Package: inlamemi 1.0.0
inlamemi: Missing Data and Measurement Error Modelling in INLA
Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.
Authors:
inlamemi_1.0.0.tar.gz
inlamemi_1.0.0.zip(r-4.5)inlamemi_1.0.0.zip(r-4.4)inlamemi_1.0.0.zip(r-4.3)
inlamemi_1.0.0.tgz(r-4.4-any)inlamemi_1.0.0.tgz(r-4.3-any)
inlamemi_1.0.0.tar.gz(r-4.5-noble)inlamemi_1.0.0.tar.gz(r-4.4-noble)
inlamemi_1.0.0.tgz(r-4.4-emscripten)inlamemi_1.0.0.tgz(r-4.3-emscripten)
inlamemi.pdf |inlamemi.html✨
inlamemi/json (API)
NEWS
| # Install 'inlamemi' in R: |
| install.packages('inlamemi', repos = c('https://emmaskarstein.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/emmaskarstein/inlamemi/issues
- framingham - Framingham heart study data
- mar_data - Simulated data with observation missing at random
- nhanes_survival - Survival data with repeated systolic blood pressure measurements
- simple_data - Simple simulated data
- two_error_data - Simulated data with two covariates with classical measurement error
Last updated 1 months agofrom:72ca7e9f1e. Checks:OK: 4 ERROR: 1 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 14 2024 |
| R-4.5-win | NOTE | Sep 14 2024 |
| R-4.5-linux | OK | Sep 14 2024 |
| R-4.4-win | OK | Sep 14 2024 |
| R-4.4-mac | OK | Sep 14 2024 |
| R-4.3-win | NOTE | Sep 14 2024 |
| R-4.3-mac | ERROR | Sep 14 2024 |
Exports:extract_variables_from_formulafit_inlamemiget_coef_impget_coef_misget_coef_moiget_imputedmake_inlamemi_control.familymake_inlamemi_familiesmake_inlamemi_formulamake_inlamemi_scaling_vectormake_inlamemi_stacksshow_data_structure
Dependencies:clicolorspacedplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbletidyselectutf8vctrsviridisLitewithr
How are the models structured?
Rendered fromVisualize_model_structure.Rmdusingknitr::rmarkdownon Sep 14 2024.Last update: 2024-07-05
Started: 2024-03-06
How to not use inlamemi
Rendered frombuilding_models_without_inlamemi.Rmdusingknitr::rmarkdownon Sep 14 2024.Last update: 2024-07-05
Started: 2024-07-05
Influence of systolic blood pressure on coronary heart disease
Rendered fromFramingham_heart_study.Rmdusingknitr::rmarkdownon Sep 14 2024.Last update: 2024-07-05
Started: 2024-03-06
Modifying the default plot
Rendered frommodifying_default_plot.Rmdusingknitr::rmarkdownon Sep 14 2024.Last update: 2024-07-05
Started: 2024-05-31
Multiple variables with measurement error and missingness
Rendered frommultiple_error_variables.Rmdusingknitr::rmarkdownon Sep 14 2024.Last update: 2024-07-05
Started: 2024-03-06
Simulated examples
Rendered fromsimulated_examples.Rmdusingknitr::rmarkdownon Sep 14 2024.Last update: 2024-07-05
Started: 2024-03-06
Survival model with repeated systolic blood pressure measurements
Rendered fromnhanes_survival_model.Rmdusingknitr::rmarkdownon Sep 14 2024.Last update: 2024-07-05
Started: 2024-07-05
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract model coefficients | coef.inlamemi |
| Fit model for measurement error and missing data in INLA | fit_inlamemi |
| Framingham heart study data | framingham |
| Extract coefficients for the imputation model (IMP) | get_coef_imp |
| Extract coefficients for the missingness model (MIS) | get_coef_mis |
| Extract coefficients for the model of interest (MOI) | get_coef_moi |
| Extract imputed values | get_imputed |
| Simulated data with observation missing at random (MAR) | mar_data |
| Survival data with repeated systolic blood pressure measurements | nhanes_survival |
| Plot model summary | plot.inlamemi |
| Print method for inlamemi | print.inlamemi |
| Visualize the model data structure as matrices in LaTeX | show_data_structure |
| Simple simulated data | simple_data |
| Summary method for inlamemi | print.summary.inlamemi summary.inlamemi |
| Simulated data with two covariates with classical measurement error | two_error_data |
