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

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

Usage

getBootCIgrpMeanDiff(
  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 a subset of data created with group_by() and pick()

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 obsDiff, LCLdiff and UCLdiff

See also

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

Examples

if (FALSE) { # \dontrun{
### will need tidyverse to run
library(tidyverse)
### create some data
### get replicable data
set.seed(12345)
n <- 120
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
#
set.seed(12345) # to get replicable results from the bootstrap
tmpDat %>%
  ### don't forget to prefix the call with "list(" to tell dplyr
  ### you are creating list output
  ### pick(everything()) has replaced cur_data(), verbose but more flexbible
  summarise(meanDiff = list(getBootCIgrpMeanDiff(scores ~ grp, pick(everything())))) %>%
  ### now unnest the list to columns
  unnest_wider(meanDiff)

### now an example of how this becomes useful: same but by gender
set.seed(12345) # to get replicable results from the bootstrap
tmpDat %>%
  group_by(gender) %>%
  ### remember the list output again!
  summarise(meanDiff = list(getBootCIgrpMeanDiff(scores ~ grp, pick(everything())))) %>%
  ### remember to unnnest again!
  unnest_wider(meanDiff)
  } # }