MPTinR (version 1.14.1)

ROCs: Recognition memory ROCs used by Klauer & Kellen (2015)

Description

Data of the meta-analysis on recognition memory ROC (receiver operating characteristic) curves reported in Klauer and Kellen (2015). In total there are 850 individual ROCs, 459 6-point ROCs and 391 8-point ROCs. Both data sets first report responses to old items and then to new item. For both item types the response categories are ordered from sure-new to sure-old. Please always cite the original authors when using this data.

Usage

data("roc6")
data("roc8")

Details

The source of each data set is given in the exp column of each data set. The id column gives a unique id for each data set. For the 6-point ROCs the first 12 columns contain the data, for the 8-point ROCs, the first 16 columns.

References

Klauer, K. C., & Kellen, D. (2015). The flexibility of models of recognition memory: The case of confidence ratings. Journal of Mathematical Psychology, 67, 8-25. tools:::Rd_expr_doi("10.1016/j.jmp.2015.05.002")

Examples

if (FALSE) {
# This example shows only how to fit the 6-point ROCs
data("roc6")

# 2HTM (2-high threshold model)
htm <- "
(1-Do)*(1-g)*(1-gn1)*(1-gn2)
(1-Do)*(1-g)*(1-gn1)*gn2
(1-Do)*(1-g)*gn1
Do*(1-do1)*(1-do2)  + (1-Do)*g*go1
Do*do1 + (1-Do)*g*(1-go1)*go2
Do*(1-do1)*do2 + (1-Do)*g*(1-go1)*(1-go2)

Dn*(1-dn1)*dn2 + (1-Dn)*(1-g)*(1-gn1)*(1-gn2)
Dn*dn1 + (1-Dn)*(1-g)*(1-gn1)*gn2
Dn*(1-dn1)*(1-dn2) + (1-Dn)*(1-g)*gn1
(1-Dn)*g*go1
(1-Dn)*g*(1-go1)*go2
(1-Dn)*g*(1-go1)*(1-go2)
"

# full 2HTM is over-parametereized:
check.mpt(textConnection(htm))
# apply some symmetric response mapping restrictions for D and g:
check.mpt(textConnection(htm), list("dn2 = do2", "gn2 = go2"))



# UVSD (unequal variance signal detection model)
uvsd <- "
pnorm(cr1, mu, sigma)
pnorm(cr1+cr2, mu, sigma) - pnorm(cr1, mu, sigma)
pnorm(cr3+cr2+cr1, mu, sigma) - pnorm(cr2+cr1, mu, sigma)
pnorm(cr4+cr3+cr2+cr1, mu, sigma) - pnorm(cr3+cr2+cr1, mu, sigma)
pnorm(cr5+cr4+cr3+cr2+cr1, mu, sigma) - pnorm(cr4+cr3+cr2+cr1, mu, sigma)
1 - pnorm(cr5+cr4+cr3+cr2+cr1, mu, sigma)
  
pnorm(cr1)
pnorm(cr2+cr1) - pnorm(cr1)
pnorm(cr3+cr2+cr1) - pnorm(cr2+cr1)
pnorm(cr4+cr3+cr2+cr1) - pnorm(cr3+cr2+cr1)
pnorm(cr5+cr4+cr3+cr2+cr1) - pnorm(cr4+cr3+cr2+cr1)
1 - pnorm(cr5+cr4+cr3+cr2+cr1)
"

# confidence criteria are parameterized as increments:
check.mpt(textConnection(uvsd))
# cr1 = [-Inf, Inf]
# cr2, cr3, cr4, cr5 = [0, Inf]
# mu = [-Inf, Inf]
# sigma = [0, Inf]


