| Title: | Statistical Models of Repeated Categorical Rating Data |
| Version: | 1.3.2 |
| Description: | Fit statistical models based on the Dawid-Skene model - Dawid and Skene (1979) <doi:10.2307/2346806> - to repeated categorical rating data. Full Bayesian inference for these models is supported through the Stan modelling language. 'rater' also allows the user to extract and plot key parameters of these models. |
| License: | GPL-2 |
| URL: | https://jeffreypullin.github.io/rater/,https://github.com/jeffreypullin/rater |
| BugReports: | https://github.com/jeffreypullin/rater/issues |
| Depends: | R (≥ 3.4.0) |
| Imports: | ggplot2 (≥ 2.2.1), loo (> 2.0.0), methods, Rcpp (≥ 0.12.0),RcppParallel (≥ 5.0.1), rlang (> 0.2.0), rstan (≥ 2.26.0),rstantools (≥ 2.0.0) |
| Suggests: | coda, covr, knitr, rmarkdown, testthat |
| LinkingTo: | BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0),RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥2.26.0) |
| VignetteBuilder: | knitr |
| Biarch: | true |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| SystemRequirements: | GNU make |
| NeedsCompilation: | yes |
| Packaged: | 2025-07-23 11:09:00 UTC; jeffreypullin |
| Author: | Jeffrey Pullin [aut, cre, cph], Damjan Vukcevic [aut], Lars Mølgaard Saxhaug [ctb] |
| Maintainer: | Jeffrey Pullin <jeffrey.pullin@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-07-23 11:50:02 UTC |
The 'rater' package.
Description
Fit statistical models based on the Dawid-Skene model to repeatedcategorical rating data. Full Bayesian inference for these models issupported through the Stan modelling language. rater also allows the user toextract and plot key parameters of these models.
References
Stan Development Team (2018). RStan: the R interface to Stan. R package version 2.18.2. http://mc-stan.org
Anaesthetist ratings for patient suitability for surgery
Description
The data consist of ratings, on a 4-point scale, made by five anaesthetistsof patients' pre-operative health. The ratings were based on theanaesthetists assessments of a standard form completed for all of thepatients. There are 45 patients (items) and five anaesthetists (raters) intotal. The first anaesthetist assessed the forms a total of three times,spaced several weeks apart. The other anaesthetists each assessed the formsonce. The data is in 'long' format.
Usage
anesthesiaFormat
Adata.frame with 315 rows and 3 columns:
- item
The item index - which item is being rated
- rater
The rater index - which rater is doing the rating
- rating
The rating given
References
Dawid, A. P., and A. M. Skene. "Maximum Likelihood Estimation of ObserverError-Rates Using the EM Algorithm." Applied Statistics 28, no. 1 (1979): 20.
Convert a rater_fit object to a codamcmc.list object.
Description
Convert a rater_fit object to a codamcmc.list object.
Usage
as_mcmc.list(fit)Arguments
fit | A rater_fit object. |
Value
A coda mcmc.list object.
Examples
# Fit a model using MCMC (the default).mcmc_fit <- rater(anesthesia, "dawid_skene")# Convert it to an mcmc.listrater_mcmc_list <- as_mcmc.list(mcmc_fit)Dentist ratings of whether caries are healthy or not based on X-rays
Description
It consists of binary ratings, made by 5 dentists, of whether a given toothwas healthy (sound) or had caries, also known as cavities. The ratings wereperformed using X-ray only, which was thought to be more error-prone thanvisual/tactile assessment of each tooth. In total 3,689 ratings were made.This data is in 'grouped' format. Each row is one of the 'pattern' withthe final columns being a tally of how many times that pattern occurs inthe dataset.
Usage
cariesFormat
Adata.frame with 6 columns and 32 rows.
- rater_1
The rating of the dentist 1
- rater_2
The rating of the dentist 2
- rater_3
The rating of the dentist 3
- rater_4
The rating of the dentist 4
- rater_5
The rating of the dentist 5
- n
The number of times the rating pattern appears in the dataset
References
Espeland, Mark A., and Stanley L. Handelman. “Using Latent Class Models toCharacterize and Assess Relative Error in Discrete Measurements.”Biometrics 45, no. 2 (1989): 587–99.
