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>qol.scores <- c(45, 80, 70, 70) # QoL data
>single.case(qol.scores, baseline = 1, level = .05) # Time.1 is baseline

Single-case data analysis

        x       dx     z       dz      Sig
Time.1 45 Baseline -1.42 Baseline Baseline
Time.2 80      -35  0.92    -2.34        *
Time.3 70      -25  0.25    -1.67        *
Time.4 70      -25  0.25    -1.67        *

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require(boot)
require(car)
require(MASS)
require(QuantPsyc) # lm.beta() function

#---- Bootstrap Mediation Function ----

boot.med <- function(data, formula.m, formula.y, iv, mv, i) {
  data.resamp <- data[i,]
  reg.m <- lm(formula.m, data=data.resamp)

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>med(data=rfe, iv = "SOQsos", dv = "MBASo", mv="REIappear", cv="AGE", reps=1000)

Independent Variable: SOQsos
Dependent Variable: MBASo
Mediator Variable: REIappear

IV (and CVs) on DV
               Estimate          SE     Pr(>|z|)           LL          UL       Beta
(Intercept)  3.69132749 0.199476668 3.154675e-40  3.300353220 4.082301760         NA
SOQsos       0.01914967 0.005112734 2.578910e-04  0.009128709 0.029170628  0.2940897

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**********************************************************************
* Project: Classification of Eating Disorders using EDE-Q
* Coded by: Sam Mancuso
* Version: 1.0
* Date: 22 July 2014
***********************************************************************

** NB
* bmi_at_assessment = Clinician assessed BMI
* menstrual_status_on_admission = Clinician assessed menstrual status

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# =================================================================
#
# Analysis of repeated single-case data
#
# Mueser, K. T., Yarnold, P. R., & Foy, D. W. (1991).
# Statistical Analysis for Single-Case Designs Evaluating Outcome
# of Imaginal Exposure Treatment of Chronic PTSD.
# Behavior Modification, 15(2), 134-155.
#
# Coded by: Sam Mancuso

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# -------------------------------------------------------------------------
# Sample Size Estimation for Comparisons between Two Groups
# in Experimental Designs
# Coded by: Sam Mancuso
# Date: 03 March 2015
# Version: 2.0
# Revision Date: 05 March 2015
# Blog: sammancuso.wordpress.com
# -------------------------------------------------------------------------

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# Import 'protest' data and convert 'protest' to 'labelled' 
protest <- read.csv("protest.csv", header = TRUE) %>%
  mutate(
    protest = labelled(
      protest, 
      c(
        "No protest" = 0, 
        "Individual protest" = 1, 
        "Collective protest" = 2
        )

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# Load the shiny and longpower packages
library(shiny)
library(longpower)

# UI ----------------------------------------------------------------------
ui <- fluidPage(
  
  # Add title
  titlePanel("Linear Mixed Model Sample Size Calculations"),
  

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SDadjP.lm <- function(model, B = 10000, seed = 1, rob = FALSE) {
  # Stop if model not 'lm' object
  stopifnot(class(model)[1] == "lm")
  # Set seed for reproducibility
  set.seed(seed)
  # Set robust estimator
  est <- ifelse(rob == TRUE, "HC3", "const")
  # Set covariance matrix
  covMat <- vcovHC(model, est)
  # Get observed t-values

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# Meta-Analysis Function --------------------------------------------------
## Summary Effect Size and its Standard Error using DL method (Eqs. 1-3)
mw_est <- function(yi,
                   vi,
                   data,
                   lab,
                   method = c("DL", "DL2"),
                   ci = .95,
                   Q.profile = FALSE,
                   pi = FALSE,