| Title: | Modelling and Validation of Non Homogeneous Poisson Processes |
|---|---|
| Description: | Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time. |
| Authors: | Ana C. Cebrian <[email protected]> |
| Maintainer: | Ana C. Cebrian <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 3.3 |
| Built: | 2024-12-24 06:43:50 UTC |
| Source: | CRAN |
NHPoisson provides tools for the modelling and maximum likelihood estimation of non homogeneous Poisson processes (NHPP) in time, where the intensity is formulated as a function of (time-dependent) covariates. A comprehensive toolkit for model selection, residual analysis and diagnostic of the fitted model is also provided. Finally, it permits random generation of NHPP.
| Package: | NHPoisson |
| Type: | Package |
| Version: | 3.0 |
| Date: | 2014-05-21 |
| License: | GPL (>=2) |
Ana C. Cebrian <[email protected]>
evir, extRemes, POT, ppstat, spatstat, yuima
This function fits all models that differ from the current model by adding a single covariate from those supplied, and calculates their AIC value. It selects the best covariate to be added to the model, according to the AIC.
addAIC.fun(mlePP, covariatesAdd, startAdd = NULL, modSim = FALSE,...)addAIC.fun(mlePP, covariatesAdd, startAdd = NULL, modSim = FALSE,...)
mlePP |
A |
covariatesAdd |
Matrix of the potential covariates to be added to the model; each column must contain a covariate. |
startAdd |
Optional. The vector of initial values for the estimation algorithm of the coefficients
of each potential covariate. If it is NULL, initial values equal to 0 are used. Remark
that in contrast to argument |
modSim |
Logical flag. If it is FALSE, information about the process is shown on the screen. For automatic selection processes, the option TRUE should be preferred. |
... |
Further arguments to pass to |
The definition of AIC uses constant k=2, but a different value k can be passed as an additional argument. The best covariate to be added is the one which leads to the model with the lowest AIC value and it improves the current model if the new AIC is lower than the current one.
A list with the following components
AICadd |
Vector of the AIC values obtained from adding to the current model each covariate in |
posminAIC |
An integer indicating the number of the column of covariatesAdd with the covariate leading to the minimum AIC. |
namecov |
Name of the covariate leading to the minimum AIC. |
AICcurrent |
AIC value of the current (initial) model. |
newCoef |
A (named) list with the initial value for the coefficient
of the best covariate to be added. It is used in |
dropAIC.fun, stepAICmle.fun, LRTpv.fun
data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) #The initial model contains only the intercept mod1Bind<-fitPP.fun(covariates=NULL, posE=BarEv$Px, inddat=BarEv$inddat, tit='BAR Intercept ', start=list(b0=1)) #the potential covariates covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) dimnames(covB)<-list(NULL,c('cos','sin','TTx','Txm31', 'Txm31**2')) aux<-addAIC.fun(mod1Bind, covariatesAdd=covB)data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) #The initial model contains only the intercept mod1Bind<-fitPP.fun(covariates=NULL, posE=BarEv$Px, inddat=BarEv$inddat, tit='BAR Intercept ', start=list(b0=1)) #the potential covariates covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) dimnames(covB)<-list(NULL,c('cos','sin','TTx','Txm31', 'Txm31**2')) aux<-addAIC.fun(mod1Bind, covariatesAdd=covB)
Barcelona daily temperature series during the summer months (May, June, July, August and September) from 1951 to 2004.
data(BarTxTn)data(BarTxTn)
Variables
dia: Postion of the day in the year, from 121 (1st of May) to 253 (30th of September).
mes: Month of the year, from 5 to 9.
ano: Year, from 1951 to 2004.
diames: Position of the day in the month, from 1 to 30 or 31.
Tx: Daily maximum temperature.
Tn: Daily minimum temperature.
Txm31: Local maximum temperature signal. Lowess of Tx with a centered window of 31 days.
Txm15: Local maximum temperature signal. Lowess of Tx with a centered window of 15 days.
Tnm31: Local minimum temperature signal. Lowess of Tn with a centered window of 31 days.
Tnm15: Local minimum temperature signal. Lowess of Tn with a centered window of 15 days.
TTx: Long term maximum temperature signal. Lowess of Tx with a centered 40% window.
TTn: Long term minimum temperature signal. Lowess of Tn with a centered 40% window.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
data(BarTxTn)data(BarTxTn)
This function calculates raw and scaled residuals of a NHPP based on
overlapping intervals. The scaled residuals can be Pearson or any other type of scaled residuals
defined by the function .
CalcRes.fun(mlePP, lint, h = NULL, typeRes = NULL)CalcRes.fun(mlePP, lint, h = NULL, typeRes = NULL)
mlePP |
An object of class |
lint |
Length of the intervals to calculate the residuals. |
h |
Optional. Weight function to calculate the scaled residuals. By default,
Pearson residuals with |
typeRes |
Optional. Label indicating the type of scaled residuals. By default, Pearson residuals are calculated and label is 'Pearson'. |
The raw residuals are based on the increments of
the raw process
in overlapping intervals centered on t:
Residuals are made 'instantaneous' dividing by the
length of the intervals (specified by the argument lint),
. A residual is calculated
for each time in the observation period.
The function also calculates the residuals scaled with the function
By default, Pearson residuals with are calculated.
A list with elements
RawRes |
Numeric vector of the raw residuals. |
ScaRes |
A list with elements ScaRes (vector of the scaled residuals) and typeRes (name of the type of scaled residuals). |
emplambda |
Numeric vector of the empirical estimator of the PP intensity on the considered intervals. |
fittedlambda |
Numeric vector of the sum of the intensities
|
lintV |
Numeric vector of the exact length of each interval. The exact length is defined as the number of observations in each interval used in the estimation (observations with inddat=1). |
lint |
Input argument. |
typeI |
Label indicating the type of intervals used to calculate the residuals, 'Overlapping'. |
h |
Input argument. |
mlePP |
Input argument. |
Abaurrea, J., Asin, J., Cebrian, A.C. and Centelles, A. (2007). Modeling and forecasting extreme heat events in the central Ebro valley, a continental-Mediterranean area. Global and Planetary Change, 57(1-2), 43-58.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67,617-666.
Brillinger, D. (1994). Time series, point processes and hybrids. Can. J. Statist., 22, 177-206.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Lewis, P. (1972). Recent results in the statistical analysis of univariate point processes. In Stochastic point processes (Ed. P. Lewis), 1-54. Wiley.
X1<-rnorm(1000) X2<-rnorm(1000) modE<-fitPP.fun(tind=TRUE,covariates=cbind(X1,X2), posE=round(runif(40,1,1000)), inddat=rep(1,1000), tim=c(1:1000), tit="Simulated example",start=list(b0=1,b1=0,b2=0), dplot=FALSE,modCI=FALSE,modSim=TRUE) #Residuals, based on overlapping intervals of length 50, from the fitted NHPP modE ResE<-CalcRes.fun(mlePP=modE, lint=50)X1<-rnorm(1000) X2<-rnorm(1000) modE<-fitPP.fun(tind=TRUE,covariates=cbind(X1,X2), posE=round(runif(40,1,1000)), inddat=rep(1,1000), tim=c(1:1000), tit="Simulated example",start=list(b0=1,b1=0,b2=0), dplot=FALSE,modCI=FALSE,modSim=TRUE) #Residuals, based on overlapping intervals of length 50, from the fitted NHPP modE ResE<-CalcRes.fun(mlePP=modE, lint=50)
This function calculates raw and scaled residuals of a NHPP based on
disjoint intervals. The scaled residuals can be Pearson or any other type of scaled residuals
defined by the function .
CalcResD.fun(mlePP, h = NULL, nint = NULL, lint = NULL, typeRes = NULL, modSim = "FALSE")CalcResD.fun(mlePP, h = NULL, nint = NULL, lint = NULL, typeRes = NULL, modSim = "FALSE")
mlePP |
An object of class |
lint |
Optional. Length of the intervals to calculate the residuals. |
h |
Optional. Weight function to calculate the scaled residuals. By default,
Pearson residuals with |
typeRes |
Optional. Label indicating the type of scaled residuals. By default, Pearson residuals are calculated and label is 'Pearson'. |
modSim |
Logical flag. If it is FALSE, some information on the intervals is shown on the screen. |
nint |
Number of intervals used to calculate the residuals. Intervals with the same length are considered. Only one of lint or nint must be specified. |
The intervals used to calculate the residuals can be specified either by nint or lint; only one of the arguments must be provided. If nint is specified, intervals of equal length are calculated.
The raw residuals are based on the increments of
the raw process
in disjoint intervals centered on t:
Residuals are made 'instantaneous' dividing by the
length of the intervals (specified by the argument lint),
.
The function also calculates the residuals scaled with the function
By default, Pearson residuals with are calculated.
A list with elements
RawRes |
Numeric vector of the raw residuals. |
ScaRes |
A list with elements ScaRes (vector of the scaled residuals) and typeRes (name of the type of scaled residuals). |
emplambda |
Numeric vector of the empirical estimator of the PP intensity on the considered intervals. |
fittedlambda |
Numeric vector of the sum of the intensities
|
lintV |
Numeric vector of the exact length of each interval. The exact length is defined as the number of observations in each interval used in the estimation (observations with inddat=1). |
lint |
Input argument. |
nint |
Input argument. |
pm |
Numeric vector of the mean point of the intervals. |
typeI |
Label indicating the type of intervals used to calculate the residuals, 'Disjoint' . |
h |
Input argument. |
mlePP |
Input argument. |
Abaurrea, J., Asin, J., Cebrian, A.C. and Centelles, A. (2007). Modeling and forecasting extreme heat events in the central Ebro valley, a continental-Mediterranean area. Global and Planetary Change, 57(1-2), 43-58.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67,617-666.
