Bug fixes

Add hard disk failure data to package

.Rbuildignore

@@ -37,3 +37,4 @@ sandbox
 speedtest
 tests/testthat/previous_results/local_tests
 README_cache
+data-raw

R/HDs.R

@@ -0,0 +1,58 @@
+#' @title Hard Drive failures.
+#'
+#' @description
+#' A data set containing hard drive failures data from Backblaze in the
+#' start-stop format used in the \code{survival} package.
+#'
+#' @details
+#'
+#' Details about the the SMART attributes can be found on
+#' \url{https://en.wikipedia.org/wiki/S.M.A.R.T.}. As stated in the orginal
+#' source
+#'
+#' "Reported stats for the same SMART stat can vary in meaning based on the
+#' drive manufacturer and the drive model. Make sure you are comparing
+#' apples-to-apples as drive manufacturers don't generally disclose what their
+#' specific numbers mean."
+#'
+#' There are some notes on \url{https://en.wikipedia.org/wiki/S.M.A.R.T.}
+#' regarding which attributes that have vendor specific raw value. Further,
+#'
+#' "The values in the files are the values reported by the drives. Sometimes,
+#' those values are out of whack. For example, in a few cases the RAW value of
+#' SMART 9 (Drive life in hours) reported a value that would make a drive 10+
+#' years old, which was not possible. In other words, it’s a good idea to have
+#' bounds checks when you process the data."
+#'
+#' Last value carried forward have been used for all covariates. See this
+#' github page for the processing steps
+#' \url{https://github.com/boennecd/backblaze_survival_analysis_prep}.
+#'
+#'
+#' @format A \code{data.frame} with the following columns:
+#' \describe{
+#'   \item{serial_number}{Serial number for the hard disk which the row belongs
+#'   to.}
+#'   \item{model}{hard disk model.}
+#'   \item{manufacturer}{manufacturer of the hard disk model.}
+#'   \item{tstart,tstop}{start and stop times on the SMART 9 attribute scale.}
+#'   \item{fails}{1 if the hard disk fails at \code{tstop}.}
+#'   \item{size_tb}{hard disk size in terabytes.}
+#'   \item{smart_x}{the raw SMART attribute x value. E.g., \code{smart_12} is the power cycle count.}
+#'   \item{smart_x_bin}{1 if the SMART attribute x value is non-zero.}
+#'   \item{..._cumsum}{cumulative sum of the prefix \code{...}.}
+#'   \item{n_fails}{number of failures in the orginal data. Hard disk should
+#'   only fail once but this is not the case in the raw data.}
+#'   \item{n_records}{number of records in the original source.}
+#'   \item{min_date,max_date}{first and last date in the original source.}
+#'   \item{min_hours,max_hours}{smallest and largest value of the SMART 9
+#'   attribute in the original source.}
+#' }
+#'
+#' @usage
+#' data("hds", package = "dynamichazard")
+#'
+#' @source
+#' Raw data from \url{https://www.backblaze.com/b2/hard-drive-test-data.html}.
+#' Data have been processed to get a start-stop \code{data.frame} format.
+"hds"

R/print.R

@@ -45,19 +45,8 @@ print.ddhazard_boot <-
   }
 
 
-#' @title Print function for \code{ddhazard} result
-#'
-#' @param x object returned from \code{\link{ddhazard}}.
-#' @param var_indices variable indices to print for time-varying effects.
-#' @param time_indices time intervals to print for time-varying effects.
-#' @param digits number of digits to print.
-#' @param ... not used.
-#'
-#' @description
-#' The \code{sd} printed for time-varying effects are point-wise standard deviations from the smoothed covariance matrices.
-#'
 #' @export
-print.ddhazard<- function(x){
+print.ddhazard<- function(x, ...){
   cat("Call:", deparse(x$call), sep = "\n")
 
   cat("\n", sQuote(x$model), " model fitted with the ", sQuote(x$method),

data-raw/hds

@@ -0,0 +1 @@
+See hds data set is a processed form of the raw `.csv` files from Backblaze. See this repository for the code https://github.com/boennecd/backblaze_survival_analysis_prep.

vignettes/Comparing_methods_for_logistic_models.Rmd

@@ -7,7 +7,7 @@ header-includes:
 output:
   pdf_document:
     fig_caption: yes
-bibliography: pbc_data.bib
+bibliography: bibliography.bib
 csl: bib_style.csl
 vignette: >
   %\VignetteIndexEntry{Comparing methods for time varying logistic models}