psycho (version 0.5.0)

crawford.test: Crawford-Garthwaite (2007) Bayesian test for single-case analysis.

Description

Neuropsychologists often need to compare a single case to a small control group. However, the standard two-sample t-test does not work because the case is only one observation. Crawford and Garthwaite (2007) demonstrate that the Bayesian test is a better approach than other commonly-used alternatives. .

Usage

crawford.test(
  patient,
  controls = NULL,
  mean = NULL,
  sd = NULL,
  n = NULL,
  CI = 95,
  treshold = 0.1,
  iter = 10000,
  color_controls = "#2196F3",
  color_CI = "#E91E63",
  color_score = "black",
  color_size = 2,
  alpha_controls = 1,
  alpha_CI = 0.8
)

Arguments

patient

Single value (patient's score).

controls

Vector of values (control's scores).

mean

Mean of the control sample.

sd

SD of the control sample.

n

Size of the control sample.

CI

Credible interval bounds.

treshold

Significance treshold.

iter

Number of iterations.

color_controls

Color of the controls distribution.

color_CI

Color of CI distribution.

color_score

Color of the line representing the patient's score.

color_size

Size of the line representing the patient's score.

alpha_controls

Alpha of the CI distribution.

alpha_CI

lpha of the controls distribution.

Details

The p value obtained when this test is used to test significance also simultaneously provides a point estimate of the abnormality of the patient<U+2019>s score; for example if the one-tailed probability is .013 then we know that the patient<U+2019>s score is significantly (p < .05) below the control mean and that it is estimated that 1.3

Examples

# NOT RUN {
library(psycho)

crawford.test(patient = 125, mean = 100, sd = 15, n = 100)
plot(crawford.test(patient = 80, mean = 100, sd = 15, n = 100))

crawford.test(patient = 10, controls = c(0, -2, 5, 2, 1, 3, -4, -2))
test <- crawford.test(patient = 7, controls = c(0, -2, 5, -6, 0, 3, -4, -2))
plot(test)
# }