Title: | Kalman Filter for Impulse Noised Outliers |
Version: | 1.0.0 |
Author: | Bertrand Cloez [aut], Isabelle Sanchez [aut, cre], Benedicte Fontez [ctr] |
Maintainer: | Isabelle Sanchez <isabelle.sanchez@inrae.fr> |
Description: | A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: <doi:10.48550/arXiv.2208.00961>. |
License: | GPL-3 |
Depends: | R (≥ 4.1.0) |
Encoding: | UTF-8 |
LazyData: | TRUE |
URL: | https://forgemia.inra.fr/isabelle.sanchez/kfino |
BugReports: | https://forgemia.inra.fr/isabelle.sanchez/kfino/-/issues |
Imports: | ggplot2, dplyr, |
Suggests: | rmarkdown, knitr, testthat (≥ 3.0.0), covr, foreach, doParallel, parallel |
VignetteBuilder: | knitr |
RoxygenNote: | 7.2.1 |
Config/testthat/edition: | 3 |
NeedsCompilation: | no |
Packaged: | 2022-10-07 07:10:31 UTC; sanchez |
Repository: | CRAN |
Date/Publication: | 2022-11-03 08:26:44 UTC |
kfino: Kalman Filtering
Description
A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: arXiv:2208.00961.
Details
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Author(s)
Maintainer: Isabelle Sanchez isabelle.sanchez@inrae.fr
Authors:
Bertrand Cloez bertrand.cloez@inrae.fr
Other contributors:
Benedicte Fontez benedicte.fontez@supagro.fr [contractor]
See Also
Useful links:
Report bugs at https://forgemia.inra.fr/isabelle.sanchez/kfino/-/issues
doutlier defines an outlier distribution (Surface of a trapezium) and uses input parameters given in the main function kfino_fit()
Description
doutlier defines an outlier distribution (Surface of a trapezium) and uses input parameters given in the main function kfino_fit()
Usage
doutlier(y, K, expertMin, expertMax)
Arguments
y |
numeric, point |
K |
numeric, constant value |
expertMin |
numeric, the minimal weight expected by the user |
expertMax |
numeric, the maximal weight expected by the user |
Details
this function is used to calculate an outlier distribution
following a trapezium shape.
y \mapsto \text{doutlier}(y,K,\text{expertMin},\text{expertMax})
is the probability density function on
[\text{expertMin},\text{expertMax}]
which is linear and verifies
\text{doutlier}(\text{expertMax},K,\text{expertMin},\text{expertMax})
=K*\text{doutlier}(\text{expertMin},K,\text{expertMin},\text{expertMax}).
In particular, when $K=1$ this corresponds to the uniform distribution.
Value
a numeric value
Examples
doutlier(2,5,10,45)
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
)
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.
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
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
kfino_plot a graphical function for the result of a kfino run
Description
kfino_plot a graphical function for the result of a kfino run
Usage
kfino_plot(
resuin,
typeG,
Tvar,
Yvar,
Ident,
title = NULL,
labelX = NULL,
labelY = NULL
)
Arguments
resuin |
a list resulting of the kfino algorithm |
typeG |
char, type of graphic, either detection of outliers (with qualitative or quantitative display) or prediction. must be "quanti" or "quali" or "prediction" |
Tvar |
char, time variable in the data.frame datain |
Yvar |
char, variable which was analysed in the data.frame datain |
Ident |
char, column name of the individual id to be analyzed |
title |
char, a graph title |
labelX |
char, a label for x-axis |
labelY |
char, a label for y-axis |
Details
The produced graphic can be, according to typeG:
- quali
This plot shows the detection of outliers with a qualitative rule: OK values (black), KO values (outliers, purple) and OOR values (out of range values defined by the user in 'kfino_fit', red)
- quanti
This plot shows the detection of outliers with a quantitative display using the calculated probability of the kfino algorithm
- prediction
This plot shows the prediction of the analyzed variable plus the OK values. Prediction corresponds to E[X_t | Y_1...t] for each time point t. Between 2 time points, we used a simple linear interpolation.
Value
a ggplot2 graphic
Examples
data(spring1)
library(dplyr)
print(colnames(spring1))
# --- Without Optimisation on initial parameters
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)
# flags are qualitative
kfino_plot(resuin=resu2,typeG="quali",
Tvar="Day",Yvar="Poids",Ident="IDE",
title="kfino spring1",
labelX="Time (day)",labelY="Weight (kg)")
# flags are quantitative
kfino_plot(resuin=resu2,typeG="quanti",
Tvar="Day",Yvar="Poids",Ident="IDE")
# predictions on OK values
kfino_plot(resuin=resu2,typeG="prediction",
Tvar="Day",Yvar="Poids",Ident="IDE")
a dataset containing the WoW weighing for 4 animals of 1296 observations, https://doi.org/10.1016/j.compag.2018.08.022
Description
A dataset for kfino algorithm
Usage
lambs
Format
a data.frame
- Poids
weight (in kg)
- Date
Date of weighing yyyy-mm-dd
- IDE
id of the animal
- Day
Date of weighing with day and time yyyy-mm-dd hh:mm:ss
- dateNum
a rescaled date - fraction of the whole observational time for one individual.
