Description Usage Arguments Value Note References Examples
View source: R/HPSESfunctions.R
Calculates the HPS effect size estimator based on data from a multiple baseline design, as described in Hedges, Pustejovsky, & Shadish (2013). Note that the data must contain one row per measurement occasion per subject.
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outcome 
vector of outcome data or name of variable within 
treatment 
vector of treatment indicators or name of variable within 
id 
factor vector indicating unique cases or name of variable within 
time 
vector of measurement occasion times or name of variable within 
data 
(Optional) dataset to use for analysis. Must be data.frame. 
phi 
(Optional) value of the autocorrelation nuisance parameter, to be used in calculating the smallsample adjusted effect size 
rho 
(Optional) value of the intraclass correlation nuisance parameter, to be used in calculating the smallsample adjusted effect size 
A list with the following components
g_dotdot  total number of nonmissing observations 
K  number of timebytreatment groups containing at least one observation 
D_bar  numerator of effect size estimate 
S_sq  sample variance, pooled across time points and treatment groups 
delta_hat_unadj  unadjusted effect size estimate 
phi  corrected estimate of firstorder autocorrelation 
sigma_sq_w  corrected estimate of withincase variance 
rho  estimated intraclass correlation 
theta  estimated scalar constant 
nu  estimated degrees of freedom 
delta_hat  corrected effect size estimate 
V_delta_hat  estimated variance of delta_hat

If phi or rho is left unspecified (or both), estimates for the nuisance parameters will be calculated.
Hedges, L. V., Pustejovsky, J. E., & Shadish, W. R. (2013). A standardized mean difference effect size for multiple baseline designs across individuals. Research Synthesis Methods, 4(4), 324341. doi: 10.1002/jrsm.1086
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