kfino_fit {kfino} | R Documentation |
kfino_fit a function to detect outlier with a Kalman Filtering approach
kfino_fit(
datain,
Tvar,
Yvar,
param = NULL,
doOptim = TRUE,
method = "ML",
threshold = 0.5,
kappa = 10,
kappaOpt = 7,
verbose = FALSE
)
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. |
The initialization parameter list 'param' contains:
(optional) numeric, target weight, NULL if the user wants to optimize it
(optional) numeric, probability to be correctly weighed, NULL if the user wants to optimize it
(optional) numeric, initial weight, NULL if the user wants to optimize it
numeric, rate of weight change, default 0.001
numeric, the minimal weight expected by the user
numeric, the maximal weight expected by the user
numeric, variance of m0, default 1
numeric, variance of mm, related to the unit of Tvar, default 0.05
numeric, variance of pp, related to the unit of Yvar, default 5
numeric, a constant value in the outlier function (trapezium), by default K=5
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.
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:
the parameter of interest - Yvar - predicted
the probability of the value being well predicted
lower bound of the confidence interval of the predicted value
upper bound of the confidence interval of the predicted value
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
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