kfino (version 1.0.0)

kfino_fit: kfino_fit a function to detect outlier with a Kalman Filtering approach

Description

kfino_fit a function to detect outlier with a Kalman Filtering approach

Usage

kfino_fit(
  datain,
  Tvar,
  Yvar,
  param = NULL,
  doOptim = TRUE,
  method = "ML",
  threshold = 0.5,
  kappa = 10,
  kappaOpt = 7,
  verbose = FALSE
)

Value

a S3 list with two data frames and a list of vectors of kfino results

detectOutlier: The whole input data set with the detected outliers flagged and the prediction of the analyzed variable. the following columns are joined to the columns present in the input data set:

prediction

the parameter of interest - Yvar - predicted

label_pred

the probability of the value being well predicted

lwr

lower bound of the confidence interval of the predicted value

upr

upper bound of the confidence interval of the predicted value

flag

flag of the value (OK value, KO value (outlier), OOR value (out of range values defined by the user in `kfino_fit` with `expertMin`, `expertMax` input parameters). If flag == OOR the 4 previous columns are set to NA.

PredictionOK: A subset of `detectOutlier` data set with the predictions of the analyzed variable on possible values (OK and KO values)

kfino.results: kfino results (a list of vectors containing the prediction of the analyzed variable, the probability to be an outlier, the likelihood, the confidence interval of the prediction and the flag of the data) on input parameters that were optimized if the user chose this option

Arguments

datain

an input data.frame of one time course to study (unique IDE)

Tvar

char, time column name in the data.frame datain, a numeric vector Tvar should be expressed as a proportion of day in seconds

Yvar

char, name of the variable to predict in the data.frame datain

param

list, a list of initialization parameters

doOptim

logical, if TRUE optimization of the initial parameters, default TRUE

method

character, the method used to optimize the initial parameters: Expectation-Maximization algorithm `"EM"` (faster) or Maximization Likelihood `"ML"` (more robust), default `"ML"`

threshold

numeric, threshold to qualify an observation as outlier according to the label_pred, default 0.5

kappa

numeric, truncation setting for likelihood optimization over initial parameters, default 10

kappaOpt

numeric, truncation setting for the filtering and outlier detection step with optimized parameters, default 7

verbose

write details if TRUE (optional), default FALSE.

Details

The initialization parameter list `param` contains:

mm

(optional) numeric, target weight, NULL if the user wants to optimize it

pp

(optional) numeric, probability to be correctly weighed, NULL if the user wants to optimize it

m0

(optional) numeric, initial weight, NULL if the user wants to optimize it

aa

numeric, rate of weight change, default 0.001

expertMin

numeric, the minimal weight expected by the user

expertMax

numeric, the maximal weight expected by the user

sigma2_m0

numeric, variance of m0, default 1

sigma2_mm

numeric, variance of mm, related to the unit of Tvar, default 0.05

sigma2_pp

numeric, variance of pp, related to the unit of Yvar, default 5

K

numeric, a constant value in the outlier function (trapezium), by default K=5

seqp

numeric vector, sequence of pp probability to be correctly weighted. default seq(0.5,0.7,0.1)

It should be given by the user based on their knowledge of the animal or the data set. All parameters are compulsory except m0, mm and pp that can be optimized by the algorithm. In the optimization step, those three parameters are initialized according to the input data (between the expert range) using quantile of the Y distribution (varying between 0.2 and 0.8 for m0 and 0.5 for mm). pp is a sequence varying between 0.5 and 0.7. A sub-sampling is performed to speed the algorithm if the number of possible observations studied is greater than 500. Optimization is performed using `"EM"` or `"ML"` method.

Examples

data(spring1)
library(dplyr)

# --- With Optimization on initial parameters - ML method
t0 <- Sys.time()
param1<-list(m0=NULL,
             mm=NULL,
             pp=NULL,
             aa=0.001,
             expertMin=30,
             expertMax=75,
             sigma2_m0=1,
             sigma2_mm=0.05,
             sigma2_pp=5,
             K=2,
             seqp=seq(0.5,0.7,0.1))

resu1<-kfino_fit(datain=spring1,
              Tvar="dateNum",Yvar="Poids",
              doOptim=TRUE,method="ML",param=param1,
              verbose=TRUE)
Sys.time() - t0

# --- Without Optimization on initial parameters
t0 <- Sys.time()
param2<-list(m0=41,
             mm=45,
             pp=0.5,
             aa=0.001,
             expertMin=30,
             expertMax=75,
             sigma2_m0=1,
             sigma2_mm=0.05,
             sigma2_pp=5,
             K=2,
             seqp=seq(0.5,0.7,0.1))
resu2<-kfino_fit(datain=spring1,
              Tvar="dateNum",Yvar="Poids",
              param=param2,
              doOptim=FALSE,
              verbose=FALSE)
Sys.time() - t0