Extract latent class probabilities from a rater fit object
Description
Extract latent class probabilities from a rater fit object
Usage
class_probabilities(fit, ...)## S3 method for class 'mcmc_fit'class_probabilities(fit, ...)## S3 method for class 'optim_fit'class_probabilities(fit, ...)Arguments
fit | A rater fit object. |
... | Extra arguments. |
Details
The latent class probabilities are obtained by marginalising outthe latent class and then calculating, for each draw of pi and theta, theconditional probability of the latent class given the other parametersand the data. Averaging these conditional probabilities gives the(unconditional) latent class probabilities retuned by this function.
Value
A I * K matrix where each element is the probably of item i beingof class k. (I is the number of items and K the number of classes).
Examples
fit <- rater(anesthesia, "dawid_skene")class_probabilities(fit)Get the underlyingstanfit object from arater_fit object.
Description
Get the underlyingstanfit object from arater_fit object.
Usage
get_stanfit(fit)Arguments
fit | A |
Value
Astanfit object from rstan.
Examples
fit <- rater(anesthesia, "dawid_skene", verbose = FALSE)stan_fit <- get_stanfit(fit)stan_fitCompute the PSIS LOO CV - a measure of model fit - of a rater fit object.
Description
Compute the PSIS LOO CV - a measure of model fit - of a rater fit object.
Usage
## S3 method for class 'rater_fit'loo(x, ..., cores = getOption("mc.cores", 1))Arguments
x | A |
... | Other arguments passed. |
cores | The number of cores to use when calling the underlyingfunctions. By default the value of the |
Details
This function is somewhat experimental; model comparison is alwaysdifficult and choosing between variants of the Dawid-Skene model shouldbe largely guided by considerations of data size and what is known aboutthe characteristics of the raters. loo is, however, one of the leadingmethods for Bayesian model comparison and should provide a helpful guidein many situations.
When calculating loo we always use the relative effectivesample size, calculated usingloo::relaive_eff to improve the estimatesof the PSIS effective sample sizes and Monte Carlo error.
For further information about the details of loo and PSIS please consultthe provided references.
Value
A loo object.
References
Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian modelevaluation using leave-one-out cross-validation and WAIC.Statistics and Computing. 27(5), 1413–1432. doi:10.1007/s11222-016-9696-4(journal version,preprint arXiv:1507.04544).
Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019).Pareto smoothed importance sampling.preprint arXiv:1507.02646
Examples
fit_ds <- rater(anesthesia, "dawid_skene", verbose = FALSE, chains = 1)fit_ccds <- rater(anesthesia, "class_conditional_dawid_skene", verbose = FALSE, chains = 1)loo_ds <- loo(fit_ds)loo_ccds <- loo(fit_ccds)# To compare the loos easily we can use the loo_compare function from the# loo package:library(loo)loo_compare(loo_ds, loo_ccds)# The documentation of the loo package contains more information about how# the output should be interpreted.Produce simulation data from a 'complete' rating design
Description
Produce simulation data from a 'complete' rating design
Usage
make_complete_rating_design_sim_data(I, J, N)Arguments
I | The number of items. |
J | The number of raters. |
N | The number of times each rater rates each item. |
Details
A 'complete' rating design is situation where every rater rateseach item the same number of times. In this function the number of timeseach rater rates each item isN.
Value
Simulation data in the format required bysimulate_dawid_skene_model() orsimulate_hier_dawid_skene_model().
Examples
make_complete_rating_design_sim_data(100, 5, 2)Make a theta parameter
Description
Make a theta parameter
Usage
make_theta(diag_values, J, K)Arguments
diag_values | The diagonal entries of each error matrix. |
J | The number of raters (The umber matrices in 3D array). |
K | The number of latent classes. |
Details
Thediag_values argument can either be a numeric vector of length1 or J. If it is length J, the jth element is the diagonal values of theerror matrix for the jth rater. If it is length 1 all raters have the samediagonal values.
Value
A c(J, K, K) array; the theta parameter
Examples
theta <- make_theta(0.7, 5, 4)theta[1, , ]Retrieve MCMC convergence diagnostics for a rater fit
Description
Retrieve MCMC convergence diagnostics for a rater fit
Usage
mcmc_diagnostics(fit, pars = c("pi", "theta"))Arguments
fit | An rater |
pars | A character vector of parameter names to return. By default |
Details
MCMC diagnostics cannot be calculate for the z due to themarginalisation used to fit the models.
