Package 'saeRobust' reference manual

Title:	Robust Small Area Estimation
Description:	Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.
Authors:	Sebastian Warnholz [aut, cre]
Maintainer:	Sebastian Warnholz <[email protected]>
License:	MIT + file LICENSE
Version:	0.5.0
Built:	2024-11-11 07:07:02 UTC
Source:	CRAN

Fit model on Bootstrap sample

Description

These functions help to repeatedly fit a rfh model on bootstrap samples. Use bootstrap as a user interface. boot can be used to extend the framework but is not meant to be used interactively. If you are interested in the parameteric bootstrap for a 'rfh' model you can use the implementation in mse.

Usage

bootstrap(object, matV = variance(object), B = NULL, ...)

boot(object, matV, B, ...)

## S4 method for signature 'ANY,ANY,integerORnumeric'
boot(object, matV, B, filter = NULL, postProcessing = identity, ...)

## S4 method for signature 'rfh,rfhVariance,'NULL''
boot(object, matV, B, ...)
bootstrap(object, matV = variance(object), B = NULL, ...)

boot(object, matV, B, ...)

## S4 method for signature 'ANY,ANY,integerORnumeric'
boot(object, matV, B, filter = NULL, postProcessing = identity, ...)

## S4 method for signature 'rfh,rfhVariance,'NULL''
boot(object, matV, B, ...)

Arguments

`object`	a fitted object
`matV`	the variance of a fitted object used to draw samples. In most cases this is `object`. Alternatively it may be useful to use a non-robust model.
`B`	the number of repetitions
`...`	arguments passed down to methods
`filter`	a vector indicating which elements in the fittedd object to keep in each repetition.
`postProcessing`	a function to process the results. Is applied before the filter.

Examples

data(milk, package = "sae")
milk$samplingVar <- milk$SD^2
modelFit <- rfh(yi ~ as.factor(MajorArea), milk, "samplingVar")
bootstrapCoefs <- bootstrap(modelFit, B = 2, filter = "coefficients")
do.call(rbind, unlist(bootstrapCoefs, FALSE))
data(milk, package = "sae")
milk$samplingVar <- milk$SD^2
modelFit <- rfh(yi ~ as.factor(MajorArea), milk, "samplingVar")
bootstrapCoefs <- bootstrap(modelFit, B = 2, filter = "coefficients")
do.call(rbind, unlist(bootstrapCoefs, FALSE))

Correlation Structure

Description

Various correlation structures. They can be used inside the rfh function to supply an alterantive variance structure to be fitted. For examples see the documentation of rfh.

Usage

corSAR1(W)

corAR1(nTime)

corSAR1AR1(nTime, W)
corSAR1(W)

corAR1(nTime)

corSAR1AR1(nTime, W)

Arguments

`W`	the row-standardised proximity matrix
`nTime`	(numeric) number of time periods

Details

corSAR1 can be used to model a simultanous autoregressive process of order one: spatial correlation.

corAR1 can be used to model a autoregressive process of order one: temporal correlation.

corSAR1AR1 can be used to model to model spatial and temporal correlation

Slots

W: the row-standardised proximity matrix
nTime: (numeric) number of time periods

Fitting Precedures

Description

Several fitting procedures. The arguments can be passed to these functions using the interface in rfh. The functions here listed are the low level implementations and are not intended for interactive use.

Usage

fitrfh(y, x, samplingVar, ...)

fitrsfh(y, x, samplingVar, W, x0Var = c(0.01, 1), ...)

fitrtfh(y, x, samplingVar, nTime, x0Var = c(0.01, 1, 1), ...)

fitrstfh(y, x, samplingVar, W, nTime, x0Var = c(0.01, 0.01, 1, 1), ...)

fitGenericModel(
  y,
  x,
  matVFun,
  fixedPointParam,
  k = 1.345,
  K = getK(k),
  x0Coef = NULL,
  x0Var = 1,
  x0Re = NULL,
  tol = 1e-06,
  maxIter = 100,
  maxIterParam = 10,
  maxIterRe = 100,
  convCrit = convCritRelative(tol),
  ...
)

## S4 method for signature 'numeric,matrixORMatrix,numeric,'NULL''
rfh(formula, data, samplingVar, correlation = NULL, ...)

## S4 method for signature 'numeric,matrixORMatrix,numeric,corSAR1'
rfh(formula, data, samplingVar, correlation = NULL, ...)

## S4 method for signature 'numeric,matrixORMatrix,numeric,corAR1'
rfh(formula, data, samplingVar, correlation = NULL, ...)