Brillinger, D. (1994). Time series, point processes and hybrids. Can. J. Statist., 22, 177-206.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Lewis, P. (1972). Recent results in the statistical analysis of univariate point processes. In Stochastic point processes (Ed. P. Lewis), 1-54. Wiley.
CalcRes.fun, unifres.fun,
graphres.fun
X1<-rnorm(1000) X2<-rnorm(1000) modE<-fitPP.fun(tind=TRUE,covariates=cbind(X1,X2), posE=round(runif(40,1,1000)), inddat=rep(1,1000), tim=c(1:1000), tit="Simulated example",start=list(b0=1,b1=0,b2=0), dplot=FALSE,modCI=FALSE,modSim=TRUE) #Residuals, based on 20 disjoint intervals of length 50, from the fitted NHPP modE ResDE<-CalcResD.fun(mlePP=modE,lint=50)X1<-rnorm(1000) X2<-rnorm(1000) modE<-fitPP.fun(tind=TRUE,covariates=cbind(X1,X2), posE=round(runif(40,1,1000)), inddat=rep(1,1000), tim=c(1:1000), tit="Simulated example",start=list(b0=1,b1=0,b2=0), dplot=FALSE,modCI=FALSE,modSim=TRUE) #Residuals, based on 20 disjoint intervals of length 50, from the fitted NHPP modE ResDE<-CalcResD.fun(mlePP=modE,lint=50)
Given the covariance matrix (or its estimation), an approximate
confidence interval for
each is calculated using the delta
method.
CIdelta.fun(VARbeta, lambdafit, covariates, clevel = 0.95)CIdelta.fun(VARbeta, lambdafit, covariates, clevel = 0.95)
VARbeta |
(Estimated) Covariance matrix of the |
lambdafit |
Numeric vector of fitted values of the PP intensity
|
covariates |
Matrix of covariates to estimate the PP intensity. |
clevel |
Confidence level of the confidence intervals. A value in the interval (0,1). |
A list with elements
LIlambda |
Numeric vector of the lower values of the intervals. |
UIlambda |
Numeric vector of the upper values of the intervals. |
lambdafit |
Input argument. |
fitPP.fun calls CIdelta.fun when the argument is CIty='Delta'.
Casella, G. and Berger, R.L., (2002). Statistical inference. Brooks/Cole.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
CItran.fun, fitPP.fun, VARbeta.fun
aux<-CIdelta.fun(VARbeta=0.01, lambdafit=exp(rnorm(100)), covariates=matrix(rep(1,100)), clevel=0.95)aux<-CIdelta.fun(VARbeta=0.01, lambdafit=exp(rnorm(100)), covariates=matrix(rep(1,100)), clevel=0.95)
Given the covariance matrix (or its estimation), an approximate
confidence interval for each is calculated using a transformation of
the confidence interval for the linear
predictor . The transformation is ,
where are the confidence limits of .
CItran.fun(VARbeta, lambdafit, covariates, clevel = 0.95)CItran.fun(VARbeta, lambdafit, covariates, clevel = 0.95)
VARbeta |
(Estimated) Coariance matrix of the |
lambdafit |
Numeric vector of fitted values of the PP intensity
|
covariates |
Matrix of covariates to estimate the PP intensity. |
clevel |
Confidence level of the confidence intervals. A value in the interval (0,1). |
A list with elements
LIlambda |
Numeric vector of the lower values of the intervals. |
UIlambda |
Numeric vector of the upper values of the intervals. |
lambdafit |
Input argument. |
fitPP.fun calls CItran.fun when the argument is CIty='Transf'.
Casella, G. and Berger, R.L., (2002). Statistical inference. Brooks/Cole.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
CIdelta.fun, fitPP.fun, VARbeta.fun
aux<-CItran.fun(VARbeta=0.01, lambdafit=exp(rnorm(100)), covariates=matrix(rep(1,100)), clevel=0.95)aux<-CItran.fun(VARbeta=0.01, lambdafit=exp(rnorm(100)), covariates=matrix(rep(1,100)), clevel=0.95)
This function computes confidence intervals for the parameters.
confintAsin.fun(mlePP, level = 0.95)confintAsin.fun(mlePP, level = 0.95)
mlePP |
|
level |
The confidence level required for the intervals. |
The confidence intervals calculated by this function are based on the asymptotic normal
approximation of th MLE of the parameters, that is
with
A matrix with two columns, the first contains the lower limits of the confidence intervals of all the parameters and the second the upper limits.
Casella, G. and Berger, R.L., (2002). Statistical inference. Brooks/Cole.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) mod1B<-fitPP.fun(covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=-1,b3=0,b4=0,b5=0)) confintAsin.fun(mod1B)data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) mod1B<-fitPP.fun(covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=-1,b3=0,b4=0,b5=0)) confintAsin.fun(mod1B)
This function fits all models obtained from the current model by deleting one covariate (except the intercept), and calculates their AIC value. It selects the best covariate to be deleted, according to the AIC value.
dropAIC.fun(mlePP, modSim = FALSE,...)dropAIC.fun(mlePP, modSim = FALSE,...)
mlePP |
A |
modSim |
Logical flag. If it is FALSE, information about the process is shown on the screen. For automatic selection processes, the option TRUE should be preferred. |
... |
Further arguments to pass to |
The definition of AIC uses constant k=2, but a different value k can be passed as an additional argument. The best covariate to be deleted is the one whose deletion leads to the model with the lowest AIC value and it improves the current model if the new AIC is lower than the current one.
A list with the following components
AICadd |
Vector of the AIC values obtained from deleting each covariate of the current model. |
posminAIC |
An integer indicating the number of the column of the covariates matrix with the covariate leading to the minimum AIC. |
namecov |
Name of the covariate leading to the minimum AIC. |
AICcurrent |
AIC value of the current (initial) model. |
Casella, G. and Berger, R.L., (2002). Statistical inference. Brooks/Cole.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. Springer.
addAIC.fun, stepAICmle.fun, LRTpv.fun
data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) dimnames(covB)<-list(NULL,c('cos','sin','TTx','Txm31', 'Txm31**2')) mod1B<-fitPP.fun(covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0,b5=0)) aux<-dropAIC.fun(mod1B)data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) dimnames(covB)<-list(NULL,c('cos','sin','TTx','Txm31', 'Txm31**2')) mod1B<-fitPP.fun(covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0,b5=0)) aux<-dropAIC.fun(mod1B)
This function calculates the empirical occurrence rates of a point process on overlapping intervals. The empirical rate centered in each time of the observation period is calculated using intervals of a given length. A plot of the empirical rate over time can be performed optionally.
emplambda.fun(posE, t, lint, plotEmp = TRUE, inddat = NULL, tit ="", scax = NULL, scay = NULL)emplambda.fun(posE, t, lint, plotEmp = TRUE, inddat = NULL, tit ="", scax = NULL, scay = NULL)
posE |
Numeric vector of the position of the occurrence points of the NHPP (or any point process in time). |
t |
Time index of the observation period. The simplest option is 1,...,n with n the length of the period. |
lint |
Length of the intervals used to calculate the rates. |
plotEmp |
Logical flag. If it is TRUE, a plot of the empirical rate is carried out. |
inddat |
Optional. Index vector equal to 1 for the observations used in the estimation process
By default, all the observations are considered, see |
tit |
Character string. A title for the plot. |
scax |
Optional. A two element vector indicating the x-scale for the plot. |
scay |
Optional. A two element vector indicating the y-scale for the plot. |
A list with elements
emplambda |
Vector of the empirical rates. |
lint |
Input argument. |
emplambdaD.fun, fitPP.fun, POTevents.fun
data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) # empirical rate based on overlapping intervals emplambdaB<-emplambda.fun(posE=BarEv$Px,inddat=BarEv$inddat, t=c(1:8415), lint=153, tit="Barcelona")data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) # empirical rate based on overlapping intervals emplambdaB<-emplambda.fun(posE=BarEv$Px,inddat=BarEv$inddat, t=c(1:8415), lint=153, tit="Barcelona")
This function calculates the empirical occurrence rates of a point process using disjoint intervals. The rate is assigned to the mean point of the interval. A plot of the empirical rate over time can be performed optionally.
emplambdaD.fun(posE, t, lint=NULL, nint = NULL, plotEmp = TRUE, inddat = NULL, tit = "", scax = NULL, scay = NULL)emplambdaD.fun(posE, t, lint=NULL, nint = NULL, plotEmp = TRUE, inddat = NULL, tit = "", scax = NULL, scay = NULL)
posE |
Numeric vector of the position of the occurrence points of the NHPP (or any point process in time). |
t |
Time index of the observation period. The simplest option is 1,...,n with n the length of the period. |
lint |
Optional (alternative argument to nint). Length of the intervals used to calculate the rates. |
nint |
Optional (alternative argument to lint). Number of intervals (of equal length) used to to calculate the rates. It is an alternative way to lint for identifying the intervals. |
plotEmp |
Logical flag. If it is TRUE, a plot of the empirical rate is carried out. |
inddat |
Optional. Index vector equal to 1 for the observations used in the
estimation process. By default, all the observations are considered,
see |
tit |
Character string. A title for the plot. |
scax |
Optional. A two element vector indicating the x-scale for the plot. |
scay |
Optional. A two element vector indicating the y-scale for the plot. |
The intervals can be specified either by nint or lint; only one of the arguments must be provided.