These MCMC diagnostics are intended as basic sanity check of the qualityof the MCMC samples returned. Users who want more in depth diagnosticsshould consider usingas_mcmc.list() to convert the samples to acoda::mcmc.list() object, orget_stanfit() to extract the underlyingstanfit object.
Value
A matrix where the columns represent different diagnostics and therows are different parameters. Currently the first column containsthe Rhat statistic and the second bulk effective samples size. Therownames contain the parameter names.
References
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, andPaul-Christian Bürkner (2019). Rank-normalization, folding, andlocalization: An improved R-hat for assessing convergence ofMCMC.arXiv preprintarXiv:1903.08008.
See Also
rstan::Rhat(),rstan::ess_bulk()as_mcmc.list(),get_stanfit().
Examples
fit <- rater(anesthesia, "dawid_skene")# Calculate the diagnostics for all parameters.mcmc_diagnostics(fit)# Calculate the diagnostics just for the pi parameter.mcmc_diagnostics(fit, pars = "pi")Probabilistic models of repeated categorical rating
Description
Functions to set up models and change their priorparameters for use inrater().
Usage
dawid_skene(alpha = NULL, beta = NULL)hier_dawid_skene(alpha = NULL)class_conditional_dawid_skene(alpha = NULL, beta_1 = NULL, beta_2 = NULL)Arguments
alpha | prior parameter for pi |
beta | prior parameter for theta. This can either be a K * K matrix, inwhich case it is interpreted as the prior parameter of all of the Jraters, or a J by K by K array in which case it is the fully specifiedprior parameter for all raters. (Here K is the number of categories in thedata and J is the number of raters in the data.) |
beta_1 | First on diagonal prior probability parameter |
beta_2 | Second on diagonal prior probability parameter for theta |
Value
a rater model object that can be passed torater().
Examples
# Model with default prior parameters:default_m <- dawid_skene()# Changing alpha:set_alpha_m <- dawid_skene(alpha = c(2, 2, 2))# Changing beta, single matrix:# (See details for how this is interpreted.)beta_mat <- matrix(1, nrow = 4, ncol = 4)diag(beta_mat) <- 4beta_mat_m <- dawid_skene()# The above is equivalent (when the model is fit - see details) to:beta_array <- array(NA, dim = c(2, 4, 4))for (i in 1:2) { beta_array[i, , ] <- beta_mat}beta_array_m <- dawid_skene(beta = beta_array)# But you can also specify an array where each slice is different.# (Again, see details for how this is interpreted.)beta_array[1, , ] <- matrix(1, nrow = 4, ncol = 4)beta_array_m <- dawid_skene(beta = beta_array)# Default:hier_dawid_skene()# Changing alphahier_dawid_skene(alpha = c(2, 2))# Default:class_conditional_dawid_skene()# Not default:class_conditional_dawid_skene( alpha = c(2, 2), beta_1 = c(4, 4), beta_2 = c(2, 2))Plot arater_fit object
Description
Plot arater_fit object
Usage
## S3 method for class 'rater_fit'plot( x, pars = "theta", prob = 0.9, rater_index = NULL, item_index = NULL, theta_plot_type = "matrix", ...)Arguments
x | An object of class |
pars | A length one character vector specifying the parameter to plot.By default |
prob | The coverage of the credible intervals shown in the |
rater_index | The indexes of the raters shown in the |
item_index | The indexes of the items shown in the class probabilitiesplot. If not plotting the class probabilities this argument will beignored. By default |
theta_plot_type | The type of plot of the "theta" parameter. Can beeither |
... | Other arguments. |
Details
The use ofpars to refer to only one parameter is for backwardscompatibility and consistency with the rest of the interface.
Value
A ggplot2 object.
Examples
fit <- rater(anesthesia, "dawid_skene")# By default will just plot the theta plotplot(fit)# Select which parameter to plot.plot(fit, pars = "pi")# Plot the theta parameter for rater 1, showing uncertainty.plot(fit, pars = "theta", theta_plot_type = "points", rater_index = 1)Extract point estimates of parameters from a fit object
Description
Extract point estimates of parameters from a fit object
Usage
point_estimate(fit, pars = c("pi", "theta", "z"), ...)Arguments
fit | A rater fit object |
pars | A character vector of parameter names to return. By default |
... | Extra arguments |
Details
If the passed fit object was fit using MCMC then the posteriormeans are returned. If it was fit through optimisation the maximum apriori (MAP) estimates are returned. The z parameter returned is thevalue of class probabilities which is largest. To return the fullposterior distributions of the latent class useclass_probabilities().
