Updated examples and documentation. *Should* be ready for initial CRA…

…N upload.

DESCRIPTION

@@ -4,7 +4,7 @@ Version: 0.0.1
 Authors@R: 
     person("Luke", "Duttweiler", , "lduttweiler@hsph.harvard.edu", role = c("aut", "cre", 'cph'),
            comment = c(ORCID = "0000-0002-0467-995X"))
-Description: Implements a Bayesian hierarchical model designed to identify skips in mobiel menstrual cycle self-tracking on mobiel apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>.
+Description: Implements a Bayesian hierarchical model designed to identify skips in mobile menstrual cycle self-tracking on mobile apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)

R/diagnostics.R

@@ -28,8 +28,8 @@
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #'
 #' #Get diagnostics for cijs
 #' skipTrack.diagnostics(modFit, 'cijs')

R/fit.R

@@ -17,8 +17,8 @@
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' modFit
 #'
 skipTrack.fit <- function(Y,cluster,

R/results.R

@@ -18,17 +18,19 @@
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' modFit
 #'
 #' # If using simulated data (which includes access to ground truth):
 #' #
-#' skipTrack.results(modFit, trueVals = simDat, burnIn = 250)
+#' skipTrack.results(modFit, trueVals = simDat, burnIn = 25)
+#' #Recommended burnIn with real data is at least 750
 #' #
 #' # If not using simulated data:
 #' #
-#  skipTrack.results(modFit, burnIn = 250)
+#' skipTrack.results(modFit, burnIn = 25)
+#' #Recommended burnIn with real data is at least 750
 #'
 #' @export
 skipTrack.results <- function(stFit, trueVals = NULL, burnIn = 750){

R/utils.R

@@ -12,8 +12,8 @@
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' plot(modFit)
 #'
 plot.skipTrack.model <- function(x, ...){
@@ -35,8 +35,8 @@ plot.skipTrack.model <- function(x, ...){
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' print(modFit)
 #'
 #'
@@ -58,7 +58,8 @@ print.skipTrack.model <- function(x, ...){
 
 #' Report skipTrack.model structure to console
 #'
-#' @param x skipTrack.model object from the function skipTrack.fit
+#' @param object skipTrack.model object from the function skipTrack.fit
+#' @param ... To match other str calls
 #'
 #' @return Invisible NULL. Prints structure of skipTrack.model object
 #' @export
@@ -67,15 +68,15 @@ print.skipTrack.model <- function(x, ...){
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' str(modFit)
 #'
 #'
-str.skipTrack.model <- function(x){
+str.skipTrack.model <- function(object, ...){
   cat(paste0('skipTrack.model S3 Object (also a list)\n\n',
-             'NChains:    ', length(x$fit), '\n',
-             'Model Type: ', x$model))
+             'NChains:    ', length(object$fit), '\n',
+             'Model Type: ', object$model))
   return(invisible())
 }
 
@@ -92,9 +93,9 @@ str.skipTrack.model <- function(x){
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
-#' summary(modFit, burnIn = 250)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
+#' summary(modFit, burnIn = 25) #Recommended burnIn with real data is at least 750
 #'
 #'
 summary.skipTrack.model <- function(object, ...){

R/visualization.R

@@ -20,8 +20,8 @@ if(getRversion() >= "2.15.1")  utils::globalVariables(c("ys", "cijs", 'taus',
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #'
 #' #Visualize results
 #' skipTrack.visualize(modFit)