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skipTrack

Welcome to the SkipTrack Package!

SkipTrack is a Bayesian hierarchical model for self-reportedmenstrual cycle length data on mobile health apps. The model is anextension of the hierarchical model presented in Li et al. (2022) thatfocuses on predicting an individual’s next menstrual cycle start datewhile accounting for cycle length inaccuracies introduced bynon-adherence in user self-tracked data. Check out the ‘Getting Started’vignette to see an overview of the SkipTrack Model!

Installation

#Install from CRANinstall.packages('skipTrack')#Install Development Versiondevtools::install_github("LukeDuttweiler/skipTrack")

Package Usage

The SkipTrack package provides functions for fitting the SkipTrackmodel, evaluating model run diagnostics, retrieving and visualizingmodel results, and simulating related data. We begin our tutorial byexamining some simulated data.

library(skipTrack)

First, we simulate data on 100 individuals from the SkipTrack modelwhere each observed\(y_{ij}\) valuehas a 75% probability of being a true cycle, a 20% probability of beingtwo true cycles recorded as one, and a 5% probability of being threetrue cycles recorded as one.

set.seed(1)#Simulate datadat<-skipTrack.simulate(n =100,model ='skipTrack',skipProb =c(.75, .2, .05))

Fitting the SkipTrack model using this simulated data requires a callto the functionskipTrack.fit. Note that because this is aBayesian model and is fit with an MCMC algorithm, it can take some timewith large datasets and a high number of MCMC reps and chains.

In this code we ask for 4 chains, each with 1000 iterations, runsequentially. Note that we recommend allowing the sampler to run longerthan this (usually at least 5000 iterations per chain), but we use ashort run here to save time.

IfuseParallel = TRUE, the MCMC chains will be evaluatedin parallel, which helps with longer runs.

ft<-skipTrack.fit(Y = dat$Y,cluster = dat$cluster,reps =1000,chains =4,useParallel =FALSE)

Once we have the model results we are able to examine modeldiagnostics, visualize results from the model, and view a modelsummary.

Diagnostics

Multivariate, multichain MCMC diagnostics, including traceplots,Gelman-Rubin diagnostics, and effective sample size, are all availablefor various parameters from the model fit. These are supplied using thegenMCMCDiag package, see that packages’ documentation fordetails.

Here we show the output of the diagnostics on the\(c_{ij}\) parameters, which show that (atleast for the\(c_{ij}\) values) thealgorithm is mixing effectively (or will be, once the algorithm runs alittle longer).

skipTrack.diagnostics(ft,param ='cijs')#> Warning in attr(x, "align"): 'xfun::attr()' is deprecated.#> Use 'xfun::attr2()' instead.#> See help("Deprecated")#> Warning in attr(x, "align"): 'xfun::attr()' is deprecated.#> Use 'xfun::attr2()' instead.#> See help("Deprecated")

#> ----------------------------------------------------#> Generalized MCMC Diagnostics using Custom Method #> ----------------------------------------------------#> #> |Effective Sample Size: #> |---------------------------#> | Chain 1| Chain 2| Chain 3| Chain 4|    Sum|#> |-------:|-------:|-------:|-------:|------:|#> |  26.895|  18.342|  18.723|  19.814| 83.774|#> #> |psrf: #> |---------------------------#> | Point est.| Upper C.I.|#> |----------:|----------:|#> |      1.003|      1.009|

Visualization

In order to see some important plots for the SkipTrack model fit, youcan simply useplot(ft), and the plots are directlyaccessible usingskipTrack.visualize(ft).

plot(ft)

Summary

A summary is available for the SkipTrack model fit withsummary(ft), with more detailed results accessible throughskipTrack.results(ft). Importantly, these results are basedon a default chain burn-in value of 750 draws. This can be changed usingthe parameterburnIn for either function.

For example usingsummary with the default burnIn…

summary(ft)

produces the following output:

#> ----------------------------------------------------#> Summary of skipTrack.fit using skipTrack model#> ----------------------------------------------------#> Mean Coefficients: #> #>             Estimate       95% CI Lower 95% CI Upper#> (Intercept)    3.406              3.382        3.437#> #> ----------------------------------------------------#> Precision Coefficients: #> #>             Estimate       95% CI Lower 95% CI Upper#> (Intercept)    5.342              5.156         5.53#> #> ----------------------------------------------------#> Diagnostics: #> #>        Effective Sample Size       Gelman-Rubin#> Betas                3077.74               1.00#> Gammas                 21.85               1.01#> cijs                   68.15               1.00#> #> ----------------------------------------------------

On the other hand if we change the burnIn to 500…

summary(ft,burnIn =500)

we see:

#> ----------------------------------------------------#> Summary of skipTrack.fit using skipTrack model#> ----------------------------------------------------#> Mean Coefficients: #> #>             Estimate       95% CI Lower 95% CI Upper#> (Intercept)    3.405              3.378        3.433#> #> ----------------------------------------------------#> Precision Coefficients: #> #>             Estimate       95% CI Lower 95% CI Upper#> (Intercept)    5.322              5.106        5.526#> #> ----------------------------------------------------#> Diagnostics: #> #>        Effective Sample Size       Gelman-Rubin#> Betas                3818.63               1.00#> Gammas                 22.59               1.01#> cijs                   75.03               1.01#> #> ----------------------------------------------------

This introduction provides enough information to start fitting theSkipTrack model. For further information regarding different methods ofsimulating data, additional model fitting, and tuning parameters forfitting the model, please see the help pages and the ‘Getting Started’vignette. Additional vignettes are forthcoming.

Bibliography