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

`getBootCICSC.Rd`

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

## 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

## Background

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

## See also

`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()`

## 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),
### now add gender
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)
}
```