) +
  scale_x_discrete(labels = c(
    "target less noisy" = "Target less noisy",
    "target more noisy" = "Target more noisy"
  ))
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?

Misc helpers

round_p(c(0.025, 0.0001, 0.568))
#> [1] "= .025" "< .001" "= .568"
mean_ci_str <- apa_mean_conf(c(1.1, 1.5, 0.9, 1.8, 1.2), bootCI = FALSE)
mean_ci_str
#> [1] "_M_ = 1.30 [0.86, 1.74]"

Inline rendering example: Mean and CI summary was M = 1.30 [0.86, 1.74].

See individual help pages for full argument details and supported model classes.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.