data.trees: Dataset Used in Stoyan, Pommerening and Wuensche (2018)

Description

Dataset used in Stoyan, Pommerening and Wuensche (2018; see also Pommerening et al., 2018). In the dataset, 15 forest managers classify 387 trees either as trees to be maintained or as trees to be removed. They assign tree marks, either 0 or 1, where mark 1 means remove.

Usage

data(data.trees)

Arguments

Format

The dataset has the following structure.

'data.frame': 387 obs. of 16 variables:
$ Number: int 142 184 9 300 374 42 382 108 125 201 ...
$ FM1 : int 1 1 1 1 1 1 1 1 1 0 ...
$ FM2 : int 1 1 1 0 1 1 1 1 1 1 ...
$ FM3 : int 1 0 1 1 1 1 1 1 1 1 ...
$ FM4 : int 1 1 1 1 1 1 0 1 1 1 ...
$ FM5 : int 1 1 1 1 1 1 0 0 0 1 ...
$ FM6 : int 1 1 1 1 0 1 1 1 1 0 ...
$ FM7 : int 1 0 1 1 0 0 1 0 1 1 ...
$ FM8 : int 1 1 1 1 1 0 0 1 0 1 ...
$ FM9 : int 1 1 0 1 1 1 1 0 1 1 ...
$ FM10 : int 0 1 1 0 1 1 1 1 0 0 ...
$ FM11 : int 1 1 1 1 0 1 1 0 1 0 ...
$ FM12 : int 1 1 1 1 1 1 0 1 0 0 ...
$ FM13 : int 0 1 0 0 1 1 1 1 1 1 ...
$ FM14 : int 1 1 1 1 1 0 1 1 1 1 ...
$ FM15 : int 1 1 0 1 1 0 1 0 0 1 ...

References

Pommerening, A., Ramos, C. P., Kedziora, W., Haufe, J., & Stoyan, D. (2018). Rating experiments in forestry: How much agreement is there in tree marking? PloS ONE, 13(3), e0194747. tools:::Rd_expr_doi("10.1371/journal.pone.0194747")

Stoyan, D., Pommerening, A., & Wuensche, A. (2018). Rater classification by means of set-theoretic methods applied to forestry data. Journal of Environmental Statistics, 8(2), 1-17.

Examples

Run this code

if (FALSE) {
#############################################################################
# EXAMPLE 1: Latent class models, latent trait models, mixed membership models
#############################################################################

data(data.trees, package="sirt")
dat <- data.trees[,-1]
I <- ncol(dat)

#** latent class models with 2, 3, and 4 classes
problevels <- seq( 0, 1, len=2 )
mod02 <- sirt::gom.em(dat, K=2, problevels, model="GOM")
mod03 <- sirt::gom.em(dat, K=3, problevels, model="GOM")
mod04 <- sirt::gom.em(dat, K=4, problevels, model="GOM")

#** grade of membership models
mod11 <- sirt::gom.em(dat, K=2, theta0.k=10*seq(-1,1,len=11), model="GOMnormal")
problevels <- seq( 0, 1, len=3 )
mod12 <- sirt::gom.em(dat, K=2, problevels, model="GOM")
mod13 <- sirt::gom.em(dat, K=3, problevels, model="GOM")
mod14 <- sirt::gom.em(dat, K=4, problevels, model="GOM")
problevels <- seq( 0, 1, len=4 )
mod22 <- sirt::gom.em(dat, K=2, problevels, model="GOM")
mod23 <- sirt::gom.em(dat, K=3, problevels, model="GOM")
mod24 <- sirt::gom.em(dat, K=4, problevels, model="GOM")

#** latent trait models
#- 1PL
mod31 <- sirt::rasch.mml2(dat)
#- 2PL
mod32 <- sirt::rasch.mml2(dat, est.a=1:I)

#- model comparison
IRT.compareModels(mod02, mod03, mod04, mod11, mod12, mod13, mod14,
                     mod22, mod23, mod24, mod31, mod32)

#-- inspect model results
summary(mod12)
round( cbind( mod12$theta.k, mod12$pi.k ),3)

summary(mod13)
round(cbind( mod13$theta.k, mod13$pi.k ),3)
}