ind_excl_step: One step in indicator exclusion procedure

In ionr: Test for Indifference of Indicator

Description

See ind_excl for details.

Usage

ind_excl_step(indicators, outcome, indicatornames = 1:ncol(indicators),
  exclude = vector(), coruse = "everything", round = F)

Arguments

indicators	Set of numeric indicators (items) in a matrix.
outcome	A numeric outcome vector. Indicators and outcome can be simulated with scale_sim
indicatornames	An array of strings for labelling the outcome. Default to numbers from 1 to n of indicators
exclude	Exclude an item excluded at previous step, e.g., as decided by ind_excl_inc
coruse	argument for function cor(). Defaults to 'everything', as simulations have no missing data.
round	Allows rounding of values in returned matrix.

Value

Provides the results of a single step in indicator exclusion procedure. See example for details

Examples

## Create a scale-outcome set that violates ION. Only 2 indicators out of 8 relate to
## the outcome, the others just relate to the 2 indicators
set.seed(466)
a<-scale_sim(n=2500, to_n=2, tn_n=6)
res=ind_excl_step(a[[1]],a[[2]])
print(res)

# note that the p-values for upper items (7 & 8 ) are much smaller than for the rest

#row number   indicator number
#r.test.t     t value of the r.test.
#t.test.p     p value of the r.test.
#cor_excl     correlation between outcome and sum-score when an item is excluded.
#cor_all      correlation between outcome and sum-score when all items are included
# (i.e., full scale).
#cor.excl_all correlation between two sum-scores.

ionr documentation built on May 2, 2019, 11:25 a.m.