Title Function designed for use in dplyr (tidyverse) piping to return CSC and bootstrap CI around that

## Usage

getBootCICSC(formula1, data, bootReps = 1000, conf = 0.95, bootCImethod = "pe")

## Arguments

formula1

formula defining the two variables to be correlated as scores ~ group

data

data.frame or tibble with the data, often constructed with pick() in dplyr

bootReps

integer giving number of bootstrap replications

conf

numeric value giving width of confidence interval, e.g. .95 (default)

bootCImethod

string giving method to derive bootstrap CI, minimum two letters 'pe', 'no', 'ba' or 'bc' for percentile, normal, basic or bca

## Value

list of named values obsCSC, LCLCSC and UCLCSC

## Background

For general information about the CSC (Clinically Significant Change criterion), see getCSC

## History/development log

Started before 5.iv.21

getCSC provides just the CSC if you don't need the CI around it. Much faster of course!

Other RCSC functions: classifyScoresVectorByRCI(), getCSC(), getRCIfromSDandAlpha()

Other bootstrap CI functions: getBootCICorr(), getBootCIalpha(), getBootCIgrpMeanDiff(), getBootCImean()

Chris Evans

## Examples

if (FALSE) {
### will need tidyverse to run
library(tidyverse)
### create some data
n <- 120
set.seed(12345) # to get replicable sample and results from the bootstrap
list(scores = rnorm(n), # Gaussian random base for scores
### now add a grouping variable: help-seeking or not
grp = sample(c("HS", "not"), n, replace = TRUE),
gender = sample(c("F", "M"), n, replace = TRUE)) %>%
as_tibble() %>%
### next add a gender effect nudging women's scores up by .4
mutate(scores = if_else(gender == "F", scores + .4, scores),
### next add the crucial help-seeking effect of 1.1
scores = if_else(grp == "HS", scores + 1.1, scores)) -> tmpDat

### have a look at that
tmpDat

### this just computes the CSC and CI(CSC) for the whole dataset
tmpDat %>%
### don't forget to prefix the call with "list(" to tell dplyr
### you are creating list output
### Also pick(everything()) has replaced the deprecated cur_data() that I used
### earlier I see the flexibility of pick() but it's ugly when used to replace
### the simpler cur_data() for this sort of thing.  Not my choice!
summarise(CSC = list(getBootCICSC(scores ~ grp, pick(everything())))) %>%
### now unnest the list to columns
unnest_wider(CSC)

### now an example of how this becomes useful: same but by grouping by gender
tmpDat %>%
group_by(gender) %>%
### remember the list output again!
summarise(CSC = list(getBootCICSC(scores ~ grp, pick(everything())))) %>%
### remember to unnnest again!
unnest_wider(CSC)
}