For the class conditional model the 'full' theta parameterisation (i.e.appearing to have the same number of parameters as the standardDawid-Skene model) is calculated and returned. This is designed to alloweasier comparison with the full Dawid-Skene model.
Value
A named list of the parameter estimates.
See Also
class_probabilities()
Examples
# A model fit using MCMC.mcmc_fit <- rater(anesthesia, "dawid_skene")# This will return the posterior mean (except for z)post_mean_estimate <- point_estimate(mcmc_fit)# A model fit using optimisation.optim_fit <- rater(anesthesia, dawid_skene(), method = "optim")# This will output MAP estimates of the parameters.map_estimate <- point_estimate(optim_fit)Extract posterior intervals for parameters of the model
Description
Extract posterior intervals for parameters of the model
Usage
## S3 method for class 'mcmc_fit'posterior_interval(object, prob = 0.9, pars = c("pi", "theta"), ...)Arguments
object | A rater |
prob | A single probability. The size of the credible intervalreturned. By default |
pars | The parameters to calculate the intervals for |
... | Other arguments. |
Details
Posterior intervals can only be calculated for models fit withMCMC. In addition, posterior intervals are not meaningful for the latentclass (and indeed cannot be calculated). Thefull posterior distributionof the latent class can be extracted usingclass_probabilities
For the class conditional model the 'full' theta parameterisation (i.e.appearing to have the same number of parameters as the standardDawid-Skene model) is calculated and returned. This is designed to alloweasier comparison with the full Dawid-Skene model.
Value
A matrix with 2 columns. The first column is the lower bound ofof the credible interval and the second is the upper bound. Each rowcorresponds to one individuals parameters. The rownames are the parameternames.
Examples
fit <- rater(anesthesia, "dawid_skene", verbose = FALSE, chains = 1)intervals <- posterior_interval(fit)head(intervals)Extract posterior intervals for parameters of the model
Description
Extract posterior intervals for parameters of the model
Usage
## S3 method for class 'optim_fit'posterior_interval(object, prob = 0.9, pars = c("pi", "theta"), ...)Arguments
object | A rater optim_fit object |
prob | A probability |
pars | The parameters to calculate the intervals for |
... | Other arguments |
Draw from the posterior predictive distribution
Description
Draw from the posterior predictive distribution
Usage
## S3 method for class 'rater_fit'posterior_predict(object, new_data, seed = NULL, ...)Arguments
object | A |
new_data | New data for the model to be fit to. The must be in the formused in |
seed | An optional random seed to use. |
... | Other arguments. |
Details
The number of raters implied by the entries in the rater columnmust match the number of raters in the fitted model.
Value
The passednew_data augmented with a column 'z' containing thelatent class of each item and 'rating' containing the simulated rating.
Examples
fit <- rater(anesthesia, "dawid_skene", verbose = FALSE)new_data <- data.frame(item = rep(1:2, each = 5), rater = rep(1:5, 2))predictions <- posterior_predict(fit, new_data)predictionsExtract posterior samples from a rater fit object
Description
Extract posterior samples from a rater fit object
Usage
posterior_samples(fit, pars = c("pi", "theta"))Arguments
fit | A rater fit object. |
pars | A character vector of parameter names to return. By default |
Details
Posterior samples can only be returned for models fitting usingMCMC not optimisation. In addition, posterior samples cannot be returnedfor the latent class due to the marginalisation technique used internally.
For the class conditional model the 'full' theta parameterisation (i.e.appearing to have the same number of parameters as the standardDawid-Skene model) is calculated and returned. This is designed to alloweasier comparison with the full Dawid-Skene model.
Value
A named list of the posterior samples for each parameters. For eachparameter the samples are in the form returned byrstan::extract().
Examples
fit <- rater(anesthesia, "dawid_skene")samples <- posterior_samples(fit)# Look at first 6 samples for each of the pi parametershead(samples$pi)# Look at the first 6 samples for the theta[1, 1, 1] parameterhead(samples$theta[, 1, 1, 1])# Only get the samples for the pi parameter:pi_samples <- posterior_samples(fit, pars = "pi")Print amcmc_fit object
Description
Print amcmc_fit object
Usage
## S3 method for class 'mcmc_fit'print(x, ...)Arguments
x | An object of class |
... | Other arguments. |
Examples
# Suppress sampling output.mcmc_fit <- rater(anesthesia, "dawid_skene", verbose = FALSE)print(mcmc_fit)Print aoptim_fit object
Description
Print aoptim_fit object
Usage
## S3 method for class 'optim_fit'print(x, ...)Arguments
x | An object of class |
... | Other arguments. |
Examples
optim_fit <- rater(anesthesia, "dawid_skene", method = "optim")print(optim_fit)Print arater_model object.
