sdt {sdtalt}R Documentation

Measures from SDT

Description

Several statistics from signal detection theory are calculated for each individual. The user can define their own, too. Summary statistics (mean, trimmed mean, standard deviation) for the sample can be calculated and BCa confidence intervals produced.

Usage

sdt(hits, fas, misses, cr, flat = 0, pmeans = FALSE, meas = "all", wk = 0.5, runboot = FALSE, trim = 0, R = 2000, confl = 0.95, newst = NULL, bound = FALSE, ...)

Arguments

hits vector of hits
fas vector of false alarms
misses vector of misses
cr vector of correct rejections
flat value added to all cells, default = 0, .5 is common
pmeans print the means and standard deviations
meas which measures. See below. Default is all 15.
wk if kappa used, the weighting value
runboot whether to run bootstrap for BCa confidence intervals
trim how much to trim the means. Default = 0, .2 is common
R how many replicates for the bootstrap. Default = 2000.
confl confidence level for intervals, between 0 and .999
newst name of any user-defined statistic is to be used. See below.
bound whether to bound infinite value to nearest finite value.
... other parameters passed to the function

Details

meas can take a list of up to 15 statistics.
HR Hit rate
FAR False alarm rate
d d'
csdt C
A A'
B B''
lnbeta lnbeta
beta beta
OR Odds ratio
lnOR lnOR
kappa Weighted kappa
phi phi
Q Yule's Q
eta Choice-theory measure eta
PC Proportion correct

newst is the name of a user-defined statistic that must have 4 arguments, for hits, false alarms, misses, and correct rejections, in this order.
If any user wants a statistics added to this function, please contact Dan Wright.

Value

If pmeans=TRUE then sample statistics are printed to screen. A dataframe of the same length as input is created with statistics for each of those listed in the meas option (15 by default).

Author(s)

Daniel B. Wright

References

Wright, D.B., Horry, R., & Skagerberg, E.M. (in press). Functions for traditional and multilevel approaches to signal detection theory. Behavior Research Methods.

Examples

hits <- rbinom(100,25,.6)
fa <- rbinom(100,25,.2)
miss <- rbinom(100,25,.4)
cr <- rbinom(100,25,.7)
sdtout <- sdt(hits,fa,miss,cr)
sdtout[1:3,]
sdt(hits,fa,miss,cr,meas=c("d","A"))[1:3,]
sdt(hits,fa,miss,cr,meas=c("d","A"),flat=.5)[1:3,]
HC <- function(hits,fas,misses,cr)
   return((hits+cr-fas-misses)/(hits+cr+fas+misses))
HCsqrt <- function(hits,fas,misses,cr)
   return(sqrt((hits+cr-fas-misses)/(hits+cr+fas+misses)))
HCstats <- sdt(hits,fa,miss,cr, meas={},newst=c(HC,HCsqrt))
HCstats[1:3,]
sdt(hits,fa,miss,cr, meas=c("d","A"), pmeans=TRUE)[1:3,]
sdt(hits,fa,miss,cr,meas=c("d","A"), pmeans=TRUE,trim=.2)[1:3,]
sdt(hits,fa,miss,cr, meas=c("A"),pmeans=TRUE,trim=.2,runboot=TRUE,confl=.90)[1:3,]

[Package sdtalt version 0.1-0.1 Index]