Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates the parametric bootstrap mean squared error estimates of the spatial EBLUPs obtained by fitting the spatial FayHerriot model, in which area effects follow a Simultaneously Autorregressive (SAR) process.
1 2 
formula 
an object of class 
vardir 
vector containing the 
proxmat 

B 
number of bootstrap replicates. Default value is 
method 
type of fitting method, to be chosen between 
MAXITER 
maximum number of iterations allowed for the Fisherscoring algorithm. Default value is 
PRECISION 
convergence tolerance limit for the Fisherscoring algorithm. Default value is 
data 
optional data frame containing the variables named in 
This function uses random number generation. To fix the seed, use set.seed
.
A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.
A formula has an implied intercept term. To remove this use either y ~ x  1 or y ~ 0 + x. See formula
for more details of allowed formulae.
The function returns a list with the following objects:
est 
a list with the results of the estimation process: 
mse 
data frame containing the naive parametric bootstrap mean squared error estimates ( 
In case that formula
, vardir
or proxmat
contain NA values a message is printed and no action is done.
Isabel Molina, Monica Pratesi and Nicola Salvati.
 Small Area Methods for Poverty and Living Conditions Estimates (SAMPLE), funded by European Commission, Collaborative Project 217565, Call identifier FP7SSH20071.
 Molina, I., Salvati, N. and Pratesi, M. (2009). Bootstrap for estimating the MSE of the Spatial EBLUP. Computational Statistics 24, 441458.
1 2 3 4 5 6 7 8  data(grapes) # Load data set
data(grapesprox) # Load proximity matrix
# Obtain the fitting values, naive and biascorrected parametric bootstrap MSE estimates
# using REML method
set.seed(123)
result < pbmseSFH(grapehect ~ area + workdays  1, var, grapesprox, B=2, data=grapes)
result

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