A list with elements
emplambda |
Vector of the empirical rates. |
lint |
Input argument. |
nint |
Input argument. |
emplambda.fun, fitPP.fun,
POTevents.fun
data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) # empirical rate based on disjoint intervals using nint to specify the intervals emplambdaDB<-emplambdaD.fun(posE=BarEv$Px,inddat=BarEv$inddat, t=c(1:8415), nint=55) # empirical rate based on disjoint intervals using lint to specify the intervals emplambdaDB<-emplambdaD.fun(posE=BarEv$Px,inddat=BarEv$inddat, t=c(1:8415), lint=153)data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) # empirical rate based on disjoint intervals using nint to specify the intervals emplambdaDB<-emplambdaD.fun(posE=BarEv$Px,inddat=BarEv$inddat, t=c(1:8415), nint=55) # empirical rate based on disjoint intervals using lint to specify the intervals emplambdaDB<-emplambdaD.fun(posE=BarEv$Px,inddat=BarEv$inddat, t=c(1:8415), lint=153)
mle for Function extractAIC
Method for generic function extractAIC for objects of the S4-class mle or
mlePP. It is the same method as in
stats4 (that method is not available outside that package).
signature(fit = "ANY")signature(fit = "mle")This function fits by maximum likelihood a NHPP where the intensity
is formulated as a function of covariates. It also calculates and plots
approximate confidence intervals for .
fitPP.fun(covariates = NULL, start, fixed=list(), posE = NULL, inddat = NULL, POTob = NULL, nobs = NULL, tind = TRUE, tim = NULL, minfun="nlminb", modCI = "TRUE", CIty = "Transf", clevel = 0.95, tit = "", modSim = "FALSE", dplot = TRUE, xlegend = "topleft", lambdaxlim=NULL,lambdaylim=NULL,...)fitPP.fun(covariates = NULL, start, fixed=list(), posE = NULL, inddat = NULL, POTob = NULL, nobs = NULL, tind = TRUE, tim = NULL, minfun="nlminb", modCI = "TRUE", CIty = "Transf", clevel = 0.95, tit = "", modSim = "FALSE", dplot = TRUE, xlegend = "topleft", lambdaxlim=NULL,lambdaylim=NULL,...)
covariates |
Matrix of the covariates to be included in the
linear predictor of the PP intensity (each column is a covariate). It is advisable to give
names to the columns of this matrix
(using |
start |
Named list of the initial values for the estimation of
the |
fixed |
Named list of the fixed |
posE |
Optional (see Details section). Numeric vector of the position of the PP occurrence points. |
inddat |
Optional (see Details section). Index vector equal to 1 for the observations used in the estimation process By default, all the observations are considered. |
POTob |
Optional (see Details section). List with elements T and thres
that defines the PP resulting from a POT approach;
see |
nobs |
Optional. Number of observations in the observation period; it is only neccessary if POTob, inddat and covariates are NULL. |
tind |
Logical flag. If it is TRUE, an independent term is fitted in the linear predictor. It cannot be a character string, so TRUE and not'TRUE' should be used. |
tim |
Optional. Time vector of the observation period. By default, a vector 1,...n is considered. |
minfun |
Label indicating the function to minimize the negative of the loglikelihood function. There are two possible values: "nlminb" (the default option) and "optim". In the last case, the method of optimization can be chosen with an additional method argument. |
modCI |
Logical flag. If it is TRUE, confidence intervals
for |
CIty |
Label indicating the method to calculate the approximate
confidence intervals for |
clevel |
Confidence level of the confidence intervals. |
tit |
Character string. A title for the plot. |
modSim |
Logical flag. If it is FALSE, information on the estimation process is shown on the screen. For simulation process, the option TRUE should be preferred. |
dplot |
Logical flag. If it is TRUE, the fitted intensity is plotted. |
xlegend |
Label indicating the position where the legend on the graph will be located. |
lambdaxlim |
Optional. Numeric vector of length 2, giving the lowest and highest values which determine the x range. |
lambdaylim |
Optional. Numeric vector of length 2, giving the lowest and highest values which determine the y range. |
... |
Further arguments to pass to |
A Poisson process (PP) is usually specified by a vector containing the occurrence
points of the process , (argument posE).
Since PP are often used in the framework of POT models, fitPP.fun also
provides the possibility of
using as input the series of the observed values in a POT model
and the threshold used to define the extreme events
(argument POTob).
In the case of PP defined by a POT approach,
the observations of the extreme events which are
not defined as the occurrence point are not considered in the estimation. This is done
through the argument inddat, see POTevents.fun. If the input is provided via argument POTob, index inddat
is calculated automatically. See Coles (2001) for more details on the POT approach.
The maximization of the loglikelihood function can be done using two different optimization routines,
optim or nlminb, selected in the argument minfun. Depending on
the covariates included in the function, one routine can succeed to converge when the other fails.
This function allows us to keep fixed some parameters (offset terms). This can be
used to specify an a priori known component to be included in the linear predictor during fitting. The fixed parameters
must be specified in the fixed argument (and also in start);
the fixed covariates must be included as columns of covariates.
The estimation of the covariance matrix is based on the
asymptotic distribution of the MLE , and calculated as the inverse of the negative of the hessian matrix.
Confidence intervals for can be calculated using two approaches
specified in the argument CIty. See Casella (2002) for more details on ML theory and delta method.
An object of class mlePP, which is a subclass of mle.
Consequently, many of the generic functions with mle methods, such as
logLik or summary, can be applied to the output of this function. Some other generic
functions related to fitted models, such as AIC or BIC, can also be applied to mlePP objects.
A homogeneous Poisson process (HPP) can be fitted as a particular case, using an intensity defined by only an intercept and no covariate.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Coles, S. (2001). An introduction to statistical modelling of extreme values. Springer.
Casella, G. and Berger, R.L., (2002). Statistical inference. Brooks/Cole.
Kutoyants Y.A. (1998).Statistical inference for spatial Poisson processes. Lecture notes in Statistics 134. Springer.
POTevents.fun, globalval.fun,
VARbeta.fun, CItran.fun, CIdelta.fun
#model fitted using as input posE and inddat and no confidence intervals data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) mod1B<-fitPP.fun(covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=-1,b3=0,b4=0,b5=0)) #model fitted using as input a list from POTevents.fun and with confidence intervals tiempoB<-BarTxTn$ano+rep(c(0:152)/153,55) mod2B<-fitPP.fun(covariates=covB, POTob=list(T=BarTxTn$Tx, thres=318), tim=tiempoB, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=-1,b3=0,b4=0,b5=0),CIty="Delta",modCI=TRUE, modSim=TRUE) #model with a fixed parameter (b0) mod1BF<-fitPP.fun(covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-89,b1=1,b2=10,b3=0,b4=0,b5=0), fixed=list(b0=-100))#model fitted using as input posE and inddat and no confidence intervals data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) mod1B<-fitPP.fun(covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=-1,b3=0,b4=0,b5=0)) #model fitted using as input a list from POTevents.fun and with confidence intervals tiempoB<-BarTxTn$ano+rep(c(0:152)/153,55) mod2B<-fitPP.fun(covariates=covB, POTob=list(T=BarTxTn$Tx, thres=318), tim=tiempoB, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=-1,b3=0,b4=0,b5=0),CIty="Delta",modCI=TRUE, modSim=TRUE) #model with a fixed parameter (b0) mod1BF<-fitPP.fun(covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-89,b1=1,b2=10,b3=0,b4=0,b5=0), fixed=list(b0=-100))
This function calculates a point estimation and an envelope for a given statistic using a Monte Carlo approach. The statistic must be a function of the occurrence points of a NHPP.
It calls the auxiliary function funSim.fun (not intended
for the users), see Details section.
GenEnv.fun(nsim, lambda, fun.name, fun.args = NULL, clevel = 0.95, cores = 1, fixed.seed=NULL)GenEnv.fun(nsim, lambda, fun.name, fun.args = NULL, clevel = 0.95, cores = 1, fixed.seed=NULL)
nsim |
Number of simulations for the calculations. |
lambda |
Numeric vector of the intensity |
fun.name |
Name of the function defining the statistic to be estimated. |
fun.args |
Additional arguments for the function fun.name. |
clevel |
Confidence level of the envelope. |
cores |
Optional. Number of cores of the computer to be used in the calculations. Default: one core is used. |
fixed.seed |
An integer or NULL. If it is an integer, that is the value used to set the seed in random generation processes. It it is NULL, a random seed is used. |
The auxiliary function funSim.fun
generates a simulated sample of the occurrence points in a NHPP
and calculates the corresponding statistic using the simulated points.
