Inference | Adjustment for multiple comparisons | Model diagnostic | Information about the R session used for this document
Package: lavaSearch2 2.0.3
lavaSearch2: Tools for Model Specification in the Latent Variable Framework
Tools for model specification in the latent variable framework (add-on to the 'lava' package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.
Authors:
lavaSearch2_2.0.3.tar.gz
lavaSearch2_2.0.3.zip(r-4.7)lavaSearch2_2.0.3.zip(r-4.6)lavaSearch2_2.0.3.zip(r-4.5)
lavaSearch2_2.0.3.tgz(r-4.6-x86_64)lavaSearch2_2.0.3.tgz(r-4.6-arm64)lavaSearch2_2.0.3.tgz(r-4.5-x86_64)lavaSearch2_2.0.3.tgz(r-4.5-arm64)
lavaSearch2_2.0.3.tar.gz(r-4.7-arm64)lavaSearch2_2.0.3.tar.gz(r-4.7-x86_64)lavaSearch2_2.0.3.tar.gz(r-4.6-arm64)lavaSearch2_2.0.3.tar.gz(r-4.6-x86_64)
lavaSearch2_2.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
lavaSearch2/json (API)
| # Install 'lavaSearch2' in R: |
| install.packages('lavaSearch2', repos = c('https://bozenne.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bozenne/lavasearch2/issues
inferencelatent-variable-modelsstatisticsopenblascpp
Last updated from:ab34e95aff. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 261 | ||
| linux-devel-x86_64 | OK | 224 | ||
| source / vignettes | OK | 268 | ||
| linux-release-arm64 | OK | 244 | ||
| linux-release-x86_64 | OK | 262 | ||
| macos-release-arm64 | OK | 315 | ||
| macos-release-x86_64 | OK | 515 | ||
| macos-oldrel-arm64 | OK | 291 | ||
| macos-oldrel-x86_64 | OK | 519 | ||
| windows-devel | OK | 253 | ||
| windows-release | OK | 282 | ||
| windows-oldrel | OK | 294 | ||
| wasm-release | OK | 158 |
Exports:addLinkcalcDistMaxBootstrapcalcDistMaxIntegralcalcType1postSelectioncalibrateType1checkDatacleancoef2coefCovcoefExtracoefIndexModelcoefInterceptcoefRefcoefRegcoefTypecoefVarcombineFormulacompare2confint2createContrasteffects2estimate2extractDatafindNewLinkformula2charactergaussian_weight_gradient.lvmgaussian_weight_hessian.lvmgaussian_weight_logLik.lvmgaussian_weight_method.lvmgaussian_weight_objective.lvmgaussian_weight_score.lvmgaussian_weight.estimate.hookgetNewLinkgetNewModelgetStepgetVarCov2glht2hessian2iid2iid2plotiidJackinformation2initVarLinkinitVarLinksintDensTrileverage2matrixPowermodel.tables2modelsearch2moments2nobs2nStepresiduals2sampleRepeatedscore2sCorrectsCorrect<-setLinkskeletonsummary2transformSummaryTabletryWithWarningsvcov2
Dependencies:abindclicodetoolscpp11digestdoParallelfarverforeachfuturefuture.applyggplot2globalsgluegtableisobanditeratorslabelinglatticelavalifecyclelistenvmagrittrMASSMatrixmultcompmvtnormnlmenumDerivparallellyplyrprogressrR6RColorBrewerRcppRcppArmadilloreshape2rlangS7sandwichscalesSQUAREMstringistringrsurvivalTH.datavctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Add a New Link Between Two Variables in a LVM | addLink addLink.lvm addLink.lvm.reduced |
| Graphical Display of the Bias or Type 1 Error | autoplot.calibrateType1 autoplot_calibrateType1 |
| 2D-display of the Domain Used to Compute the Integral | autoplot.intDensTri |
| Display the Value of a Coefficient across the Steps. | autoplot.modelsearch2 autplot-modelsearch2 |
| Adjust the p.values Using the Quantiles of the Max Statistic | calcDistMax calcDistMaxBootstrap calcDistMaxIntegral |
| Compute the Type 1 Error After Selection [EXPERIMENTAL] | calcType1postSelection |
| Simulation Study Assessing Bias and Type 1 Error | calibrateType1 calibrateType1.lvm calibrateType1.lvmfit |
| Check that Validity of the Dataset | checkData checkData.lvm |
| Simplify a lvm object | clean clean.lvm |
| Model Coefficients With Small Sample Correction | coef2 coef2.lvmfit |
| Extract the Coefficient by Type | coefByType coefCov coefCov.lvm coefCov.lvmfit coefCov.multigroup coefExtra coefExtra.lvm coefExtra.lvmfit coefExtra.multigroup coefIndexModel coefIndexModel.lvm coefIndexModel.lvmfit coefIndexModel.multigroup coefIndexModel.multigroupfit coefIntercept coefIntercept.lvm coefIntercept.lvmfit coefIntercept.multigroup coefRef coefRef.lvmfit coefReg coefReg.lvm coefReg.lvmfit coefReg.multigroup coefVar coefVar.lvm coefVar.lvmfit coefVar.multigroup |
