| dprime_compare | R Documentation | 
This function will test the 'any-differences' hypothesis (conceptually a one-way ANOVA test for d-primes) with one of the Wald, Pearson or likelihood ratio chi-square test statistics. The common d-prime is estimated with ML or weighted average.
dprime_compare(correct, total, protocol, conf.level = 0.95,
   statistic = c("likelihood", "Pearson", "Wald.p", "Wald.d"),
   estim = c("ML", "weighted.avg"))
| correct | a numeric vector of the number of correct answers; one element for each test. | 
| total | a numeric vector of the total number of trials; one element for each test. | 
| protocol | a character vector or factor naming the protocol used; one element
for each test. Currently the following protocols are supported:
 | 
| conf.level | the confidence level for the estimated common d-prime. | 
| statistic | the test statistic for testing the 'any-differences' hypothesis. | 
| estim | The estimation method for the common d-prime. | 
The vectors correct, total and protocol have to
be of the same length.
The function has a print method.
an object of class "dprime_compare" with the following elements
| stat.value | the value of the (chi-square) test statistic for the 'any-differences' hypothesis. | 
| df | the degrees of freedom for the  | 
| p.value | the p-value for the 'any-differences' test. | 
| statistic | the name of the test statistic for the 'any-differences' test. | 
| data | the data table produced by  | 
| coefficients | 'table' with estimated common d-prime, standard error and confidence
limits storred as a one-row  | 
| conf.level | confidence level for the common d-prime. | 
| conf.int | the confidence interval for the common d-prime. | 
| estim | the estimation method for the common d-prime. | 
| conf.method | the statistical method/test statistic used to compute the confidence interval for the common d-prime. | 
Rune Haubo B Christensen
dprime_test, dprime_table,
posthoc.dprime_compare.
## Make some fake data:
n <- rep(40, 4)
x <- c(25, 25, 30, 35)
protocol <- c("triangle", "duotrio", "threeAFC", "twoAFC")
## Look at the data table with d-primes etc.:
dprime_table(x, n, protocol)
## 'any differences' test:
## ML estimation and test with likelihood statistic:
(dpc <- dprime_compare(x, n, protocol))
## Other estimation/statistic options:
dprime_compare(x, n, protocol, estim="weighted.avg")
dprime_compare(x, n, protocol, statistic="Pearson")
dprime_compare(x, n, protocol, statistic="Wald.p")
dprime_compare(x, n, protocol, statistic="Wald.d")
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