A list with elements
valmed |
Point estimation (mean value) of the statistic to be calculated. |
valinf |
Lower value of the simulated CI. |
valsup |
Upper value of the simulated CI. |
lambda |
Input argument. |
nsim |
Input argument. |
nsimval |
Number of valid simulations (used in the calculation of the CI and the point estimation). |
fixed.seed |
Input argument. |
# Calculation of the point estimation and a 95% CI based on 100 simulations #for the second occurrence time of a NHPP with intensity lambdat. #posk.fun(x, k) is a function that returns the value in the row k of vector x. lambdat<-runif(1000,0.01,0.02) aux<-GenEnv.fun(lambda=lambdat,fun.name="posk.fun",fun.args=2,nsim=100) #if we want reproducible results, we can fixed the seed in the generation process #(the number of cores used in the calculations must also be the same to reproduce #the result) aux<-GenEnv.fun(lambda=lambdat,fun.name="posk.fun",fun.args=2,nsim=100,fixed.seed=123) #the result (with 1 core): Lower interval: 25.55; Mean value: 136.06; Upper interval: 288# Calculation of the point estimation and a 95% CI based on 100 simulations #for the second occurrence time of a NHPP with intensity lambdat. #posk.fun(x, k) is a function that returns the value in the row k of vector x. lambdat<-runif(1000,0.01,0.02) aux<-GenEnv.fun(lambda=lambdat,fun.name="posk.fun",fun.args=2,nsim=100) #if we want reproducible results, we can fixed the seed in the generation process #(the number of cores used in the calculations must also be the same to reproduce #the result) aux<-GenEnv.fun(lambda=lambdat,fun.name="posk.fun",fun.args=2,nsim=100,fixed.seed=123) #the result (with 1 core): Lower interval: 25.55; Mean value: 136.06; Upper interval: 288
This function performs a thorough validation analysis for a fitted NHPP. It calculates the (generalized) uniform and the raw (or scaled) residuals, performs residual plots for the uniform residuals, and time residual and lurking variable plots for the raw or scaled residuals. It also plots the fitted and empirical estimations of the NHPP intensity. Optionally, it also performs a residual QQplot.
globalval.fun(mlePP, lint = NULL, nint = NULL, Xvar = NULL, namXvar = NULL, Xvart = NULL, namXvart = NULL, h = NULL, typeRes = NULL, typeResLV="Pearson",typeI = "Disjoint", nsim = 100, clevel = 0.95, resqqplot = FALSE, nintLP = 100, tit = "", flow = 0.5, addlow = FALSE, histWgraph=TRUE,plotDisp=c(2,2), indgraph = FALSE, scax = NULL, scay = NULL, legcex = 0.5, cores = 1, xlegend = "topleft", fixed.seed=NULL)globalval.fun(mlePP, lint = NULL, nint = NULL, Xvar = NULL, namXvar = NULL, Xvart = NULL, namXvart = NULL, h = NULL, typeRes = NULL, typeResLV="Pearson",typeI = "Disjoint", nsim = 100, clevel = 0.95, resqqplot = FALSE, nintLP = 100, tit = "", flow = 0.5, addlow = FALSE, histWgraph=TRUE,plotDisp=c(2,2), indgraph = FALSE, scax = NULL, scay = NULL, legcex = 0.5, cores = 1, xlegend = "topleft", fixed.seed=NULL)
mlePP |
An object of class |
lint |
Length of the intervals used to calculate the residuals. |
nint |
Number of intervals used to calculate the residuals. Intervals of equal length are considered. Only used if typeI="Disjoint". In that case, only one of the arguments lint or nint must be specified. |
Xvar |
Optional. Matrix of the lurking variables (each column is a variable). |
namXvar |
Optional. Vector of names of the variables in Xvar. |
Xvart |
Optional. Matrix of the variables for the residual plots (each column is a variable). A time plot is performed in all the cases. |
namXvart |
Optional. Vector of names of the variables in Xvart. |
h |
Optional. Weight function to calculate the scaled residuals. By default, Pearson residuals with
are calculated. This function is used to calculate both the scaled residuals and the residuals for the lurking variables (except if typeResLV="Raw"). |
typeRes |
Optional. Label indicating the type of scaled residuals. By default, Pearson residuals are calculated and label is "Pearson". |
typeResLV |
Label indicating the type of residuals ("Raw" or any type of scaled residuals such as "Pearson") to calculate the residuals for the lurking variable plots. |
typeI |
Label indicating the type ("Overlapping" or "Disjoint") of intervals used to calculate the residuals. |
clevel |
Confidence level of the residual envelopes. |
resqqplot |
Logical flag. It is is TRUE, a residual qqplot is carried out. |
nsim |
Number of simulations for the residual qqplot. |
nintLP |
Number of levels considered in the lurking variables. It is used as argument
nint in the call of the function |
tit |
Character string. A title for the plot. |
flow |
Argument f for the lowess smoother of the raw (or scaled) residual
plots, see |
addlow |
Logical flag. If it is TRUE, a lowess is added in the residual plots. |
histWgraph |
Logical flag. If it is TRUE, a new graphical device is opened
with the option |
plotDisp |
A vector of the form |
indgraph |
Logical flag. If it is TRUE, the validation plots (except the residual versus variables plots) in
|
scax |
Optional. Vector of two values indicating the range of values for the x-axis in the fitted and empirical rate plot. An adequate range is selected by default. |
scay |
Optional. Vector of two values indicating the range of values for the x-axis in the fitted and empirical rate plot. An adequate range is selected by default. |
legcex |
cex argument for the legend in the residual time plots
(see |
cores |
Optional. Number of cores of the computer to be used in the calculations. Default: one core is used. |
xlegend |
Argument xlegend used in the call of the function
|
fixed.seed |
An integer or NULL. It is the argument for |
If typeI="Overlapping", argument lint is compulsory. If typeI="Disjoint", only one of the arguments lint or nlint must be specified.
A list with the same elements that CalcRes.fun or
CalcResD.fun
(depending on the value of the argument typeI).
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
graphres.fun, graphrate.fun, resQQplot.fun,
graphResCov.fun, graphresU.fun
data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) modB<-fitPP.fun(tind=TRUE,covariates=covB, POTob=list(T=BarTxTn$Tx, thres=318), tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0,b5=0),CIty="Transf",modCI=TRUE, modSim=TRUE,dplot=FALSE) #Since only one graphical device is opened and the argument histWgraph is TRUE #by default, the different plots can be scrolled up and down with the "Page Up" #and "Page Down" keys. aux<-globalval.fun(mlePP=modB,lint=153, typeI="Disjoint", typeRes="Raw",typeResLV="Raw", resqqplot=FALSE) #If typeRes and typeResLV are not specified, Pearson residuals are calculated #by default. aux<-globalval.fun(mlePP=modB,lint=153, typeI="Disjoint", resqqplot=FALSE)data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) modB<-fitPP.fun(tind=TRUE,covariates=covB, POTob=list(T=BarTxTn$Tx, thres=318), tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0,b5=0),CIty="Transf",modCI=TRUE, modSim=TRUE,dplot=FALSE) #Since only one graphical device is opened and the argument histWgraph is TRUE #by default, the different plots can be scrolled up and down with the "Page Up" #and "Page Down" keys. aux<-globalval.fun(mlePP=modB,lint=153, typeI="Disjoint", typeRes="Raw",typeResLV="Raw", resqqplot=FALSE) #If typeRes and typeResLV are not specified, Pearson residuals are calculated #by default. aux<-globalval.fun(mlePP=modB,lint=153, typeI="Disjoint", resqqplot=FALSE)
This function calculates the empirical and the cumulative fitted occurrence rate of a PP on overlapping or disjoint intervals and plot them versus time.
graphrate.fun(objres = NULL, fittedlambda = NULL, emplambda = NULL, t = NULL, lint = NULL, typeI = "Disjoint", tit = "", scax = NULL, scay = NULL, xlegend = "topleft",histWgraph=TRUE)graphrate.fun(objres = NULL, fittedlambda = NULL, emplambda = NULL, t = NULL, lint = NULL, typeI = "Disjoint", tit = "", scax = NULL, scay = NULL, xlegend = "topleft",histWgraph=TRUE)
objres |
Optional. A list with (at least) elements fittedlambda, emplambda, t,
and typeI.
For example, the output from |
fittedlambda |
Optional. Numeric vector of the cumulative
fitted intensities |
emplambda |
Optional. Numeric vector of the empirical PP occurrence rates estimated over the considered intervals (usually divided by the length of the interval). |
t |
Optional. Time vector of the PP observation period. |
lint |
Optional. Length of the intervals used to calculate the empirical and the (cumulative) fitted occurrence intensities. |
typeI |
Label indicating the type ('Overlapping' or 'Disjoint') of the intervals. |
tit |
Character string. A title for the plot. |
scax |
Optional. Vector of two values giving the range of values for the x-axis. An adequate range is selected by default. |
scay |
Optional. Vector of two values giving the range of values for the y-axis. An adequate range is selected by default. |
xlegend |
Label indicating the position where the legend on the graph will be located. |
histWgraph |
Logical flag. If it is TRUE, a new graphical device is opened
with the option |
Either the argument objres or the set of arguments (fittedlambda, emplambda, t) must be specified. If objres is provided, fittedlambda, emplambda, t,lint and typeI are ignored.
In order to make comparable the empirical and the fitted occurrence rates, a cumulative fitted rate must be used. That means that argument fittedlambda must be the sum of the intensities fitted by the model over the same interval where the empirical rates have been calculated.
