A test on the discrepancy between two tasks in a single case, by comparison
to the mean of discrepancies of the same two tasks in a control sample. Use
only when the two tasks are measured on the same scale with the same
underlying distribution because no standardisation is performed on task
scores. As a rule-of-thumb, the UDT may be applicable to pairs of tasks for
which it would be sensible to perform a paired t-test within the control
group. Calculates however a standardised effect size in the same manner as
RSDT(). This is original behaviour from Crawford and Garthwaite
(2005) but might not be appropriate. So use this standardised effect size
with caution. Calculates a standardised effect size of task discrepancy as
well as a point estimate of the proportion of the control population that
would be expected to show a more extreme discrepancy and respective
confidence intervals.
UDT(
case_a,
case_b,
controls_a,
controls_b,
sd_a = NULL,
sd_b = NULL,
sample_size = NULL,
r_ab = NULL,
alternative = c("two.sided", "greater", "less"),
conf_int = TRUE,
conf_level = 0.95,
conf_int_spec = 0.01,
na.rm = FALSE
)A list with class "htest" containing the following components:
statistic | the t-statistic. | parameter | |
| the degrees of freedom for the t-statistic. | p.value | the p-value of the test. | |
estimate | unstandardised case scores, task difference and pont estimate of proportion control population expected to above or below the observed task difference. | ||
control.desc | named numerical with descriptive statistics of the control samples. | null.value | |
| the value of the difference under the null hypothesis. | alternative | a character string describing the alternative hypothesis. | |
method | a character string indicating what type of test was performed. |
Case's score on task A.
Case's score on task B.
Controls' scores on task A. Takes either a vector of observations or a single value interpreted as mean. Note: you can supply a vector as input for task A while mean and SD for task B.
Controls' scores on task B. Takes either a vector of observations or a single value interpreted as mean. Note: you can supply a vector as input for task B while mean and SD for task A.
If single value for task A is given as input you must supply the standard deviation of the sample.
If single value for task B is given as input you must supply the standard deviation of the sample.
If A or B is given as mean and SD you must supply the sample size. If controls_a is given as vector and controls_b as mean and SD, sample_size must equal the number of observations in controls_a.
If A and/or B is given as mean and SD you must supply the correlation between the tasks.
A character string specifying the alternative hypothesis,
must be one of "two.sided" (default), "greater" or
"less". You can specify just the initial letter. Since the direction
of the expected effect depends on which task is set as A and which is set
as B, be very careful if changing this parameter.
Initiates a search algorithm for finding confidence
intervals. Defaults to TRUE, set to FALSE for faster
calculation (e.g. for simulations).
Level of confidence for intervals, defaults to 95%.
The size of iterative steps for calculating confidence intervals. Smaller values gives more precise intervals but takes longer to calculate. Defaults to a specificity of 0.01.
Remove NAs from controls.
Running UDT is equivalent to running TD on discrepancy scores
making it possible to run unstandardised tests with covariates by applying
BTD_cov to discrepancy scores.
Crawford, J. R., & Garthwaite, P. H. (2005). Testing for Suspected Impairments and Dissociations in Single-Case Studies in Neuropsychology: Evaluation of Alternatives Using Monte Carlo Simulations and Revised Tests for Dissociations. Neuropsychology, 19(3), 318 - 331. tools:::Rd_expr_doi("10.1037/0894-4105.19.3.318")
UDT(-3.857, -1.875, controls_a = 0, controls_b = 0, sd_a = 1,
sd_b = 1, sample_size = 20, r_ab = 0.68)
UDT(case_a = size_weight_illusion[1, "V_SWI"], case_b = size_weight_illusion[1, "K_SWI"],
controls_a = size_weight_illusion[-1, "V_SWI"], controls_b = size_weight_illusion[-1, "K_SWI"])
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