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

Description Usage Details Note Source References Examples

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

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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.

Note

Whenever using any of the data available here, please make sure to cite the original sources given in the following.

Source

The 6-point ROCs contains data from the following sources:

The 8-point ROCs contains data from the following sources:

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. https://doi.org/10.1016/j.jmp.2015.05.002

Examples

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## Not run: 
# 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

## End(Not run)

MPTinR documentation built on Aug. 27, 2020, 1:07 a.m.