##plot of rates based on overlapping intervals graphrate.fun(emplambda=runif(500,0,1), fittedlambda=runif(500,0,1), t=c(1:500), lint=100, tit="Example", typeI="Overlapping") #plot of rates based on disjoint intervals graphrate.fun(emplambda=runif(50,0,1), fittedlambda=runif(50,0,1), t=c(1:50), lint=10, tit="Example", typeI="Disjoint") #Example using objres as input. In this example X1 has no influence on the rate; #consequently the fitted rate is almost a constant. X1<-rnorm(1000) modE<-fitPP.fun(tind=TRUE,covariates=cbind(X1), posE=round(runif(40,1,1000)), inddat=rep(1,1000), tim=c(1:1000), tit="Simulated example", start=list(b0=1,b1=0), modCI=FALSE,modSim=TRUE,dplot=FALSE) ResDE<-CalcResD.fun(mlePP=modE,lint=50) graphrate.fun(ResDE, tit="Example")##plot of rates based on overlapping intervals graphrate.fun(emplambda=runif(500,0,1), fittedlambda=runif(500,0,1), t=c(1:500), lint=100, tit="Example", typeI="Overlapping") #plot of rates based on disjoint intervals graphrate.fun(emplambda=runif(50,0,1), fittedlambda=runif(50,0,1), t=c(1:50), lint=10, tit="Example", typeI="Disjoint") #Example using objres as input. In this example X1 has no influence on the rate; #consequently the fitted rate is almost a constant. X1<-rnorm(1000) modE<-fitPP.fun(tind=TRUE,covariates=cbind(X1), posE=round(runif(40,1,1000)), inddat=rep(1,1000), tim=c(1:1000), tit="Simulated example", start=list(b0=1,b1=0), modCI=FALSE,modSim=TRUE,dplot=FALSE) ResDE<-CalcResD.fun(mlePP=modE,lint=50) graphrate.fun(ResDE, tit="Example")
This function plots residuals of a NHPP (raw or scaled, overlapping or disjoint) versus time or other variables which are monotonous functions.
graphres.fun(objres = NULL, typeRes = "Raw", t = NULL, res = NULL, lint = NULL, posE = NULL, fittedlambda = NULL, typeI = "Disjoint", Xvariables = NULL, namXv = NULL, histWgraph=TRUE, plotDisp=c(2,2), addlow = FALSE, lwd = 2, tit = "", flow = 0.5, xlegend = "topleft", legcex = 0.5)graphres.fun(objres = NULL, typeRes = "Raw", t = NULL, res = NULL, lint = NULL, posE = NULL, fittedlambda = NULL, typeI = "Disjoint", Xvariables = NULL, namXv = NULL, histWgraph=TRUE, plotDisp=c(2,2), addlow = FALSE, lwd = 2, tit = "", flow = 0.5, xlegend = "topleft", legcex = 0.5)
objres |
Optional. A list with (at least) elements t, typeI and Rawres and/or ScaRes, depending on
the value of typeRes. For example, the output list from the functions
|
typeRes |
Label indicating the type of residuals ("Raw" or any type of scaled residuals such as "Pearson"). |
t |
Optional. Time vector of the PP observation period. |
res |
Optional. Vector of residuals. |
lint |
Optional. Length of the intervals used to calculate the residuals. |
posE |
Optional. Numeric vector of the PP occurrence times. Only used when typeI = "Overlapping". |
fittedlambda |
Optional. Vector of the cumulative fitted PP intensity over the intervals. Used to calculate the envelopes when typeRes="Raw". |
typeI |
Label indicating the type ("Overlapping" or "Disjoint") of intervals. |
Xvariables |
Optional. Matrix of the variables for the residual plots (each column is a variable). |
namXv |
Optional. Vector of the names of the variables in Xvariables. |
histWgraph |
Logical flag. If it is TRUE, a new graphical device is opened
with the option |
plotDisp |
A vector of the form |
tit |
Character string. A title for the plots. |
addlow |
Logical flag. If it is TRUE, a lowess is added to the residual plots. |
lwd |
Argument lwd for plotting the lowess lines, see |
flow |
Argument f for the lowess, see |
xlegend |
Label giving the position of the graph where the legend will be located. |
legcex |
Argument cex for the legend, see |
Either argument objres or pair of arguments (t,res) must be specified. If objres is provided, arguments t,res, typeRes, typeI, posE and fittedlambda are ignored.
A residual plot versus time is always performed. These plots are intended for time or variables which are monotonous functions, since residuals are calculated over a given time interval and plotted versus the value of the variables in the mean point of the interval.
A smoother (lowess) of the residuals can be optionally added to the plots. In the case of overlapping intervals, the residuals of the occurrence points are marked differently from the rest. In the case typeRes="Raw" (if argument fittedlambda is available) or typeRes="Pearson", envelopes for the residuals are also plotted. The envelopes are based on an approach analogous to the one shown in Baddeley et al. (2005) for spatial Poisson processes. The envelopes for raw residuals are,
where index i runs over the integers in the interval .
The envelopes for the Pearson residuals are,
These plots allow us to analyze the effect on the intensity, of the covariates included in the model or other potentially influent variables. They show if the mean or the dispersion of the residuals vary sistematically, see for example residual analysis in Atkinson (1985) or Collett (1994).
Atkinson, A. (1985). Plots, transformations and regression. Oxford University Press.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B, 67, 617-666.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Collett, D. (1994). Modelling survival data in medical research. Chapman & Hall.
#Example using objres as input X1<-c(1:1000)**0.5 modE<-fitPP.fun(tind=TRUE,covariates=cbind(X1), posE=round(runif(40,1,1000)), inddat=rep(1,1000), tim=c(1:1000), tit="Simulated example", start=list(b0=1,b1=0), modSim = TRUE, dplot = FALSE) ResDE<-CalcResD.fun(mlePP=modE,lint=50) graphres.fun(objres=ResDE, typeRes="Raw", Xvariables=cbind(X1), namXv=c("X1"), plotDisp=c(2,1), addlow=TRUE,tit="Example") #Example using the set of arguments res, t and fittedlambda as input #In this case, with typeI="Disjoint", only values of t, fittedlambda and Xvariables #in the midpoint of the intervals must be provided. #Since a 1X1 layout is specified in plotDisp and only one #graphical device is opened by default, the two resulting plots can be scrolled #up and down with the "Page Up" and "Page Down" keys. X1<-c(1:500)**0.5 graphres.fun(res=rnorm(50),posE=round(runif(50,1,500)), fittedlambda=runif(500,0,1)[seq(5,495,10)], t=seq(5,495,10), typeRes="Raw", typeI="Disjoint",Xvariables=X1[seq(5,495,10)], namXv=c("X1"), plotDisp=c(1,1), tit="Example 2",lint=10)#Example using objres as input X1<-c(1:1000)**0.5 modE<-fitPP.fun(tind=TRUE,covariates=cbind(X1), posE=round(runif(40,1,1000)), inddat=rep(1,1000), tim=c(1:1000), tit="Simulated example", start=list(b0=1,b1=0), modSim = TRUE, dplot = FALSE) ResDE<-CalcResD.fun(mlePP=modE,lint=50) graphres.fun(objres=ResDE, typeRes="Raw", Xvariables=cbind(X1), namXv=c("X1"), plotDisp=c(2,1), addlow=TRUE,tit="Example") #Example using the set of arguments res, t and fittedlambda as input #In this case, with typeI="Disjoint", only values of t, fittedlambda and Xvariables #in the midpoint of the intervals must be provided. #Since a 1X1 layout is specified in plotDisp and only one #graphical device is opened by default, the two resulting plots can be scrolled #up and down with the "Page Up" and "Page Down" keys. X1<-c(1:500)**0.5 graphres.fun(res=rnorm(50),posE=round(runif(50,1,500)), fittedlambda=runif(500,0,1)[seq(5,495,10)], t=seq(5,495,10), typeRes="Raw", typeI="Disjoint",Xvariables=X1[seq(5,495,10)], namXv=c("X1"), plotDisp=c(1,1), tit="Example 2",lint=10)
This function performs lurking variable plots
for a set of variables. The function
graphResX.fun performs the lurking variable plot for one variable and
graphResCov.fun calls this function for a set of variables;
see graphResX.fun for details.
graphResCov.fun(Xvar, nint, mlePP, h = NULL, typeRes = "Pearson", namX = NULL, histWgraph=TRUE, plotDisp=c(2,2), tit = "")graphResCov.fun(Xvar, nint, mlePP, h = NULL, typeRes = "Pearson", namX = NULL, histWgraph=TRUE, plotDisp=c(2,2), tit = "")
Xvar |
Matrix of variables (each column is a variable). |
nint |
Number of intervals each covariate is divided into to perform the lurking variable plot. |
mlePP |
An object of class |
typeRes |
Label indicating the type of residuals ("Raw" or any type of scaled residuals such as "Pearson") used in the plots. |
h |
Optional. Weight function used to calculate the scaled residuals (if
typeRes is not equal to "Raw"). By default, Pearson residuals with
|
namX |
Optional. Vector of the names of the variables in Xvar. |
histWgraph |
Logical flag. If it is TRUE, a new graphical device is opened
with the option |
plotDisp |
A vector of the form |
tit |
Character string. A title for the plot. |
A list with elements
mXres |
Matrix of residuals (each column contains the residuals of a variable). |
mXm |
Matrix of mean values (each column contains the mean values of a variable in each interval). |
mXpc |
Matrix of the quantiles that define the intervals of each variable (each column contains the quantiles of one variable). |
nint |
Input argument. |
mlePP |
Input argument. |
Atkinson, A. (1985). Plots, transformations and regression. Oxford University Press.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67,617-666.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
#Simulated process without any relationship with variables Y1 and Y2 #The plots are performed dividing the variables into 50 intervals #Raw residuals. X1<-rnorm(500) X2<-rnorm(500) auxmlePP<-fitPP.fun(posE=round(runif(50,1,500)), inddat=rep(1,500), covariates=cbind(X1,X2),start=list(b0=1,b1=0,b2=0)) Y1<-rnorm(500) Y2<-rnorm(500) res<-graphResCov.fun(mlePP=auxmlePP, Xvar=cbind(Y1,Y2), nint=50, typeRes="Raw",namX=c("Y1","Y2"),plotDisp=c(2,1)) #If more variables were specified in the argument Xvar, with #the same 2X1 layout specified in plotDisp, the resulting plots could be #scrolled up and down with the "Page Up" and "Page Down" keys.#Simulated process without any relationship with variables Y1 and Y2 #The plots are performed dividing the variables into 50 intervals #Raw residuals. X1<-rnorm(500) X2<-rnorm(500) auxmlePP<-fitPP.fun(posE=round(runif(50,1,500)), inddat=rep(1,500), covariates=cbind(X1,X2),start=list(b0=1,b1=0,b2=0)) Y1<-rnorm(500) Y2<-rnorm(500) res<-graphResCov.fun(mlePP=auxmlePP, Xvar=cbind(Y1,Y2), nint=50, typeRes="Raw",namX=c("Y1","Y2"),plotDisp=c(2,1)) #If more variables were specified in the argument Xvar, with #the same 2X1 layout specified in plotDisp, the resulting plots could be #scrolled up and down with the "Page Up" and "Page Down" keys.
