fixMissing.fn {CGManalyzer}

Function to fix missing values in a vector

Description

Function to fix missing values in a vector

Usage

fixMissing.fn(y, x, Method = c("skip", "linearInterpolation", "loess", "dayCycle"),
OBScycle = 24 * 60 * 60/10)

Arguments

y

a vector of data with missing values

x

a vector for a series of consecutive time indices

Method

method options for fixing missing value. "skip": skip all missing values; "loess": use local fitting by loess(); "dayCycle":a missing value is replaced by the mean of the two values one day ahead and one day behind plus the mean of the differences between the two edge points and their corresponding means of the two values one day head and one day behind in a segment with missing values in which the missing value belongs to.

OBScycle

number of observations in a full cycle

Value

a vector of data from 'y' but with missing values fixed

Author(s)

Xiaohua Douglas Zhang

References

Zhang XD, Zhang Z, Wang D. 2018. CGManalyzer: an R package for analyzing continuous glucose monitoring studies. Bioinformatics 34(9): 1609-1611 (DOI: 10.1093/bioinformatics/btx826).

Examples

data.mat <-
   cbind( "x"=c(0,  3,  6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42),
          "signal"=c(3.930, 3.973, 4.005, NA, 4.164, 4.190, 4.205, NA, 4.186,
          4.265, NA, 4.266, 4.357, 4.503, 4.690) )
dataFixNA.mat <- fixMissing.fn( y=data.mat[,2], x=data.mat[,1], Method="linearInterpolation")
data.mat
dataFixNA.mat