MonoInc {MonoInc}

Monotonic Increasing

Description

Combines many of the functions in the MonoInc package. Given a data range, weights, and imputation methods of choice, MonoInc will impute flagged values using either one or a combination of two imputation methods. It can also perform all single imputation methods for comparison.

Usage

MonoInc(data, id.col, x.col, y.col, data.r = NULL, tol = 0, direction = "inc", w1 = 0.5, 
  min, max, impType1 = "nn", impType2 = "reg", sum = FALSE)

Arguments

data

a data.frame or matrix of measurement data

id.col

column where the id's are stored

x.col

column where x values, or time variable is stored

y.col

column where y values, or measurements are stored

data.r

range for y values; must have three columns: 1 - must match values in xcol, 2 - lower range values, 3 - upper range values

tol

tolerance; how much outside of the range (data.r) is acceptable; same units as data in ycol

direction

the direction of the function a choice between increasing 'inc', and decreasing 'dec'

w1

weight of imputation type 1 (impType1); default is 0.50

min

lowest acceptable value for measurement; does not have to be a number in ycol

max

highest acceptable value for measurement; does not have to be a number in ycol

impType1

imputation method 1, a choice between Nearest Neighbor "nn", Regression "reg", Fractional Regression "fr", Last Observation Carried Forward "locf", or Last & Next "ln"; default is "nn"

impType2

imputation method 2; default is "reg"

sum

if true the function will return a matrix of all imputation methods in the package

Details

If two imputation methods are chosen, MonoInc will take a weighted average of the output of the imputed values. User must chose one or two imputation methods or sum=TRUE for a comparison. If there are not enough values available to impute missing or erroneous values, MonoInc will return an NA. Advice: Do NOT overwrite original data using this function! Use parallel processing if available on your device.

Value

Returns the data matrix with additional columns for the selected imputation method. If sum=TRUE, it will return a column for each single imputation method. The Y column will have NAs, indicating that this observation was flagged and imputed, for summary only. Duplicate rows are removed.

Author(s)

Michele Josey [e-mail] Melyssa Minto [e-mail]

Examples

data(simulated_data)
simulated_data <- simulated_data[1:1000,]
data(data.r)
library(sitar)

## Run MonoInc
sum <- MonoInc(simulated_data, 1,2,3, data.r,5,direction='inc', w1=0.3, min=30, max=175, 
    impType1=NULL, impType2=NULL, sum=TRUE)
head(sum)
test <- MonoInc(simulated_data, 1,2,3, data.r,5,direction='inc', w1=0.3, min=30, max=175, 
    impType1="nn", impType2="fr")
head(test)

## plot longitudinal height for each id
mplot(x=X, y=Nn.Fr, data=test)
tol <- 5
lines(data.r[,1], data.r[,2]-tol, col=2, lty=2)
lines(data.r[,1], data.r[,3]+tol, col=2, lty=2)