This function checks the properties that must be fulfilled by the uniform (generalized) residuals of a PP: uniform character and uncorrelation. Optionally, the existence of patterns versus covariates or potentially influent variables can be graphically analyzed.
graphresU.fun(unires, posE, Xvariables = NULL, namXv = NULL, flow = 0.5, tit = "", addlow = TRUE, histWgraph=TRUE, plotDisp=c(2,2), indgraph = FALSE)graphresU.fun(unires, posE, Xvariables = NULL, namXv = NULL, flow = 0.5, tit = "", addlow = TRUE, histWgraph=TRUE, plotDisp=c(2,2), indgraph = FALSE)
unires |
Numeric vector of the uniform residuals. |
posE |
Numeric vector of the occurrence times of the PP. |
Xvariables |
Matrix of variables to perform the residual plots (each column is a variable). |
namXv |
Optional. Vector of names of the variables in Xvariables. |
tit |
Character string. A title for the plot. |
addlow |
Logical flag. If it is TRUE, a lowess is added to the plots. |
flow |
Argument f for the lowess smoother; see |
histWgraph |
Logical flag. If it is TRUE, a new graphical device is opened
with the option |
plotDisp |
A vector of the form |
indgraph |
Logical flag. If it is TRUE, the validation plots (except the residuals versus variables
plots) are carried out in four1 |
The validation analysis of the uniform character consists in a uniform Kolmogorov-Smirnov test and a qqplot with a 95% confidence band based on a beta distribution. The analysis of the serial correlation is based on the Pearson correlation coefficient, Ljung-Box tests and a lagged serial correlation plot. An index plot of the residuals and residual plots versus the variables in argument Xvariables are performed to analyze the effect of covariates or other potentially influent variables. These plots will show if the mean or dispersion of the residuals vary sistematically, see model diagnostic of Cox-Snell residuals in Collett (1994) for more details.
Abaurrea, J., Asin, J., Cebrian, A.C. and Centelles, A. (2007). Modeling and forecasting extreme heat events in the central Ebro valley, a continental-Mediterranean area. Global and Planetary Change, 57(1-2), 43-58.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B, 67, 617-666.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Collett, D. (1994). Modelling survival data in medical research. Chapman \& Hall.
Ogata, Y. (1988). Statistical models for earthquake occurrences and residual analysis for point processes. Journal of the American Statistical Association, 83(401), 9-27.
#Since only one graphical device is opened and the argument histWgraph #is TRUE by default, the resulting residual plots (three pages with the #considered 1X2 layout for the residual versus variables plot) #can be scrolled up and down with the "Page Up" and "Page Down" keys. X1<-rnorm(500) X2<-rnorm(500) graphresU.fun(unires=runif(30,0,1),posE=round(runif(30,0,500)), Xvariables=cbind(X1,X2), namXv=c("X1","X2"),tit="Example",flow=0.7,plotDisp=c(1,2))#Since only one graphical device is opened and the argument histWgraph #is TRUE by default, the resulting residual plots (three pages with the #considered 1X2 layout for the residual versus variables plot) #can be scrolled up and down with the "Page Up" and "Page Down" keys. X1<-rnorm(500) X2<-rnorm(500) graphresU.fun(unires=runif(30,0,1),posE=round(runif(30,0,500)), Xvariables=cbind(X1,X2), namXv=c("X1","X2"),tit="Example",flow=0.7,plotDisp=c(1,2))
This function performs a lurking variable plot to analyze the residuals in terms of different levels of the variable.
graphResX.fun(X, nint, mlePP, typeRes = "Pearson", h = NULL, namX = NULL)graphResX.fun(X, nint, mlePP, typeRes = "Pearson", h = NULL, namX = NULL)
X |
Numeric vector, the variable for the lurking variable plot. |
nint |
Number of intervals or levels the variable is divided into. |
mlePP |
An object of class |
typeRes |
Label indicating the type of residuals ('Raw' or any type of scaled residuals such as 'Pearson'). |
h |
Optional. Weight function used to calculate the scaled residuals (if
typeRes is not equal to 'Raw'). By default, Pearson residuals with
|
namX |
Optional. Name of variable X. |
The residuals for different levels of the variable are analyzed.
For a variable , the considered levels are
where is the sample j-percentile of X. This type of plot is
specially useful for variables which are not a monotonous function of time.
In the case typeRes='Raw' or typeRes='Pearson', envelopes for the residuals are also plotted. The envelopes are based on an approach analogous to the one in Baddeley et al. (2005) for spatial Poisson processes. The envelopes for raw residuals are
where index i runs over the integers in the level ,
and is its length (number of observations in ).
The envelopes for the Pearson residuals are,
A list with elements
Xres |
Vector of residuals. |
xm |
Vector of the mean value of the variable in each interval. |
pc |
Vector of the quantiles that define the levels of the variable. |
typeRes |
Input argument. |
namX |
Input argument. |
lambdafit |
Input argument. |
posE |
Input argument. |
Atkinson, A. (1985). Plots, transformations and regression. Oxford University Press.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67, 617-666.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
##Simulated process not related to variable X ##Plots dividing the variable into 50 levels X1<-rnorm(500) X2<-rnorm(500) auxmlePP<-fitPP.fun(posE=round(runif(50,1,500)), inddat=rep(1,500), covariates=cbind(X1,X2),start=list(b0=1,b1=0,b2=0)) ##Raw residuals res<-graphResX.fun(X=rnorm(500),nint=50,mlePP=auxmlePP,typeRes="Raw") ##Pearson residuals res<-graphResX.fun(X=rnorm(500),nint=50,mlePP=auxmlePP,typeRes="Pearson")##Simulated process not related to variable X ##Plots dividing the variable into 50 levels X1<-rnorm(500) X2<-rnorm(500) auxmlePP<-fitPP.fun(posE=round(runif(50,1,500)), inddat=rep(1,500), covariates=cbind(X1,X2),start=list(b0=1,b1=0,b2=0)) ##Raw residuals res<-graphResX.fun(X=rnorm(500),nint=50,mlePP=auxmlePP,typeRes="Raw") ##Pearson residuals res<-graphResX.fun(X=rnorm(500),nint=50,mlePP=auxmlePP,typeRes="Pearson")
This function calculates, for each covariate in the model (except the intercept), the p-value of a likelihood ratio test comparing the original fitted NHPP with the model excluding that covariate from the linear predictor.
LRTpv.fun(mlePP)LRTpv.fun(mlePP)
mlePP |
An object of class |
A LRT is carried for all the covariates in the linear predictor except the intercept. If the model has not an intercept and there is only one covariate, no test can be carried out.
A matrix with one column, which contains the LRT p-values for all the covariates in the model (except the intercept)
fitPP.fun, testlik.fun, dropAIC.fun, addAIC.fun
data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) mod1B<-fitPP.fun(tind=TRUE,covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0,b5=0),dplot=FALSE, modCI=FALSE) LRTpv.fun(mod1B)data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) mod1B<-fitPP.fun(tind=TRUE,covariates=covB, posE=BarEv$Px, inddat=BarEv$inddat, tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0,b5=0),dplot=FALSE, modCI=FALSE) LRTpv.fun(mod1B)
"mlePP" for results of maximum likelihood estimation of Poisson processes with covariatesThis class encapsulates the output from the maximum likelihood estimation of a Poisson process where the intensity is modeled as a linear function of covariates.
Objects can be created by calls of the form new("mlePP", ...), but most often as the
result of a call to fitPP.fun.
call:Object of class "language". The call to fitPP.fun.
coef:Object of class "numeric". The estimated coefficientes of the model.
fullcoef:Object of class "numeric". The full coefficient vector, including the fixed
parameters of the model. It has an attribute, called 'TypeCoeff' which shows the names of the fixed parameters.
vcov:Object of class "matrix". Approximate variance-covariance matrix of the estimated coefficients.
It has an attribute, called 'CalMethod' which shows the method used to calcualte the inverse of the information matrix:
'Solve function', 'Cholesky', 'Not possible' or 'Not required' if modCI=FALSE.
min:Object of class "numeric". Minimum value of objective function, that is the
negative of the loglikelihood function.
details:Object of class "list". The output returned from optim.
