Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/DiagStempCens.R
Return measures and graphics for diagnostic analysis in spatio-temporal model with censored/missing responses.
1 | DiagStempCens(Est.StempCens, type.diag = "individual", diag.plot = TRUE, ck)
|
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 ideas of \insertCitecook1977detection;textualStempCens and \insertCitezhu2001case;textualStempCens. 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 L. Valeriano, Victor H. Lachos and Larissa A. Matos
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ## 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)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.