DataTemporalMap-class {EHRtemporalVariability} | R Documentation |
Class DataTemporalMap
Description
Class DataTemporalMap
object contains the statistical distributions of data estimated at a
specific time period. Both relative and absolute frequencies are included.
Details
Objects of this class are generated automatically by the estimateDataTemporalMap
function,
but its construction and extension is open towards fostering its use through external methods.
E.g., one may use additional probability distribution estimation methods, or even construct
compatible DataTemporalMaps for other unstructured data such as images or free text.
Value
A DataTemporalMap
object.
Slots
probabilityMap
v-by-d numerical
matrix
representing the probability distribution temporal map (relative frequency).countsMap
v-by-d numerical
matrix
representing the counts temporal map (absolute frequency).dates
d-dimensional
Date
array of the temporal batches.support
v-by-1 numerical or character
matrix
representing the support (the value at each bin) of probabilityMap and countsMap.variableName
name of the variable (character).
variableType
type of the variable (character) among "numeric", "character", "Date" and "factor".
period
batching period among "week", "month" and "year".
Examples
# Generation through estimateDataTemporalMap function:
dataset <- read.csv2(system.file("extdata",
"nhdsSubset.csv",
package="EHRtemporalVariability"),
sep = ",",
header = TRUE,
na.strings = "",
colClasses = c( "character", "numeric", "factor",
"numeric" , rep( "factor", 22 ) ) )
datasetFormatted <- EHRtemporalVariability::formatDate(
input = dataset,
dateColumn = "date",
dateFormat = "%y/%m")
probMaps <- estimateDataTemporalMap(data = datasetFormatted,
dateColumnName = "date",
period = "month")
class( probMaps[[1]] )
# Manual generation:
countsMatrix <- matrix(sample.int(25, size = 12*10, replace = TRUE), nrow = 12, ncol = 10)
probabilityMatrix <- sweep(countsMatrix,1,rowSums(countsMatrix),"/")
dates <- seq(Sys.Date(),(Sys.Date()+30*11),30)
x <- new('DataTemporalMap', probabilityMap = probabilityMatrix,
countsMap = countsMatrix, dates = dates, support = data.frame(1:10),
variableName = "example", variableType = "numeric", period = "month")
plotDataTemporalMap(x)