addAIC.fun: Calculate the AIC for all one-covariate additions to the...

In NHPoisson: Modelling and Validation of Non Homogeneous Poisson Processes

Description

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.

Usage

addAIC.fun(mlePP, covariatesAdd, startAdd = NULL, modSim = FALSE,...)

Arguments

mlePP	A "mlePP"-class object; usually the output from fitPP.fun. It defines the current model. The fitted model cannot include fixed parameters.
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 start of fitPP.fun, startAdd is a numeric vector not a list.
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 AIC, for example the constant k for the AIC calculation.

Details

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.

Value

A list with the following components

AICadd	Vector of the AIC values obtained from adding to the current model each covariate in covariatesAdd.
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 stepAICmle.fun.

See Also

dropAIC.fun, stepAICmle.fun, LRTpv.fun

Examples

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)

NHPoisson documentation built on Feb. 19, 2020, 5:07 p.m.