DiagStempCens {StempCens} | R Documentation |
Return measures and graphics for diagnostic analysis in spatio-temporal model with censored/missing responses.
DiagStempCens(Est.StempCens, type.diag = "individual", diag.plot = TRUE, ck = 3)
Est.StempCens |
an object of class |
type.diag |
type of diagnostic: ' |
diag.plot |
|
ck |
The value for |
This function uses the case deletion approach to study the impact of deleting one or more observations from the dataset on the parameters estimates, using the idea of Cook (1977). The measure is defined by
GD_i(θ*)=(θ* - θ*[i])'[-Q**(θ|θ*)](θ* - θ*[i]), i=1,....m,
where θ* is the estimate of θ using the complete data, θ*[i] are the estimates obtained after deletion of the i-th observation (or group of observations) and Q**(θ|θ*) is the Hessian matrix.
We can eliminate an observation, an entire location or an entire time index.
The function returns a list with the diagnostic measures.
type.diag == individual | time | location
:GD
is a data.frame with the index value of the observation and the GD measure.
type.diag == all
:GDind
is a data.frame with the index value of the observation and the GD measure for individual.
GDtime
is a data.frame with the time index value and the GD measure for time.
GDloc
is a data.frame with the side index value and the GD measure for location.
Katherine A. L. Valeriano, Victor H. Lachos and Larissa A. Matos
# Initial parameter values beta <- c(-1,1.5); phi <- 3; rho <- 0.40; tau2 <- 1; sigma2 <- 2 # Simulating data n1 <- 8 # Number of spatial locations n2 <- 4 # Number of temporal index set.seed(700) x.coord <- round(runif(n1,0,10),9) # X coordinate y.coord <- round(runif(n1,0,10),9) # Y coordinate coordenadas <- cbind(x.coord,y.coord) # Cartesian coordinates without repetitions coord2 <- cbind(rep(x.coord,each=n2),rep(y.coord,each=n2)) # Cartesian coordinates with repetitions time <- as.matrix(seq(1,n2,1)) # Time index without repetitions time2 <- as.matrix(rep(time,n1)) # Time index with repetitions x1 <- rexp(n1*n2,2) x2 <- rnorm(n1*n2,2,1) x <- cbind(x1,x2) media <- x%*%beta # Covariance matrix H <- as.matrix(dist(coordenadas)) # Spatial distances Mt <- as.matrix(dist(time)) # Temporal distances Cov <- CovarianceM(phi,rho,tau2,sigma2,distSpa=H,disTemp=Mt,kappa=0,type.S="gaussian") # Data require(mvtnorm) y <- as.vector(rmvnorm(1,mean=as.vector(media),sigma=Cov)) perc <- 0.1 aa=sort(y); bb=aa[1:(perc*n1*n2)]; cutof<-bb[perc*n1*n2] cc=matrix(1,(n1*n2),1)*(y<=cutof) y[cc==1] <- cutof y[17] <- abs(y[17])+2*sd(y) # Estimation est <- EstStempCens(y, x, cc, time2, coord2, inits.phi=2.5, inits.rho=0.5, inits.tau2=0.8, type.Data="balanced", cens.type="left", method="nlminb", kappa=0, type.S="gaussian", IMatrix=TRUE, lower.lim=c(0.01,-0.99,0.01), upper.lim=c(30,0.99,20), M=20, perc=0.25, MaxIter=10, pc=0.2, error = 10^-6) # Diagnostic diag <- DiagStempCens(est, type.diag="time", diag.plot = TRUE, ck=1)