View source: R/localinfmeasfin.R
localinfmeas | R Documentation |
It computes some measures and plots to asses the local influence of outliers in the SAEM spatial estimation for censored spatial observations, for six types of covariance functions (est$type): "exponential", "matern", "gauss", "spherical","powered.exponential" or "stable" and "cauchy".
localinfmeas(est, fix.nugget = TRUE, diag.plot = TRUE, type.plot = "all", c = 3)
est |
object of the class "SAEMSpatialCens". See |
fix.nugget |
(logical) it indicates if the τ^2 parameter must be fixed. |
diag.plot |
(logical) it indicates if diagnostic plots must be showed. |
type.plot |
type of plot (all: all graphics, rp: response perturbation,smp: scale matrix perturbation, evp: explanatory variable perturbation). |
c |
constant used for fixing the limit of detection (benchmark value). |
this function uses the Maximum likelihood expectation (MLE) under three perturbation schemes, in the response (M(0)_y), scale matrix (M(0)_{Σ}) and explanatory variables (M(0)_X), to detect the influence of outliers in the SAEM estimation procedure.
in addition to the diagnostic graphics (response, scale matrix and explanatory variable schemes, respectively), the function returns the next values.
Qwrp |
negative Q_{ω_0} matrix under the response perturbation scheme. |
Qwsmp |
negative Q_{ω_0} matrix under the scale matrix perturbation scheme. |
Qwevp |
negative Q_{ω_0} matrix under the explanatory variable perturbation scheme. |
respper |
data.frame containing an indicator of the presence of atypical values and the M(0) values for the response perturbation scheme. |
smper |
data.frame containing an indicator of the presence of atypical values and the M(0) values for the scale matrix perturbation scheme. |
expvper |
a data.frame containing an indicator of the presence of atypical values and the M(0) values for the explanatory variable perturbation scheme. |
limrp |
limit of detection for outliers for the response perturbation scheme. |
limsmp |
limit of detection for outliers for the scale matrix perturbation scheme. |
limevp |
limit of detection for outliers for the explanatory variable perturbation scheme. |
Alejandro Ordonez <<ordonezjosealejandro@gmail.com>>, Victor H. Lachos <<hlachos@ime.unicamp.br>> and Christian E. Galarza <<cgalarza88@gmail.com>>
Maintainer: Alejandro Ordonez <<ordonezjosealejandro@gmail.com>>
Cook, R. D. (1986). Assessment of local influence. Journal of the Royal Statistical Society, Series B,, 48, 133-169.
Zhu, H., Lee, S., Wei, B. & Zhou, J. (2001). Case-deletion measures for models with incomplete data. Biometrika, 88, 727-737.
SAEMSCL
require(geoR) data("Missouri") data=Missouri data$V3=log((data$V3)) cc=data$V5 y=data$V3 n=127 k=1 datare1=data coords=datare1[,1:2] data1=data.frame(coords,y) data1=data1[cc==0,] geodata=as.geodata(data1,y.col=3,coords.col=1:2) v=variog(geodata) v1=variofit(v) cov.ini=c(0,2) est=SAEMSCL(cc,y,cens.type="left",trend="cte",coords=coords,M=15,perc=0.25, MaxIter=5,pc=0.2,cov.model="exponential",fix.nugget=TRUE,nugget=2, inits.sigmae=cov.ini[2],inits.phi=cov.ini[1], search=TRUE,lower=0.00001,upper=100) w=localinfmeas(est,fix.nugget=TRUE,c=3) res=w$respper res[res[,1]=="atypical obs",] sm=w$smper sm[sm[,1]=="atypical obs",] ev=w$expvper ev[ev[,1]=="atypical obs",] ##############ANOTHER EXAMPLE######### n<-200 ### sample size for estimation n1=100 ### number of observation used in the prediction ###simulated coordinates r1=sample(seq(1,30,length=400),n+n1) r2=sample(seq(1,30,length=400),n+n1) coords=cbind(r1,r2) coords1=coords[1:n,] cov.ini=c(0.2,0.1) type="exponential" xtot=as.matrix(rep(1,(n+n1))) xobs=xtot[1:n,] beta=5 ###simulated data obj=rspacens(cov.pars=c(3,.3,0),beta=beta,x=xtot,coords=coords,cens=0.25,n=(n+n1), n1=n1,cov.model=type,cens.type="left") data2=obj$datare cc=obj$cc y=obj$datare[,3] ##### generating atypical observations### y[91]=y[91]+4 y[126]=y[126]+4 y[162]=y[162]+4 coords=obj$datare[,1:2] ###initial values### cov.ini=c(0.2,0.1) est=SAEMSCL(cc,y,cens.type="left",trend="cte",coords=coords,M=15,perc=0.25, MaxIter=10,pc=0.2,cov.model=type,fix.nugget=TRUE,nugget=0,inits.sigmae=cov.ini[1], inits.phi=cov.ini[2],search=TRUE,lower=0.00001,upper=50) w=localinfmeas(est,fix.nugget=TRUE,c=3) res=w$respper res[res[,1]=="atypical obs",] sm=w$smper sm[sm[,1]=="atypical obs",] ev=w$expvper ev[ev[,1]=="atypical obs",]
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