options(help_type="html")
library(sensR)

#################################
## Bi et al 1997 has an example using the following four protocols:
n <- c(40, 40, 40, 40)
x <- c(25, 25, 30, 35)
protocol <- c("triangle", "duotrio", "threeAFC", "twoAFC")

## Lets start by getting an overview of the data:
dprime_table(x, n, protocol)

## Notice that this is just the same as discrim():
discrim(25, 40, method="triangle")

#################################
## The any-differences test:

dprime_compare(x, n, protocol)

## Lets look at the arguments that dprime_compare takes:
args(dprime_compare)

## so we can get the weighted average estimate of d-prime with
dprime_compare(x, n, protocol, estim="weighted.avg")

## Could also change the confidence level:
dprime_compare(x, n, protocol, conf.level= 0.90)

#################################
## Test of common d-prime:

dprime_test(x, n, protocol)
(dpfit <- dprime_test(x, n, protocol))
dpfit
## which kind of test is provided here?
## what are the conclusions?

## Could we test the same for the data individually?
posthoc(dpfit, test="zero", padj.method="none")
## Notice how much stronger the joint test is.

## Lets look at the arguments for dprime_test:
args(dprime_test)

## So we could do a similarity test with:
dprime_test(x, n, protocol, alternative="similarity", dprime0=1.9)

## Notice that none of the individual tests would be significant:
posthoc(dpfit, test=1.9, alternative = "less", padj.method="none")

##################################################################
## Memo rules:

## dprime_compare:   The any-differences hypothesis        One  p-value
## dprime_test:      Tests of common d-prime               One  p-value
## posthoc:          Tests involving individual d-primes   Many p-values

##################################################################
## Post-hoc comparisons
##################################################################
## All pairwise comparisons:
posthoc(dpfit)
posthoc(dpfit, padj.method="none")
## alternatively:
posthoc(dpfit, test="pairwise")
?p.adjust

## a little borring, so here are some other data:
x2 <- c(30, 22, 35, 30)
(dpfit2 <- dprime_compare(x2, n, protocol))
posthoc(dpfit2, test="pairwise")
dprime_table(x2, n, protocol)

#################################
## Test *some* pairwise comparisons:

## Assume d' from group 2 is a _standard_
## now perform all comparisons with group 2:
posthoc(dpfit2, test="base", base=2)

#################################
## Test: different from common d-prime:

posthoc(dpfit, test="common")
posthoc(dpfit2, test="common", padj="none")

## recall the common d-prime:
coef(dprime_test(x, n, protocol))
(d.c <- coef(dprime_test(x, n, protocol))[1, 1])
## now consider the test:
posthoc(dpfit, test=d.c, padj="none")
## So what is the difference here?


#################################
## Test: different from specific value:

## test if dprimes are different from zero:
posthoc(dpfit, test=0, padj="none")
## Alternatively:
posthoc(dpfit, test="zero", padj="none")

?posthoc
args(posthoc)
##################################################################
### Misc:

discrim(20, 20, method="triangle", statistic="likelihood")
discrim(20, 20, method="triangle", statistic="Wald")

discrim(4, 20, method="triangle", statistic="likelihood")
discrim(4, 20, method="triangle", statistic="Wald")