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

Description Usage Format Source References Examples

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

1

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 ...

Source

http://www.pommerening.org/wiki/images/d/dc/CoedyBreninSortedforPublication.txt

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. 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. http://www.jenvstat.org/v08/i02

Examples

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## Not run: 
#############################################################################
# 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)

## End(Not run)

alexanderrobitzsch/sirt documentation built on June 27, 2021, 12:03 a.m.