# MSD (mixture signal detection model): 
# NOTE: To follow CRAN rules restricting examples to a width of 100 characters, 
# the following example is splitted into multiple strings concatenated by paste().
# To view the full model use: cat(msd)
msd <- paste(c("
l*(pnorm(cr1-mu))  +  (1 - l) * (pnorm(cr1-mu2))
l*(pnorm(cr1+cr2-mu) - pnorm(cr1-mu)) + (1 - l)*(pnorm(cr1+cr2-mu2)-pnorm(cr1-mu2))
l*(pnorm(cr1+cr2+cr3-mu)-pnorm(cr1+cr2-mu)) + (1-l)*(pnorm(cr1+cr2+cr3-mu2)-pnorm(cr1+cr2-mu2))
",
"l*(pnorm(cr1+cr2+cr3+cr4-mu) - pnorm(cr1+cr2+cr3-mu)) + ",
"(1 - l)*(pnorm(cr1+cr2+cr3+cr4-mu2)-pnorm(cr1+cr2+cr3-mu2))",
"
l*(pnorm(cr1+cr2+cr3+cr4+cr5-mu)-pnorm(cr1+cr2+cr3+cr4-mu)) + ",
"(1 - l)*(pnorm(cr1+cr2+cr3+cr4+cr5-mu2)-pnorm(cr1+cr2+cr3+cr4-mu2))",
"
l * (1-pnorm(cr1+cr2+cr3+cr4+cr5-mu))  +  (1 - l)*(1-pnorm(cr1+cr2+cr3+cr4+cr5-mu2))

pnorm(cr1)
pnorm(cr1+cr2) - pnorm(cr1)
pnorm(cr1+cr2+cr3) - pnorm(cr1+cr2)
pnorm(cr1+cr2+cr3+cr4) - pnorm(cr1+cr2+cr3)
pnorm(cr1+cr2+cr3+cr4+cr5) - pnorm(cr1+cr2+cr3+cr4)
1-pnorm(cr1+cr2+cr3+cr4+cr5)
"), collapse = "") 
cat(msd)

# confidence criteria are again parameterized as increments:
check.mpt(textConnection(msd))
# cr1 = [-Inf, Inf]
# cr2, cr3, cr4, cr5 = [0, Inf]
# lambda = [0, 1]
# mu, mu2 = [-Inf, Inf]


# DPSD (dual-process signal detection model)
dpsd <- "
(1-R)*pnorm(cr1- mu)
(1-R)*(pnorm(cr1 + cr2 - mu) - pnorm(cr1 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 - mu) - pnorm(cr1 + cr2 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 + cr4 - mu) - pnorm(cr1 + cr2 + cr3 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 + cr4 + cr5 - mu) - pnorm(cr1 + cr2 + cr3 + cr4 - mu))
R + (1-R)*(1 - pnorm(cr1 + cr2 + cr3 + cr4 + cr5 - mu))

pnorm(cr1)
pnorm(cr1 + cr2) - pnorm(cr1)
pnorm(cr1 + cr2 + cr3) - pnorm(cr1 + cr2)
pnorm(cr1 + cr2 + cr3 + cr4) - pnorm(cr1 + cr2 + cr3)
pnorm(cr1 + cr2 + cr3 + cr4 + cr5) - pnorm(cr1 + cr2 + cr3 + cr4)
1 - pnorm(cr1 + cr2 + cr3 + cr4 + cr5)
"

uvsd_fit <- fit.model(roc6[,1:12], textConnection(uvsd),
            lower.bound=c(-Inf, rep(0, 5), 0.001), upper.bound=Inf)

msd_fit <- fit.model(roc6[,1:12], textConnection(msd),
            lower.bound=c(-Inf, rep(0, 7)), upper.bound=c(rep(Inf, 5), 1, Inf, Inf))

dpsd_fit <- fit.model(roc6[,1:12], textConnection(dpsd),
            lower.bound=c(-Inf, rep(0, 6)), upper.bound=c(rep(Inf, 6), 1))

htm_fit <- fit.mpt(roc6[,1:12], textConnection(htm), 
                   list("dn2 = do2", "gn2 = go2"))

select.mpt(list(uvsd_fit, dpsd_fit, msd_fit, htm_fit))
# Note that the AIC and BIC results do not adequately take model flexibility into account.
##      model n.parameters G.Squared.sum df.sum p.sum p.smaller.05
## 1 uvsd_fit            7      1820.568   1377     0           50
## 2 dpsd_fit            7      2074.188   1377     0           64
## 3  msd_fit            8      1345.595    918     0           51
## 4  htm_fit            9      1994.217    459     0          138
##   delta.AIC.sum wAIC.sum AIC.best delta.BIC.sum wBIC.sum BIC.best
## 1        0.0000        1      230        0.0000        1      273
## 2      253.6197        0      161      253.6197        0      183
## 3      443.0270        0       16     4996.8517        0        3
## 4     2009.6489        0       56    11117.2982        0        4
}