Description
Print arater_model object.
Usage
## S3 method for class 'rater_model'print(x, ...)Arguments
x | A |
... | Other arguments |
Examples
mod <- dawid_skene()print(mod)Provide a summary of the priors specified in arater_fit object.
Description
Provide a summary of the priors specified in arater_fit object.
Usage
## S3 method for class 'rater_fit'prior_summary(object, ...)Arguments
object | A |
... | Other arguments. |
Examples
# Fit a model using MCMC (the default).fit <- rater(anesthesia, "dawid_skene", verbose = FALSE)# Summarise the priors (and model) specified in the fit.prior_summary(fit)Fit statistical models to repeated categorical rating data using Stan
Description
This functions allows the user to fit statistical models of noisycategorical rating, based on the Dawid-Skene model, using Bayesianinference. A variety of data formats and models are supported. Inferenceis done using Stan, allowing models to be fit efficiently, using bothoptimisation and Markov Chain Monte Carlo (MCMC).
Usage
rater( data, model, method = "mcmc", data_format = "long", long_data_colnames = c(item = "item", rater = "rater", rating = "rating"), inits = NULL, verbose = TRUE, ...)Arguments
data | A 2D data object: data.frame, matrix, tibble etc. with data ineither long or grouped format. |
model | Model to fit to data - must be rater_model or a characterstring - the name of the model. If the character string is used, theprior parameters will be set to their default values. |
method | A length 1 character vector, either |
data_format | A length 1 character vector, |
long_data_colnames | A 3-element named character vector that specifiesthe names of the three required columns in the long data format. The vectormust have the required names:* item: the name of the column containing the item indexes,* rater: the name of the column containing the rater indexes,* rating: the name of the column containing the ratings.By default, the names of the columns are the same as the names of thevector: |
inits | The initialization points of the fitting algorithm |
verbose | Should |
... | Extra parameters which are passed to the Stan fitting interface. |
Details
The default MCMC algorithm used by Stan is No U Turn Sampling(NUTS) and the default optimisation method is LGFGS. For MCMC 4 chainsare run be default with 2000 iterations in total each.
Value
An object of class rater_fit containing the fitted parameters.
See Also
rstan::sampling(),rstan::optimizing()
Examples
# Fit a model using MCMC (the default).mcmc_fit <- rater(anesthesia, "dawid_skene")# Fit a model using optimisation.optim_fit <- rater(anesthesia, dawid_skene(), method = "optim")# Fit a model using passing data grouped data.grouped_fit <- rater(caries, dawid_skene(), data_format = "grouped")Objects exported from other packages
Description
These objects are imported from other packages. Follow the linksbelow to see their documentation.
- loo
Simulate data from the Dawid-Skene model
Description
Simulate data from the Dawid-Skene model
Usage
simulate_dawid_skene_model(pi, theta, sim_data, seed = NULL)Arguments
pi | The pi parameter of the Dawid-Skene model. |
theta | The theta parameter of the Dawid-Skene model. |
sim_data | Data to guide the simulation. The data must be in the longdata format used in
|
seed | An optional random seed to use. |
Details
The number of raters implied by the entries in the rater columnmust match the number of raters implied by the passed theta parameter.
This function can also be used to simulate from the class-conditionalDawid-Skene model by specifying theta in the required form (i.e whereall off-diagonal entries of the error matrices are equal.)
Value
The passedsim_data augmented with columns:
"z"containing the latent class of each item,"rating"containing the simulated ratings.
Examples
J <- 5K <- 4pi <- rep(1 / K, K)theta <- make_theta(0.7, J, K)sim_data <- data.frame(item = rep(1:2, each = 5), rater = rep(1:5, 2))simulations <- simulate_dawid_skene_model(pi, theta, sim_data)simulationsSimulate data from the hierarchical Dawid-Skene model
Description
Simulate data from the hierarchical Dawid-Skene model
Usage
simulate_hier_dawid_skene_model(pi, mu, sigma, sim_data, seed = NULL)Arguments
pi | The pi parameter of the hierarchical Dawid-Skene model. |
mu | The mu parameter of the hierarchical Dawid-Skene model. |
sigma | The sigma parameter of the hierarchical Dawid-Skene model. |
sim_data | Data to guide the simulation. The data must be in the longdata format used in
|
seed | An optional random seed to use. |
Details
The number of raters implied by the entries in the rater columnmust match the number of raters implied by the passed theta parameter.
