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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