## S4 method for signature 'numeric,matrixORMatrix,numeric,corSAR1AR1'
rfh(formula, data, samplingVar, correlation = NULL, ...)
fitrfh(y, x, samplingVar, ...)

fitrsfh(y, x, samplingVar, W, x0Var = c(0.01, 1), ...)

fitrtfh(y, x, samplingVar, nTime, x0Var = c(0.01, 1, 1), ...)

fitrstfh(y, x, samplingVar, W, nTime, x0Var = c(0.01, 0.01, 1, 1), ...)

fitGenericModel(
  y,
  x,
  matVFun,
  fixedPointParam,
  k = 1.345,
  K = getK(k),
  x0Coef = NULL,
  x0Var = 1,
  x0Re = NULL,
  tol = 1e-06,
  maxIter = 100,
  maxIterParam = 10,
  maxIterRe = 100,
  convCrit = convCritRelative(tol),
  ...
)

## S4 method for signature 'numeric,matrixORMatrix,numeric,'NULL''
rfh(formula, data, samplingVar, correlation = NULL, ...)

## S4 method for signature 'numeric,matrixORMatrix,numeric,corSAR1'
rfh(formula, data, samplingVar, correlation = NULL, ...)

## S4 method for signature 'numeric,matrixORMatrix,numeric,corAR1'
rfh(formula, data, samplingVar, correlation = NULL, ...)

## S4 method for signature 'numeric,matrixORMatrix,numeric,corSAR1AR1'
rfh(formula, data, samplingVar, correlation = NULL, ...)

Arguments

`y`	(numeric) response vector
`x`	([m\|M]atrix) the design matrix
`samplingVar`	(numeric) vector with sampling variances
`...`	arguments passed to `fitGenericModel`
`W`	(matrix) proximity matrix
`x0Var`	(numeric) starting values for variance parameters
`nTime`	(integer) number of time periods
`matVFun`	(function) a function with one argument - the variance parameters - constructing something like variance
`fixedPointParam`	(function) a function with one argument. The vector of model parameters. Returns a list of results of the next iteration in the overall algorithm.
`k`	(numeric) tuning constant
`K`	(numeric) scaling constant
`x0Coef`	(numeric) starting values for regression coefficients
`x0Re`	(numeric) starting values for random effects
`tol`	(numeric) numerical toloerance to be used during optimisation
`maxIter`	(integer) the maximum number of iterations for model parameters.
`maxIterParam`	(integer) the maximum number of iterations for each parameter in each overall iteration
`maxIterRe`	(integer) the maximum number of iterations for fitting the random effects
`convCrit`	(function) a function defining the stopping rule
`formula`	(formula) a formula specifying the fixed effects part of the model.
`data`	(data.frame) a data set.
`correlation`	an optional correlation structure, e.g. corSAR1, for the random effects part of the model. Default is no correlation, i.e. a random intercept.

Details

fitrfh implements the robust Fay-Herriot model; fitrsfh the spatial, fitrtfh the temporal, and fitrstfh the spatio-temporal extension to this model type. See rfh how to fit such models. fitGenericModel is used by all these implementations and can be used for possible extensions of the framework.

Examples

data(milk, package = "sae")
x <- matrix(1, nrow = NROW(milk))
y <- milk$yi
samplingVar <- milk$SD^2
fitrfh(y, x, samplingVar)
data(milk, package = "sae")
x <- matrix(1, nrow = NROW(milk))
y <- milk$yi
samplingVar <- milk$SD^2
fitrfh(y, x, samplingVar)

Fixed Point Algorithm Infrastructure

Description

A fixed-point function supplied by the user is iteratively evaluated. addAverageDamp can be used to add average damping to the function - this may have a positive effect on the speed of convergence.

Usage

fixedPoint(fun, x0, convCrit)

addAverageDamp(fun)

addConstraintMin(fun, value)

addConstraintMax(fun, value)

convCritAbsolute(tolerance = 1e-06)

convCritRelative(tolerance = 1e-06)

addMaxIter(fun, maxIter)

addCounter(fun)

addHistory(fun)

addStorage(fun)

newtonRaphson(funList, ...)

newtonRaphsonFunction(funList)
fixedPoint(fun, x0, convCrit)

addAverageDamp(fun)

addConstraintMin(fun, value)

addConstraintMax(fun, value)

convCritAbsolute(tolerance = 1e-06)

convCritRelative(tolerance = 1e-06)

addMaxIter(fun, maxIter)

addCounter(fun)

addHistory(fun)

addStorage(fun)

newtonRaphson(funList, ...)

newtonRaphsonFunction(funList)

