What is uSDT for? | The data | Preparing the data | Condition coding: deviation vs treatment | How the median split works | Inspecting the prepared data | Fitting the model | Understanding fixed and random effects | Testing unconscious processing: The three hypotheses | H1: Group-level sensitivity difference ($\Delta d'$) | H2: Latent correlation between tasks ($\rho$) | H3: Latent regression (Indirect $d'$ on Direct $d'$) | Reliability of individual differences | Visualizing the model | The latent regression | Shrinkage | Subject intervals | ROC curves | Where to go from here | Parametric bootstrap inference | Long-format datasets | References
Package: uSDT 0.1.0.9000
uSDT: Hierarchical Signal Detection Theory Models for Unconscious Processing
Fits hierarchical signal detection theory (SDT) models to paired direct and indirect measures, the design used to test for unconscious processing. Continuous indirect measures (typically response times) are dichotomized with the within-subject median split of Meyen et al. (2022) <doi:10.1037/xge0001065> so that both tasks are placed on a common sensitivity scale. The package estimates a binomial probit mixed model in which the two sensitivities are correlated random effects, and tests the three hypotheses of interest: the group-level difference between sensitivities, their latent correlation, and the latent regression of the indirect on the direct measure, whose intercept is the test for unconscious processing. Frequentist estimation uses 'lme4'.
Authors:
uSDT_0.1.0.9000.tar.gz
uSDT_0.1.0.9000.zip(r-4.7-any)uSDT_0.1.0.9000.zip(r-4.6-any)uSDT_0.1.0.9000.zip(r-4.5-any)
uSDT_0.1.0.9000.tgz(r-4.6-any)uSDT_0.1.0.9000.tgz(r-4.5-any)
uSDT_0.1.0.9000.tar.gz(r-4.7-any)uSDT_0.1.0.9000.tar.gz(r-4.6-any)
uSDT_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
uSDT/json (API)
| # Install 'uSDT' in R: |
| install.packages('uSDT', repos = c('https://ricardoreysaez.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ricardoreysaez/usdt/issues
Pkgdown/docs site:https://ricardoreysaez.github.io
Datasets:
- vadillo_awareness - Awareness data from a probabilistic cuing experiment
- vadillo_cuing - Cuing data from a probabilistic cuing experiment
Last updated from:6965e77831. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 186 | ||
| source / vignettes | OK | 185 | ||
| linux-release | OK | 199 | ||
| macos-release | OK | 131 | ||
| macos-oldrel | OK | 106 | ||
| windows-devel | OK | 124 | ||
| windows-release | OK | 116 | ||
| windows-oldrel | OK | 149 | ||
| wasm-release | OK | 91 |
Exports:hsdtlatent_corlatent_regressionmeyen_splitsdt_momentssensitivity_diffusdt_bootusdt_data_longusdt_data_tasksusdt_reliabilityusdt_tests
Dependencies:bootclicpp11farverggplot2gluegtableisobandlabelinglatticelifecyclelme4MASSMatrixminqanlmenloptrR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangS7scalesvctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fit a hierarchical signal detection theory model | hsdt print.hsdt summary.hsdt |
| Dichotomize response times into binary choices | meyen_split |
| Diagnostic and analytical plots for hierarchical SDT models | plot.hsdt |
| Signal detection measures for each subject | sdt_moments |
| Parametric bootstrap intervals for hierarchical SDT models | usdt_boot |
| Prepare data for hierarchical SDT models | print.usdt_data usdt_data usdt_data_long usdt_data_tasks |
| Test the three core hypotheses of a hierarchical SDT model | latent_cor latent_regression sensitivity_diff usdt_hypotheses usdt_tests |
| Reliability of direct and indirect task measures | print.usdt_reliability summary.usdt_reliability usdt_reliability |
| Awareness data from a probabilistic cuing experiment | vadillo_awareness |
| Cuing data from a probabilistic cuing experiment | vadillo_cuing |