knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
The goal of edmdata
is to provide a set of an example assessment data sets
for psychometric modeling.
You can install edmdata
from github with:
# install.packages("devtools") devtools::install_github("tmsalab/edmdata")
library("edmdata")
items_ecpe
: N = r nrow(items_ecpe)
subject responses to J = r ncol(items_ecpe)
items.qmatrix_ecpe
: J = r nrow(qmatrix_ecpe)
items and K = r ncol(qmatrix_ecpe)
traits. items_fractions
: N = r nrow(items_fractions)
subject responses to J = r ncol(items_fractions)
items.qmatrix_fractions
: J = r nrow(items_fractions)
items and K = r ncol(items_fractions)
traits. items_probability_part_one_full
: N = r nrow(items_probability_part_one_full)
subject responses to J = r ncol(items_probability_part_one_full)
items.items_probability_part_one_reduced
: N = r nrow(items_probability_part_one_reduced)
subject responses to J = r ncol(items_probability_part_one_reduced)
items.qmatrix_probability_part_one
: J = r nrow(qmatrix_probability_part_one)
items and K = r ncol(qmatrix_probability_part_one)
traits. items_revised_psvtr
: N = r nrow(items_revised_psvtr)
subject responses
to J = r ncol(items_revised_psvtr)
items.items_ordered_eclsk_atl
: N = r nrow(items_ordered_eclsk_atl)
subject responses
to J = r ncol(items_ordered_eclsk_atl)
items.items_ordered_timss15_background
: N = r nrow(items_ordered_timss15_background)
subject responses
to J = r ncol(items_ordered_timss15_background)
items.items_ordered_pisa12_us_vignette
:
N = r nrow(items_ordered_pisa12_us_vignette)
subject responses to J = r ncol(items_ordered_pisa12_us_vignette)
items.items_pisa12_us_math
:
N = r nrow(items_pisa12_us_math)
subject responses to J = r ncol(items_pisa12_us_math)
items.items_spm_ls
: N = r nrow(items_spm_ls)
subject responses to J = r ncol(items_spm_ls)
items.items_hcp_penn_matrix
: N = r nrow(items_hcp_penn_matrix)
subject responses to J = r ncol(items_hcp_penn_matrix)
items.items_hcp_penn_matrix_missing
: N = r nrow(items_hcp_penn_matrix_missing)
subject responses with missing data indicators to J = r ncol(items_hcp_penn_matrix_missing)
items.items_matrix_reasoning
: N = r nrow(items_matrix_reasoning)
subject responses to J = r ncol(items_matrix_reasoning)
items.items_taylor_manifest_anxiety_scale
: N = r nrow(items_taylor_manifest_anxiety_scale)
subject responses to J = r ncol(items_taylor_manifest_anxiety_scale)
items.items_narcissistic_personality_inventory
: N = r nrow(items_narcissistic_personality_inventory)
subject responses to J = r ncol(items_narcissistic_personality_inventory)
items.qmatrix_oracle_k2_j12
: 12 items and 2 traits.qmatrix_oracle_k3_j20
: 20 items and 3 traits.qmatrix_oracle_k4_j20
: 20 items and 4 traits.qmatrix_oracle_k5_j30
: 30 items and 5 traits.strategy_oracle_k3_j20_s2
: 20 items, 3 traits, and 2 strategies.strategy_oracle_k3_j30_s2
: 30 items, 3 traits, and 2 strategies.strategy_oracle_k3_j40_s2
: 40 items, 3 traits, and 2 strategies.strategy_oracle_k3_j50_s2
: 50 items, 3 traits, and 2 strategies.strategy_oracle_k4_j20_s2
: 20 items, 4 traits, and 2 strategies.strategy_oracle_k4_j30_s2
: 30 items, 4 traits, and 2 strategies.strategy_oracle_k4_j40_s2
: 40 items, 4 traits, and 2 strategies.strategy_oracle_k4_j50_s2
: 50 items, 4 traits, and 2 strategies.There are two ways to access the data contained within this package.
The first is to load the package itself and type the name of a data set. This approach takes advantage of R’s lazy loading mechansim, which avoids loading the data until it is used in R session. For details on how lazy loading works, please see Section 1.17: Lazy Loading of the R Internals manual.
# Load the `edmdata` package library("edmdata") # See the first 10 observations of the `items_revised_psvtr` dataset head(items_revised_psvtr) # View the help documentation for `items_revised_psvtr` ?items_revised_psvtr
The second approach is to use the data()
command to load data on the
fly without loading the package. After using data()
, the data set
will be available to use under the given name.
# Loading `items_revised_psvtr` without a `library(edmdata)` call data("items_revised_psvtr", package = "edmdata") # See the first 10 observations of the `items_revised_psvtr` dataset head(items_revised_psvtr) # View the help documentation for `items_revised_psvtr` ?items_revised_psvtr
Want to see how each data set was imported? Check out the
data-raw
folder!
James Joseph Balamuta, Steven Andrew Culpepper, Jeffrey Douglas
edmdata
packageTo ensure future development of the package, please cite edmdata
package if used during an analysis or simulation study. Citation information
for the package may be acquired by using in R:
citation("edmdata")
MIT
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