If nlminb is used to minimize the function, it is NULL.
minuslogl:Object of class "function". The negative of the loglikelihood function.
nobs:Object of class "integer". The number of observations.
method:Object of class "character". It is a bit different from the slot in the extended
class mle: here,
it is the input argument minfun of fitPP.fun instead of the method used
in optim (this information already appears in details).
detailsb:Object of class "list".The output returned from nlminb.
If optim is used to minimize the function, it is NULL.
npar:Object of class "integer". Number of estimated parameters.
inddat:Object of class "numeric". Input argument of fitPP.fun.
lambdafit:Object of class "numeric". Vector of the fitted intensity .
LIlambda:Object of class "numeric". Vector of lower limits of the CI.
UIlambda:Object of class "numeric". Vector of upper limits of the CI.
convergence:Object of class "integer". A code of convergence. 0 indicates successful convergence.
posE:Object of class "numeric". Input argument of fitPP.fun.
covariates:Object of class "matrix". Input argument of fitPP.fun.
tit:Object of class "character". Input argument of fitPP.fun.
tind:Object of class "logical". Input argument of fitPP.fun.
t:Object of class "numeric". Input argument of fitPP.fun.
Class "mle", directly.
Most of the S4 methods in stats4 for the S4-class mle can be used. Also a mle method
for the generic function extractAIC and a version of the profile
mle method adapted to the mlePP objects are available:
signature(object = "mle")
signature(object = "mle")
signature(object = "mle")
signature(object = "mle")
signature(object = "mle")
signature(object = "mle")
signature(object = "mle")
signature(object = "mle")
signature(object = "mle")
signature(fitted = "mlePP")
Some other generic functions related to fitted models, such as AIC or BIC, can also
be applied to mlePP objects.
Let us remind that, as in all the S4-classes, the symbol @ must be used instead of $ to name the slots: mlePP@covariates, mlepp@lambdafit, etc.
showClass("mlePP")showClass("mlePP")
This function calculates the characteristics of the extreme events
of a series defined using a peak over threshold (POT) method
with an extreme threshold. The initial and the maximum intensity positions,
the mean excess, the maximum excess and the length of each event are calculated.
POTevents.fun(T, thres, date = NULL)POTevents.fun(T, thres, date = NULL)
T |
Numeric vector, the series |
thres |
Threshold value used to define the extreme events. |
date |
Optional. A vector or matrix indicating the date of each observation. |
One of the elements of the output from this function is a vector (inddat) which marks the observations that should be used in the estimation of a point process, resulting from a POT approach. The observations to be considered in the estimation are marked with 1 and correspond to the non occurrence observations and to a single occurrence point per event. The occurence point is defined as the point where maximum intensity of the event occurs.The observations in an extreme event which are not the occurrence point are marked with 0 and treated as non observed.
A list with components
Pi |
Vector of the initial points of the extreme events. |
datePi |
Date of the initial points Pi. |
Px |
Vector of the points of maximum excess of the extreme events. |
datePx |
Vector of the date of the maximum excess points Px. |
Im |
Vector of the mean excesses (over the threshold) of the extreme events. |
Ix |
Vector of the maximum excesses (over the threshold) of the extreme events. |
L |
Vector of the lengths of the extreme events. |
inddat |
Index equal to 1 in the observations used in the estimation process and to 0 in the others. |
data(BarTxTn) dateB<-cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$diames) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=dateB)data(BarTxTn) dateB<-cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$diames) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=dateB)
mlePP for Function profile
Method for generic function profile for objects of the S4-class
mlePP. It is almost identical to the method mle for this function in stats4,
but small changes have to be done due to the differences in the arguments of the functions
mle and fitPP.fun. In order to profile an mlePP object, its vcov slot cannot be missing.
That means that if the function fitPP.fun is used to create the object, the argument modCI=TRUE must be used.
signature(fitted = "mlePP")This function performs a qqplot comparing the empirical quantiles of the residuals with the expected quantiles under the fitted NHPP, calculated by a Monte Carlo approach.
It calls the auxiliary function resSim.fun
(not intended for the users), see Details section.
resQQplot.fun(nsim, objres, covariates, clevel = 0.95, cores = 1, tit ="", fixed.seed=NULL, histWgraph=TRUE)resQQplot.fun(nsim, objres, covariates, clevel = 0.95, cores = 1, tit ="", fixed.seed=NULL, histWgraph=TRUE)
nsim |
Number of simulations for the calculations. |
objres |
A list with the same elements of the output list from the
function |
covariates |
Matrix of covariates to fit the NHPP (each column is a covariate). |
clevel |
Confidence level of the residual envelope. |
cores |
Optional. Number of cores of the computer to be used in the calculations. Default: one core is used. |
tit |
Character string. A title for the plot. |
fixed.seed |
An integer or NULL. If it is an integer, that is the value used to set the seed in random generation processes. It it is NULL, a random seed is used. |
histWgraph |
Logical flag. Only used in Windows platforms. If it is TRUE, a new graphical device is opened
with the option |
The expected quantiles are calculated as the median values of the simulated samples.
Confidence intervals for each quantile with pointwise significance level
clevel are calculated as quantiles of probability 1-clevel /2 and clevel/2
of the simulated sample for each residual.
All type of residuals (disjoint or overlapping and Pearson or raw residuals) are supported by this function. However, the qqplot for overlapping residuals can be a high time consuming process. So, disjoint residuals should be prefered in this function.
The auxiliary function resSim.fun generates a NHPP with intensity ,
fits the model using the covariate matrix and calculates the residuals.
A list with elements
resmed |
Numeric vector containing the mean of the simulated residuals in each point. |
ressup |
Numeric vector of the upper values of the simulated envelopes. |
resinf |
Numeric vector of the lower values of the simulated envelopes. |
objres |
Input argument. |
nsim |
Input argument. |
fixed.seed |
Input argument. |
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
X1<-rnorm(500) X2<-rnorm(500) aux<-fitPP.fun(tind=TRUE,covariates=cbind(X1,X2), posE=round(runif(40,1,500)), inddat=rep(1,500), tim=c(1:500), tit="Simulated example", start=list(b0=1,b1=0,b2=0),dplot=FALSE) auxRes<-CalcResD.fun(mlePP=aux,lint=50) #if we want reproducible results, we can fixed the seed in the generation process #(the number of cores used in the calculations must also be the same to reproduce # the result) auxqq<-resQQplot.fun(nsim=50,objres=auxRes, covariates=cbind(X1,X2), fixed.seed=123)X1<-rnorm(500) X2<-rnorm(500) aux<-fitPP.fun(tind=TRUE,covariates=cbind(X1,X2), posE=round(runif(40,1,500)), inddat=rep(1,500), tim=c(1:500), tit="Simulated example", start=list(b0=1,b1=0,b2=0),dplot=FALSE) auxRes<-CalcResD.fun(mlePP=aux,lint=50) #if we want reproducible results, we can fixed the seed in the generation process #(the number of cores used in the calculations must also be the same to reproduce # the result) auxqq<-resQQplot.fun(nsim=50,objres=auxRes, covariates=cbind(X1,X2), fixed.seed=123)
This function generates the occurrence times of the points
of a NHPP with a given time-varying intensity ,
in a period (0, T). The length of argument lambda determines T,
the length of the observation period.
It calls the auxiliary function buscar (not intended
for the users), see Details section.
simNHP.fun(lambda, fixed.seed=NULL)simNHP.fun(lambda, fixed.seed=NULL)
lambda |
Numeric vector, the time varying intensity |
fixed.seed |
An integer or NULL. If it is an integer, that is the value used to set the seed in random generation processes. It it is NULL, a random seed is used. |
The generation of the NHPP points consists in two steps.
First, the points of a homogeneous PP of intensity 1 are generated using
independent exponentials. Then, the homogeneous occurrence times are transformed into
the points of a non homogeneous process with intensity .
This transformation is performed by the auxiliary function buscar
(not intended for the user).
A list with elements
posNH |
Numeric vector of the occurrences times of the NHPP generated in the observation period (0,T). |
lambda |
Input argument. |
fixed.seed |
Input argument. |
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Ross, S.M. (2006). Simulation. Academic Press.
#Generation of the occurrence times of a homogeneours PP with constant intensity #0.01 in a period of time of length 1000 aux<-simNHP.fun(lambda=rep(0.01,1000)) aux$posNH #if we want reproducible results, we can fixed the seed in the generation process aux<-simNHP.fun(lambda=rep(0.01,1000),fixed.seed=123) aux$posNH #and the result is: # [1] 85 143 275 279 284 316 347 362 634 637 738 786 814 852 870 955 #Generation of the occurrence times of a NHPP with time-varying intensity t in #a period of time of length 500 t<-runif(500, 0.01,0.1) aux<-simNHP.fun(lambda=t) aux$posNH#Generation of the occurrence times of a homogeneours PP with constant intensity #0.01 in a period of time of length 1000 aux<-simNHP.fun(lambda=rep(0.01,1000)) aux$posNH #if we want reproducible results, we can fixed the seed in the generation process aux<-simNHP.fun(lambda=rep(0.01,1000),fixed.seed=123) aux$posNH #and the result is: # [1] 85 143 275 279 284 316 347 362 634 637 738 786 814 852 870 955 #Generation of the occurrence times of a NHPP with time-varying intensity t in #a period of time of length 500 t<-runif(500, 0.01,0.1) aux<-simNHP.fun(lambda=t) aux$posNH
Performs stepwise model selection by AIC for Poisson proces models estimated by maximum likelihood.