dateNum=(Heure - min(Heure))/86400 + (Date - min(Date))/86400
a dataset containing the WoW weighing for one animal (merinos lamb) of 397 observations. https://doi.org/10.1016/j.compag.2018.08.022
Description
A dataset for kfino algorithm
Usage
merinos1
Format
a data.frame
- Poids
weight (in kg)
- Date
Date of weighing yyyy-mm-dd
- IDE
id of the animal
- Day
Date of weighing with day and time yyyy-mm-dd hh:mm:ss
- dateNum
a rescaled date - fraction of the whole observational time for one individual.
dateNum=(Heure - min(Heure))/86400 + (Date - min(Date))/86400
a dataset containing the WoW weighing for one animal (merinos lamb) of 345 observations, difficult to model. https://doi.org/10.1016/j.compag.2018.08.022
Description
A dataset for kfino algorithm
Usage
merinos2
Format
a data.frame
- Poids
weight (in kg)
- Date
Date of weighing yyyy-mm-dd
- IDE
id of the animal
- Day
Date of weighing with day and time yyyy-mm-dd hh:mm:ss
- dateNum
a rescaled date - fraction of the whole observational time for one individual.
dateNum=(Heure - min(Heure))/86400 + (Date - min(Date))/86400
a dataset containing the WoW weighing for one animal of 203 observations. https://doi.org/10.1016/j.compag.2018.08.022
Description
A dataset for kfino algorithm
Usage
spring1
Format
a data.frame
- Poids
weight (in kg)
- Date
Date of weighing yyyy-mm-dd
- IDE
id of the animal
- Day
Date of weighing with day and time yyyy-mm-dd hh:mm:ss
- dateNum
a rescaled date - fraction of the whole observational time for one individual.
dateNum=(Heure - min(Heure))/86400 + (Date - min(Date))/86400
utils_EM a function to estimate the parameters 'm_0' , 'mm', 'pp' through an Expectation-Maximization (EM) method
Description
utils_EM a function to estimate the parameters 'm_0' , 'mm', 'pp' through an Expectation-Maximization (EM) method
Usage
utils_EM(param, kappaOpt, Y, Tps, N, scalingC)
Arguments
param |
list, see initial parameter list in |
kappaOpt |
numeric, truncation setting for initial parameters' optimization, default 7 |
Y |
character, name of the numeric variable to predict in the data.frame datain |
Tps |
character, time column name in the data.frame datain, a numeric vector. Tvar can be expressed as a proportion of day in seconds |
N |
numeric, length of the numeric vector of Y values |
scalingC |
numeric, scaling constant. To be changed if the function is not able to calculate the likelihood because the number of data is large |
Details
utils_EM is a tool function used in the main kfino_fit
function. It uses the same input parameter list than the main function.
Value
a list:
- m0
numeric, optimized m0
- mm
numeric, optimized mm
- pp
numeric, optimized pp
- likelihood
numeric, the calculated likelihood
Examples
set.seed(1234)
Y<-rnorm(n=10,mean=50,4)
Tps<-seq(1,10)
N=10
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))
print(Y)
utils_EM(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
utils_fit a fonction running the kfino algorithm to filter data and detect outliers under the knowledge of all parameters
Description
utils_fit a fonction running the kfino algorithm to filter data and detect outliers under the knowledge of all parameters
Usage
utils_fit(param, threshold, kappa = 10, Y, Tps, N)
Arguments
param |
list, see initial parameter list in |
threshold |
numeric, threshold for confidence interval, default 0.5 |
kappa |
numeric, truncation setting for likelihood optimization, default 10 |
Y |
character, name of the numeric variable to predict in the data.frame datain |
Tps |
character, time column name in the data.frame datain, a numeric vector. Tvar can be expressed as a proportion of day in seconds |
N |
numeric, length of the numeric vector of Y values |
Details
utils_fit is a tool function used in the main kfino_fit
function. It uses the same input parameter list than the main function.
Value
a list
- prediction
vector, the prediction of weights
- label
vector, probability to be an outlier
- likelihood
numeric, the calculated likelihood
- lwr
vector of lower bound confidence interval of the prediction
- upr
vector of upper bound confidence interval of the prediction
- flag
char, is an outlier or not
Examples
set.seed(1234)
Y<-rnorm(n=10,mean=50,4)
Tps<-seq(1,10)
N=10
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))
print(Y)
utils_fit(param=param2,threshold=0.5,kappa=10,Y=Y,Tps=Tps,N=N)
utils_likelihood a function to calculate a likelihood on initial parameters optimized by a grid search
Description
utils_likelihood a function to calculate a likelihood on initial parameters optimized by a grid search
Usage
utils_likelihood(param, kappaOpt = 7, Y, Tps, N, scalingC)
Arguments
param |
list, see initial parameter list in |
kappaOpt |
numeric, truncation setting for initial parameters' optimization, default 7 |
Y |
character, name of the numeric variable to predict in the data.frame datain |
Tps |
character, time column name in the data.frame datain, a numeric vector. Tvar can be expressed as a proportion of day in seconds |
N |
numeric, length of the numeric vector of Y values |
scalingC |
numeric, scaling constant. To be changed if the function is not able to calculate the likelihood because the number of data is large |
Details
utils_likelihood is a tool function used in the main
kfino_fit
function. It uses the same input parameter list than
the main function.
Value
a likelihood
Examples
set.seed(1234)
Y<-rnorm(n=10,mean=50,4)
Tps<-seq(1,10)
N=10
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))
print(Y)
utils_likelihood(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)