Arguments

`fun`	the function to be evaluated in the algorithm
`x0`	starting value
`convCrit`	a function returning a logical scalar. Is called with two arguments; the first is the value from iteration n; the second is the value from iteration n-1
`value`	(numeric)
`tolerance`	a numeric value > 0
`maxIter`	maximum number of iterations
`funList`	(list) the functions to be evaluated in the algorithm. First element is typically the score function, second is the derivative of the score.
`...`	arguments passed to `fixedPoint`

Details

addAverageDamp adds average damping to an arbitrary fixed point function.

addConstraintMin takes care that values are not below a minimum value.

addConstraintMax takes care that values are not larger than maximum value.

convCritAbsolute absolute difference as convergence criterion.

convCritRelative relative (to previous iteration) absolute difference as convergence criterion. If value is smaller than 1, absolute difference is used.

addMaxIter can be used to modify convergence criterion functions.

addCounter can be used to count the number of calls of a function.

addHistory can be used to save a history of results of a function. The history is stored as a matrix, so this works best if the return value of fun is numeric.

addStorage will add a storage to a function. The storage is a list in which each result is stored. The function will coerce the return value into a numeric.

newtonRaphson finds zeroes of a function. The user can supply the function and its first derivative. Note that the Newton Raphson Algorithm is a special case of a fixed point algorithm thus it is implemented using fixedPoint and is only a convenience.

Examples

## Not run: 
vignette("fixedPoint", "saeRobust")

## End(Not run)
## Not run: 
vignette("fixedPoint", "saeRobust")

## End(Not run)

makeXY

Description

Extract respone vector and design matrix from data with given formula.

Usage

makeXY(.formula, .data)
makeXY(.formula, .data)

Arguments

`.formula`	(formula)
`.data`	(data.frame) data from which design matrix and response to extract from

Examples

set.seed(1)
makeXY(y ~ I(x^2), data.frame(x = rnorm(10), y = rnorm(10)))
set.seed(1)
makeXY(y ~ I(x^2), data.frame(x = rnorm(10), y = rnorm(10)))

Matrix constructor functions

Description

These functions construct different parts of matrix components. They are used internally. If you are interested in the weights of a model fitted using rfh please try to use weights.fitrfh on that object.

Usage

matU(.V)

matTrace(x)

matB(y, X, beta, re, matV, psi)

matBConst(y, X, beta, matV, psi)

matA(y, X, beta, matV, psi)

matAConst(y, X, matV, psi)

matW(y, X, beta, re, matV, psi)

matWbc(y, reblup, W, samplingVar, c = 1)

matTZ(.nDomains, .nTime)

matTZ1(.nDomains = 10, .nTime = 10)
matU(.V)

matTrace(x)

matB(y, X, beta, re, matV, psi)

matBConst(y, X, beta, matV, psi)

matA(y, X, beta, matV, psi)

matAConst(y, X, matV, psi)

matW(y, X, beta, re, matV, psi)

matWbc(y, reblup, W, samplingVar, c = 1)

matTZ(.nDomains, .nTime)

matTZ1(.nDomains = 10, .nTime = 10)

Arguments

`.V`	(Matrix) variance matrix
`x`	([m\|M]atrix) a matrix
`y`	(numeric) response
`X`	(Matrix) design matrix
`beta`	(numeric) vector of regression coefficients
`re`	(numeric) vector of random effects
`matV`	(list of functions) see `matVFH` for an example
`psi`	(function) the influence function
`reblup`	(numeric) vector with robust best linear unbiased predictions
`W`	(Matrix) the weighting matrix
`samplingVar`	(numeric) the vector of sampling variances
`c`	(numeric) scalar
`.nDomains`	(integer) number of domains
`.nTime`	(integer) number of time periods

Details

matU computes U. U is the matrix containing only the diagonal elements of V. This function returns a list of functions which can be called to compute specific transformations of U.

matTrace computes the trace of a matrix.

matB computes the matrix B which is used to compute the weights in the pseudo linearised representation of the REBLUP.

matBConst returns a function with one argument, u, to compute the matrix B. This function is used internally to compute B in the fixed point algorithm.

matA computes the matrix A which is used to compute the weights in the pseudo linearized representation of the REBLUP.

matAConst returns a function with one argument, beta, to compute the matrix A. This function is used internally to compute A in the fixed point algorithm for beta.

matW returns a matrix containing the weights as they are defined for the pseudo linear form, such that matW %*% y is the REBLUP.

matWbc returns a matrix containing the weights as they are defined for the pseudo linear form, such that matWbc %*% y is the bias-corrected REBLUP. c is a multiplyer for the standard deviation.

matTZ constructs the Z matrix in a linear mixed model with autocorrelated random effects.

matTZ1 constructs the Z1 matrix in a linear mixed model with autocorrelated random effects.