Value
The passedsim_data augmented with columns:
"z"containing the latent class of each item,"rating"containing the simulated rating.
Examples
J <- 5K <- 4pi <- rep(1 / K, K)mu <- matrix(0, nrow = K, ncol = K)diag(mu) <- 5sigma <- matrix(sqrt(2) / sqrt(pi), nrow = K, ncol = K)sim_data <- data.frame(item = rep(1:2, each = 5), rater = rep(1:5, 2))sim_result <- simulate_hier_dawid_skene_model(pi, mu, sigma, sim_data)sim_result$simsim_result$thetaSummarise amcmc_fit object
Description
Summarise amcmc_fit object
Usage
## S3 method for class 'mcmc_fit'summary(object, n_pars = 8, ...)Arguments
object | An object of class |
n_pars | The number of pi/theta parameters and z 'items' to display. |
... | Other arguments passed to function. |
Details
For the class conditional model the 'full' theta parameterisation(i.e. appearing to have the same number of parameters as the standardDawid-Skene model) is calculated and returned. This is designed to alloweasier comparison with the full Dawid-Skene model.
Examples
fit <- rater(anesthesia, "dawid_skene", verbose = FALSE)summary(fit)Summarise anoptim_fit object
Description
Summarise anoptim_fit object
Usage
## S3 method for class 'optim_fit'summary(object, n_pars = 8, ...)Arguments
object | An object of class |
n_pars | The number of pi/theta parameters and z 'items' to display. |
... | Other arguments passed to function. |
Details
For the class conditional model the 'full' theta parameterisation(i.e. appearing to have the same number of parameters as the standardDawid-Skene model) is calculated and returned. This is designed to alloweasier comparison with the full Dawid-Skene model.
Examples
fit <- rater(anesthesia, "dawid_skene", method = "optim")summary(fit)Summarise arater_model.
Description
Summarise arater_model.
Usage
## S3 method for class 'rater_model'summary(object, ...)Arguments
object | A |
... | Other arguments. |
Examples
mod <- dawid_skene()summary(mod)Compute the WAIC - a measure of model fit - of a rater fit object.
Description
Compute the WAIC - a measure of model fit - of a rater fit object.
Usage
## S3 method for class 'rater_fit'waic(x, ...)Arguments
x | A |
... | Other arguments passed. |
Details
This function provides provides an additional method for modelcomparison, on top of theloo() function. In general we recommend thatloo() is preferred: see the documentation of the loo package for details.Also, note the comments regarding model selection the the details sectionofloo().
Value
A waic/loo object.
References
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation andwidely application information criterion in singular learning theory.Journal of Machine Learning Research 11, 3571-3594.
Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian modelevaluation using leave-one-out cross-validation and WAIC.Statistics and Computing. 27(5), 1413–1432. doi:10.1007/s11222-016-9696-4(journal version,preprint arXiv:1507.04544).
Examples
fit_ds <- rater(anesthesia, "dawid_skene", verbose = FALSE, chains = 1)fit_ccds <- rater(anesthesia, "class_conditional_dawid_skene", verbose = FALSE, chains = 1)waic(fit_ds)waic(fit_ccds)Convert wide data to the long format
Description
Convert wide data to the long format
Usage
wide_to_long(data)Arguments
data | Data in a wide format. Must be 2D data object which can beconverted to a data.frame |
Details
Wide data refers to a way of laying out categorical rating datawhere each item is one row and each column represents the ratings of eachrater. Elements of the data can beNA, indicating that an item wasn'trated by a rater. Wide data cannot represent the same rater rating an itemmultiple times.
Currently any column names of the data are ignored and the raters arelabelled by their column position (1 indexed, left to right). Only numericratings are currently supported.
Value
The data converted into long format. A data.frame with three columnsitem, rater and rating.
Examples
wide_data <- data.frame(dater_1 = c(3, 2, 2), rater_2 = c(4, 2, 2))wide_datalong_data <- wide_to_long(wide_data)long_data