Changelog

Source: NEWS.md

easyRasch 0.3.1

• RIpartgamLD() no longer shows negative gamma values.
• RIitemfit(): new option to set upper/lower cutoff values when calculating quantile cutoff values for conditional highlighting based on simulations from RIgetfit()
  • new default setting for cutoff is c(.001,.999). The old default was c(.005,.995), which according to preliminary simulation studies resulted in increased rates of false positives.
• RIestThetas(): added note in documentation that it is not advisable to use this function with incomplete response data.
  • RIestThetasOLD() or RIestThetasOLD2() are recommended when there are missing responses for some items for some respondents.
• RItargeting(model = "RM") now sorts items according to order in data.
• RIrestscore() and RIitemfit() now also output a column with item (average for polytomous items) location. This is due to preliminary simulation studies indicating that misfitting items > 1.5 logits from person mean require a larger sample to identify reliably.

easyRasch 0.3

• package name changed from RISEkbmRasch to easyRasch.

easyRasch 0.2.4.6

• RIpartgamDIF() added for convenient use of iarm::partgam_DIF() to assess DIF.
• RIpartgamLD() added for convenient use of iarm::partgam_LD() to assess local dependence.
• RImissing() and RImissingP() now return a message (not a plot) if no data is missing.

easyRasch 0.2.4.5

• Changed caption text in RIresidcorr() to be more grammatically correct.
• Bug fix for RItargeting() and RIitemparams() to use max(na.rm = TRUE).

easyRasch 0.2.4.4

• New data pre-analysis check RIcheckdata() to determine whether there are at least 3 responses in each cell (item response category)
  • implemented for RItargeting() and RIitemparams()
  • if there are fewer than 3 responses in any cell mirt will be used to estimate item threshold locations, since it is less prone to extreme values under these conditions than eRm.
  • if there are fewer than 3 responses in any cell a warning message will appear

easyRasch 0.2.4.3

• New function RIrestscore(), a wrapper function to simplify output from iarm::item_restscore().
• Fix for RIitemhierarchy(), where na.rm = TRUE was omitted from rowMeans() leading to no mean location for items with less thresholds than others.
• Fix for RIresidcorr() to make conditional highlighting work for any values.
  • Added option output = "quarto" for output of a knitr::kable() table (without conditional highlighting).

easyRasch 0.2.4.2

• RIitemfit() now consistently states that conditional item fit is based on complete cases only in the automatic caption text.
• RIitemfit() has a new option for conditional highlighting of misfit based on rule-of-thumb values for infit MSQ according to Smith et al. (1998), since Müller (2020) showed that these can be fairly accurate for conditional infit and thus useful for a quick look at item fit.
• RIgetfit() now defaults to use the same sample size that the conditional item fit function uses, which means only complete cases. There is an option to change this behavior in the simulation function.

easyRasch 0.2.4.1

• New function - RIpboot() generates datasets using parametric bootstrapping.

Bug fix:

• RIgetfitPlot() fix for when/if the first iteration of simulations has missing data

Known issue:

• RIgetfit() and RIgetResidCor() gets issues upstream with eRm::RM() when badly skewed dichotomous data is used as input.
  • A temporary fix has been implemented, discarding simulated datasets with less than 8 positive responses for any item.

easyRasch 0.2.4

• RIgetResidCor() and RIgetfit() now replicate the sample theta distribution accurately using resampling with replacement (parametric bootstrapping based on estimated sample thetas/item thresholds).
• RIgetResidCor() and RIgetfit() will now omit simulated datasets with empty cells (zero responses in response categories that should have responses).
• RIgetResidCor() now automatically chooses PCM or RM depending on data (model option removed)
• RIscoreSE() has support for dichotomomous data and automatically chooses PCM or RM depending on data
• RItileplot() has a new option for setting text_color.

easyRasch 0.2.3.1

• RIitemfit() should finally calculate misfit correctly so that the sorting works
• Changed RIgetfit() model estimation function to use psychotools::PCModel.fit() to speed up simulations slightly for polytomous models. Like the eRm::PCM() function, this also uses Conditional Maximum Likelihood, and produces identical results with iarm::out_infit().

easyRasch 0.2.3

• RIgetfit() now retains the variable/item names from the data.
• RIitemfit() now uses conditional highlighting with individual cutoff values for each item.
• RIitemfit() outputs two new variables indicating the differences between observed infit/outfit and cutoff threshold values.
• RIitemfit() optionally sorts the table output based on misfit per item, using either “infit” or “outfit”.
• RIgetfitPlot() optionally shows observed item fit in the plot. Ideally, 95% CI would be shown, but the SE output from iarm::out_infit is not reliable according to the author (Müller, 2020), and iarm::boot_fit() does not output SE, only p-values.

easyRasch 0.2.2.1

• Changed simulation based cutoff thresholds used by RIitemfit() to be quantile(fitmetric, .995) and .005 instead of .99 and .01 in previous version, to be consistent with RIgetResidCor() which uses the one-sided quantile(fitmetric, .99).

easyRasch 0.2.2

• New RIitemfit() function, which replaces both RIitemfitPCM() and RIitemfitRM()
  • Only outputs conditional MSQ (ZSTD irrelevant for conditional item fit)
  • Automatically uses RM or PCM depending on data structure.
  • Optional conditional highlighting of simulation based cutoff values, and includes the cutoff intervals when using output = "table" (default).
• Modified RIgetfit() to only use conditional MSQ when running simulations.
  • Automatically uses RM or PCM depending on data structure.
• Modified RIgetfitPlot() accordingly.
• Removed RIgetfitTable() and RIgetfitLoHi() since this information now is included in the output from the new function RIitemfit()

easyRasch 0.2.1.1

• Added 1st and 99th percentiles (upper/lower limits) for simulation based item fit metrics from RIgetfitTable()
• RIitemfitPCM() and RIgetfitLoHi() now use 1st/99th percentile values from simulation as cutoffs.
• RIgetfitPlot() now uses these options (see ?ggdist::stat_dotsinterval for details) for rendering the distribution of simulated+estimated item fit metrics

stat_dotsinterval(quantiles = iterations, point_interval = median_qi,
                        layout = "weave", slab_color = NA,
                        .width = c(0.66, 0.99)

easyRasch 0.2.1

• Dichotomous data now working with RIgetfit() and RIgetResidCor()
  • no integration with RIitemfitRM() yet.
• Renamed option method to model for RIgetfit() and RIgetResidCor() for consistency across functions.

easyRasch 0.2.0

Major update

Implemented two simulation functions to get cutoff values for item fit and residual correlations (Yen’s Q3). For now, these only work with polytomous (PCM) data.

As always, documentation is available by using ?function (without the parentheses otherwise usually included).

New functions, and brief descriptions:

• RIgetfit() - Get simulation based cutoff values for MSQ and ZSTD.
  • RIgetfitTable() - Summarises simulation based cutoff values for each item.
  • RIgetfitPlot() - Plot (one at a time)
• RIgetResidCor() - Get simulation based cutoff values for Yen’s Q3 residual correlations
  • Based on Christensen et al. (2017, DOI: 10.1177/0146621616677520).
  • Uses your dataset to get appropriate cutoff values for use with RIresidcorr()

Changes:

• RIitemfitPCM() now has two new options:
  • simcut Set to TRUE if you want to use simulation based cutoff values
  • gf The output object from RIgetfit() is needed when simcut = TRUE
  • example command: RIitemfitPCMtest(df, simcut = TRUE, gf = getfit)