Description Usage Arguments Details Value References Examples
Takes a single observation and compares it to a distribution estimated by a
control sample, while controlling for the effect of covariates, using
Bayesian methodology. This test is used when assessing a case conditioned on
some other variable, for example, assessing abnormality when controlling for
years of education or sex. Under the null hypothesis the case is an
observation from the distribution of scores from the task of interest coming
from observations having the same score as the case on the covariate(s).
Returns a significance test, point and interval estimates of difference
between the case and the mean of the controls as well as point and interval
estimates of abnormality, i.e. an estimation of the proportion of controls
that would exhibit a more extreme conditioned score. This test is based on
random number generation which means that results may vary between runs. This
is by design and the reason for not using set.seed()
to reproduce
results inside the function is to emphasise the randomness of the test. To
get more accurate and stable results please increase the number of iterations
by increasing iter
whenever feasible. Developed by Crawford,
Garthwaite and Ryan (2011).
1 2 3 4 5 6 7 8 9 10 11 12 
case_task 
The case score from the task of interest. Must be a single value. 
case_covar 
A vector containing the case scores on all covariates
included. Can be of any length except 0, in that case use

control_task 
A vector containing the scores from the controls on the task of interest. Or a vector of length 2 containing the mean and standard deviation of the task. In that order. 
control_covar 
A vector, matrix or dataframe containing the control scores on the covariates included. If matrix or dataframe each column represents a covariate. Or a matrix or dataframe containing summary statistics where the first column represents the means for each covariate and the second column represents the standard deviation. 
alternative 
A character string specifying the alternative hypothesis,
must be one of 
int_level 
The probability level on the Bayesian credible intervals, defaults to 95%. 
iter 
Number of iterations to be performed. Greater number gives better estimation but takes longer to calculate. Defaults to 10000. 
use_sumstats 
If set to 
cor_mat 
A correlation matrix of all variables included. NOTE: the first variable should be the task of interest. 
sample_size 
An integer specifying the sample size of the controls. 
Uses random generation of inverse wishart distributions from the CholWishart package (Geoffrey Thompson, 2019).
A list with class "htest"
containing the following components:
statistic  the average zvalue over
iter number of iterations. 
parameter  the degrees of freedom used to specify the posterior distribution. 
p.value  the average pvalue over iter number of
iterations. 
estimate  case scores expressed as zscores on task X and Y. Standardised effect size (ZCCC) of task difference between case and controls and point estimate of the proportion of the control population estimated to show a more extreme task difference. 
null.value  the value of the difference between tasks under the null hypothesis. 
interval  named numerical vector containing level of confidence and confidence intervals for both effect size and pvalue. 
desc  data frame containing means and standard deviations for controls as well as case scores. 
cor.mat  matrix giving the correlations between the task of interest and the covariates included. 
sample.size  number of controls.. 
alternative  a character string describing the alternative hypothesis. 
method  a character string indicating what type of test was performed. 
data.name
 a character string giving the name(s) of the data 
Crawford, J. R., Garthwaite, P. H., & Ryan, K. (2011). Comparing a single case to a control sample: Testing for neuropsychological deficits and dissociations in the presence of covariates. Cortex, 47(10), 11661178. doi: 10.1016/j.cortex.2011.02.017
Geoffrey Thompson (2019). CholWishart: Cholesky Decomposition of the Wishart Distribution. R package version 1.1.0. https://CRAN.Rproject.org/package=CholWishart
1 2 3 4  BTD_cov(case_task = size_weight_illusion[1, "V_SWI"],
case_covar = size_weight_illusion[1, "YRS"],
control_task = size_weight_illusion[1, "V_SWI"],
control_covar = size_weight_illusion[1, "YRS"], iter = 100)

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