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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)
an object of class Est.StempCens
given as output by the EstStempCens
function. In the EstStempCens
function, IMatrix
must be TRUE
.
type of diagnostic: 'individual
' is related when one observation is deleted,
'time
' is related when an entire time is deleted, 'location
' is related when an entire location is deleted and
'all
' the three cases ('individual
', 'time
' and 'location
').
By default type.diag
is individual
.
TRUE
or FALSE
. It indicates if diagnostic plots must be showed. By default = TRUE
.
the value for ck
considered in the benchmark value for the index plot:
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.
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 ideas of cook1977detection;textualStempCens and zhu2001case;textualStempCens. The measure is defined by
where
We can eliminate an observation, an entire location or an entire time index.
# NOT RUN {
# Initial parameter values
beta <- c(-1,1.5)
phi <- 3; rho <- 0.40
tau2 <- 1; sigma2 <- 2
# Simulating data
n1 <- 5 # Number of spatial locations
n2 <- 5 # Number of temporal index
set.seed(98765)
x.co <- round(runif(n1,0,10),9) # X coordinate
y.co <- round(runif(n1,0,10),9) # Y coordinate
coord <- cbind(x.co,y.co) # Cartesian coordinates without repetitions
coord2 <- cbind(rep(x.co,each=n2),rep(y.co,each=n2)) # Cartesian coordinates with repetitions
time <- as.matrix(seq(1,n2)) # 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
Ms <- as.matrix(dist(coord)) # Spatial distances
Mt <- as.matrix(dist(time)) # Temporal distances
Cov <- CovarianceM(phi,rho,tau2,sigma2,Ms,Mt,0,"exponential")
# Data
require(mvtnorm)
y <- as.vector(rmvnorm(1,mean=as.vector(media),sigma=Cov))
perc <- 0.20
aa <- sort(y); bb <- aa[((1-perc)*n1*n2+1):(n1*n2)]; cutof <- bb[1]
cc <- matrix(1,(n1*n2),1)*(y>=cutof)
y[cc==1] <- cutof
y[17] <- abs(y[17])+2*sd(y)
LI <- y
LS <- y; LS[cc==1] <- Inf # Right-censored
# Estimation
set.seed(74689)
est <- EstStempCens(y, x, cc, time2, coord2, LI, LS, init.phi=2.5, init.rho=0.5, init.tau2=0.8,
type.Data="balanced", method="nlminb", kappa=0, type.S="exponential",
IMatrix=TRUE, lower.lim=c(0.01,-0.99,0.01), upper.lim=c(30,0.99,20), M=20,
perc=0.25, MaxIter=300, pc=0.20)
# Diagnostic
set.seed(12345)
diag <- DiagStempCens(est, type.diag="time", diag.plot = TRUE, ck=1)
# }
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