It calls the auxiliary function checkdim (not intended
for the users).
stepAICmle.fun(ImlePP, covariatesAdd = NULL, startAdd = NULL, direction = "forward", ...)stepAICmle.fun(ImlePP, covariatesAdd = NULL, startAdd = NULL, direction = "forward", ...)
ImlePP |
A |
covariatesAdd |
Matrix of the potential covariates to be added to the model; each column must
contain a covariate. In the 'forward' and the 'both' directions, it is compulsory to assign a matrix to this argument.
It is advisable to give names to the columns of
this matrix (using |
startAdd |
Optional. The vector of initial values for the estimation of the coefficients of each potential covariate. If it is NULL, initial values equal to 0 are used. |
direction |
Label indicating the direction of the algortihm: 'forward' (the default), 'backward' or 'both'. |
... |
Further arguments to pass to |
Three directions, forward, backward and both, are implemented. The initial model is given by
ImlePP and the algorithm stops
when none of the covariates eliminated from the model
or added from the potential covariates set (argument covariatesAdd ) improves the model
fitted in the previous step, according to the AIC. For the 'both' and 'forward' directions, the argument covariatesADD
is compulsary, and the default NULL leads to an error.
In the 'both' direction, 'forward' and 'backward' steps are carried out alternatively. In the 'forward' direction, the initial model usually contains only the intercept.
A mlePP-class object, the fit of the final PP model selectecd by the algorithm.
Casella, G. and Berger, R.L., (2002). Statistical inference. Brooks/Cole.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. Springer.
addAIC.fun, dropAIC.fun, testlik.fun
data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) #The initial model contains only the inercept mod1Bind<-fitPP.fun(covariates=NULL, posE=BarEv$Px, inddat=BarEv$inddat, tit='BAR Intercept ', start=list(b0=1)) #the potential covariates covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) dimnames(covB)<-list(NULL,c('cos','sin','TTx','Txm31', 'Txm31**2')) bb<-stepAICmle.fun(ImlePP=mod1Bind, covariates=covB, startAdd=c(1,-1,0,0,0), direction='both')data(BarTxTn) BarEv<-POTevents.fun(T=BarTxTn$Tx,thres=318, date=cbind(BarTxTn$ano,BarTxTn$mes,BarTxTn$dia)) #The initial model contains only the inercept mod1Bind<-fitPP.fun(covariates=NULL, posE=BarEv$Px, inddat=BarEv$inddat, tit='BAR Intercept ', start=list(b0=1)) #the potential covariates covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) dimnames(covB)<-list(NULL,c('cos','sin','TTx','Txm31', 'Txm31**2')) bb<-stepAICmle.fun(ImlePP=mod1Bind, covariates=covB, startAdd=c(1,-1,0,0,0), direction='both')
This function performs a likelihood ratio test, a test to compare the fit of two models, where the first one (the null model ModR) is a particular case of the other (the alternative model ModG).
testlik.fun(ModG, ModR)testlik.fun(ModG, ModR)
ModG |
An object of class |
ModR |
An object of class |
The test statistic is twice the difference in the log-likelihoods
of the models.
Under the null, the statistic follows a distribution with degrees
of freedom df2-df1,the number of parameters of modG and modR respectively.
A list with elements
pv |
P-value of the likelihood ratio test. |
ModG |
Input argument. |
ModR |
Input argument. |
Casella, G. and Berger, R.L., (2002). Statistical inference. Brooks/Cole.
##The alternative model modB is specified by the output fitPP.fun ##The null model modBR is specified by a list with elments llik and npar data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) modB<-fitPP.fun(tind=TRUE,covariates=covB, POTob=list(T=BarTxTn$Tx, thres=318), tim=c(1:8415), tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0,b5=0),dplot=FALSE,modCI=TRUE, modSim=TRUE) modBR<-fitPP.fun(tind=TRUE,covariates=covB[,1:4], POTob=list(T=BarTxTn$Tx, thres=318), tim=c(1:8415), tit="BAR Tx; cos, sin, TTx, Txm31", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0),dplot=FALSE,modCI=TRUE, modSim=TRUE) aux<-testlik.fun(ModG=modB,ModR=modBR)##The alternative model modB is specified by the output fitPP.fun ##The null model modBR is specified by a list with elments llik and npar data(BarTxTn) covB<-cbind(cos(2*pi*BarTxTn$dia/365), sin(2*pi*BarTxTn$dia/365), BarTxTn$TTx,BarTxTn$Txm31,BarTxTn$Txm31**2) modB<-fitPP.fun(tind=TRUE,covariates=covB, POTob=list(T=BarTxTn$Tx, thres=318), tim=c(1:8415), tit="BAR Tx; cos, sin, TTx, Txm31, Txm31**2", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0,b5=0),dplot=FALSE,modCI=TRUE, modSim=TRUE) modBR<-fitPP.fun(tind=TRUE,covariates=covB[,1:4], POTob=list(T=BarTxTn$Tx, thres=318), tim=c(1:8415), tit="BAR Tx; cos, sin, TTx, Txm31", start=list(b0=-100,b1=1,b2=10,b3=0,b4=0),dplot=FALSE,modCI=TRUE, modSim=TRUE) aux<-testlik.fun(ModG=modB,ModR=modBR)
This function transforms the points of a NHPP into
the occurrence points
of a HPP of rate 1.
transfH.fun(mlePP)transfH.fun(mlePP)
mlePP |
An object of class |
Transformation of the NHPP points into
the HPP points is based on
the time scale transformation,
(usually the estimated value is used in the transformation.)
A list with elements
posEH |
Numeric vector of the transformed occurrence times of the HPP. |
posE |
Slot of the input argument mlePP. |
lambdafit |
Slot of the input argument mlePP. |
inddat |
Slot of the input argument mlePP. |
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Cox, D.R., Isham, V., 1980. Point Processes. Chapman and Hall.
Daley, D. and D. Vere-Jones (2003). An Introduction to the Theory of Point Processes. Springer.
X1<-rnorm(500) X2<-rnorm(500) auxmlePP<-fitPP.fun(posE=round(runif(50,1,500)), inddat=rep(1,500), covariates=cbind(X1,X2),start=list(b0=1,b1=0,b2=0)) posEH<-transfH.fun(auxmlePP)X1<-rnorm(500) X2<-rnorm(500) auxmlePP<-fitPP.fun(posE=round(runif(50,1,500)), inddat=rep(1,500), covariates=cbind(X1,X2),start=list(b0=1,b1=0,b2=0)) posEH<-transfH.fun(auxmlePP)
This function calculates the exponential and the uniform (generalized)
residuals of a
HPP, using the occurrence points .
unifres.fun(posEH)unifres.fun(posEH)
posEH |
Numeric vector, the occurrence points of a HPP. |
The exponential residuals of a HPP are defined as the inter-event distances
, that are an i.i.d. exponential sample. The series
is an example of the generalized residuals proposed by Cox and Snell (1968).
The uniform residuals, defined as the function
of the exponential residuals, are an i.i.d. uniform sample, see Ogata (1988).
A list with elements
expres |
Numeric vector of the exponential residuals. |
unires |
Numeric vector of the uniform residuals. |
posEH |
Input argument. |
Abaurrea, J., Asin, J., Cebrian, A.C. and Centelles, A. (2007). Modeling and forecasting extreme heat events in the central Ebro valley, a continental-Mediterranean area. Global and Planetary Change, 57(1-2), 43-58.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
Cox, D. R. and Snell, E. J. (1968). A general definition of residuals. Journal of the Royal Statistical Society, series B, 30(2), 248-275. 83(401), 9-27.
Ogata, Y. (1988). Statistical models for earthquake occurrences and residual analysis for point processes.Journal of the American Statistical Association, 83(401), 9-27.
## generates the occurrence times of a homogeneours PP with constant intensity 0.01 ## and calculates de residuals aux<-simNHP.fun(lambda=rep(0.01,1000)) res<-unifres.fun(aux$posNH)## generates the occurrence times of a homogeneours PP with constant intensity 0.01 ## and calculates de residuals aux<-simNHP.fun(lambda=rep(0.01,1000)) res<-unifres.fun(aux$posNH)
This function estimates the covariance matrix of the ML estimators of the
parameters, using the asymptotic distribution and properties of the ML estimators.
VARbeta.fun(covariates, lambdafit)VARbeta.fun(covariates, lambdafit)
covariates |
Matrix of covariates (each column is a covariate). |
lambdafit |
Numeric vector, the fitted PP intensity |
The covariance matrix is calculated as the inverse of the negative of the hessian matrix. The inverse of the matrix
is calculated using the solve function. If this function leads to an error in the calculation, the
inverse is calculated via its Cholesky decomposition. If this option also fails,
the covariance matrix is not estimated and a matrix of dimension is returned.
VARbeta |
Coariance matrix of the |
The function fitPP.fun calls this function.
Casella, G. and Berger, R.L., (2002). Statistical inference. Brooks/Cole.
Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.
lambdafit<-runif(100,0,1) X<-cbind(rep(1,100),rnorm(100),rnorm(100)) aux<-VARbeta.fun(covariates=X, lambdafit=lambdafit)lambdafit<-runif(100,0,1) X<-cbind(rep(1,100),rnorm(100),rnorm(100)) aux<-VARbeta.fun(covariates=X, lambdafit=lambdafit)