| Extract the Type of Each Coefficient | coefType coefType.lvm coefType.lvmfit coefType.multigroup |
| Combine formula | combineFormula |
| Test Linear Hypotheses With Small Sample Correction | compare.lvmfit2 compare2 compare2.lvmfit compare2.lvmfit2 |
| Confidence Intervals With Small Sample Correction | confint2 confint2.lvmfit model.tables2 |
| formula character conversion | convFormulaCharacter formula2character |
| Create Contrast matrix | createContrast createContrast.character createContrast.list createContrast.lvmfit createContrast.lvmfit2 createContrast.mmm |
| Degree of Freedom for the Chi-Square Test | dfSigma |
| Effects Through Pathways With Small Sample Correction | effects.lvmfit2 effects2 effects2.lvmfit effects2.lvmfit2 |
| Extract Data From a Latent Variable Model | extractData extractData.lvmfit |
| Find all New Links Between Variables | findNewLink findNewLink.lvm |
| Estimate LVM With Weights | gaussian_weight gaussian_weight.estimate.hook gaussian_weight_gradient.lvm gaussian_weight_hessian.lvm gaussian_weight_logLik.lvm gaussian_weight_method.lvm gaussian_weight_objective.lvm gaussian_weight_score.lvm |
| Extract the Links that Have Been Found by the modelsearch2. | getNewLink getNewLink.modelsearch2 |
| Extract the Model that Has Been Retains by the modelsearch2. | getNewModel getNewModel.modelsearch2 |
| Extract one Step From the Sequential Procedure | getStep getStep.modelsearch2 |
| Residual Variance-Covariance Matrix With Small Sample Correction. | getVarCov2 getVarCov2.lvmfit getVarCov2.lvmfit2 |
| General Linear Hypothesis Testing With Small Sample Correction | glht.lvmfit2 glht2 glht2.lvmfit glht2.lvmfit2 glht2.mmm |
| Hessian With Small Sample Correction. | hessian2 hessian2.lvmfit hessian2.lvmfit2 |
| Influence Function With Small Sample Correction. | iid.lvmfit2 iid2 iid2.lvmfit iid2.lvmfit2 |
| Display the i.i.d. Decomposition | iid2plot |
| Jackknife iid Decomposition from Model Object | iidJack iidJack.default |
| Expected Information With Small Sample Correction. | information.lvmfit2 information2 information2.lvmfit information2.lvmfit2 |
| Normalize var1 and var2 | initVarLink initVarLinks |
| Integrate a Gaussian/Student Density over a Triangle | intDensTri |
| Tools for Model Specification in the Latent Variable Framework | lavaSearch2-package lavaSearch2 lavaSearch2, |
| Leverage With Small Sample Correction. | leverage2 leverage2.lvmfit leverage2.lvmfit2 |
| Power of a Matrix | matrixPower |
| Data-driven Extension of a Latent Variable Model | modelsearch2 modelsearch2.lvmfit |
| Effective Sample Size. | nobs2 nobs2.lvmfit nobs2.lvmfit2 |
| Find the Number of Steps Performed During the Sequential Testing | nStep nStep.modelsearch2 |
| Residuals With Small Sample Correction. | residuals2 residuals2.lvmfit |
| Simulate Repeated Measurements over time | sampleRepeated |
| Score With Small Sample Correction | score.lvmfit2 score2 score2.lvmfit score2.lvmfit2 |
| Depreciated Method For Small Sample Correction | sCorrect sCorrect.default sCorrect<- sCorrect<-.default |
| Set a Link to a Value | setLink setLink.lvm |
| Display the Type 1 Error Rate | summary.calibrateType1 |
| Outcome of Linear Hypothesis Testing | summary.glht2 |
| summary Method for modelsearch2 Objects | summary.modelsearch2 |
| Latent Variable Model Summary After Small Sample Correction | summary.lvmfit2 summary2 summary2.lvmfit summary2.lvmfit2 |
| Apply Transformation to Summary Table | transformSummaryTable |
| Run an Expression and Catch Warnings and Errors | tryWithWarnings |
| Variance-Covariance With Small Sample Correction | vcov.lvmfit2 vcov2 vcov2.lvmfit vcov2.lvmfit2 |