References

Warnholz, S. (2016): "Small Area Estimaiton Using Robust Extension to Area Level Models". Not published (yet).

Examples

data("grapes", package = "sae")
data("grapesprox", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

matV <- variance(fitRFH)

# matU:
matU(matV$V())$U()
matU(matV$V())$sqrt()
matU(matV$V())$sqrtInv()

# matB (and matA + matW accordingly):
matB(
  fitRFH$y,
  fitRFH$x,
  fitRFH$coefficients,
  fitRFH$re,
  matV,
  function(x) psiOne(x, k = fitRFH$k)
)

matBConst(
  fitRFH$y,
  fitRFH$x,
  fitRFH$coefficients,
  matV,
  function(x) psiOne(x, k = fitRFH$k)
)(fitRFH$re)

# construcors for 'Z' in linear mixed models
matTZ(2, 3)
matTZ1(2, 3)
data("grapes", package = "sae")
data("grapesprox", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

matV <- variance(fitRFH)

# matU:
matU(matV$V())$U()
matU(matV$V())$sqrt()
matU(matV$V())$sqrtInv()

# matB (and matA + matW accordingly):
matB(
  fitRFH$y,
  fitRFH$x,
  fitRFH$coefficients,
  fitRFH$re,
  matV,
  function(x) psiOne(x, k = fitRFH$k)
)

matBConst(
  fitRFH$y,
  fitRFH$x,
  fitRFH$coefficients,
  matV,
  function(x) psiOne(x, k = fitRFH$k)
)(fitRFH$re)

# construcors for 'Z' in linear mixed models
matTZ(2, 3)
matTZ1(2, 3)

Compute the Mean Squared Error of an Estimator

Description

A generic function to compute the mean squared error of the predicted values under the estimated model. See also rfh for examples.

Usage

mse(object, ...)

## S3 method for class 'fitrfh'
mse(object, type = "pseudo", predType = "reblupbc", B = 100, ...)
mse(object, ...)

## S3 method for class 'fitrfh'
mse(object, type = "pseudo", predType = "reblupbc", B = 100, ...)

Arguments

`object`	(see methods) an object containing the estimation result, e.g. rfh
`...`	arguments passed to methods
`type`	(character) the type of the MSE. Available are 'pseudo' and 'boot'
`predType`	(character) the type of prediction: `c("reblup", "reblupbc")`
`B`	(numeric) number of bootstrap repetitions

Details

Type pseudo is an approximation of the MSE based on a pseudo linearisation approach by Chambers, et. al. (2011). The specifics can be found in Warnholz (2016). Type boot implements a parameteric bootstrap for these methods.

References

Chambers, R., H. Chandra and N. Tzavidis (2011). "On bias-robust mean squared error estimation for pseudo-linear small area estimators". In: Survey Methodology 37 (2), pp. 153–170.

Warnholz, S. (2016): "Small Area Estimaiton Using Robust Extension to Area Level Models". Not published (yet).

Examples

data("grapes", package = "sae")
data("grapesprox", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

mseRFH <- mse(fitRFH)
plot(mseRFH)
data("grapes", package = "sae")
data("grapesprox", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

mseRFH <- mse(fitRFH)
plot(mseRFH)

Plots

Description

Various implementations of diagnostic plots. They are linked together using the plot generic function.

Usage

## S3 method for class 'rfh'
plot(x, y, ...)

## S3 method for class 'prediction.fitrfh'
plot(x, y, alpha = 0.05, ...)

## S3 method for class 'mse.fitrfh'
plot(x, y = "pseudo", xlim = NULL, ylim = NULL, ...)

qqPlot(sample)

blandAltmanPlot(x, y, alpha = 0.05)
## S3 method for class 'rfh'
plot(x, y, ...)

## S3 method for class 'prediction.fitrfh'
plot(x, y, alpha = 0.05, ...)

## S3 method for class 'mse.fitrfh'
plot(x, y = "pseudo", xlim = NULL, ylim = NULL, ...)

qqPlot(sample)

blandAltmanPlot(x, y, alpha = 0.05)

Arguments

`x`	an object
`y`	for mse estimates a filter for the predictors; otherwise ignored
`...`	ignored
`alpha`	(numeric) between 0 and 1 - used in computation of confidence interval
`xlim`, `ylim`	arguments are passed to coord_cartesian and coord_flip.
`sample`	(numeric) a vector

Details

qqPlot a QQ-Plot using ggplot2

blandAltmanPlot a Bland-Altman plot. Solid line is the mean. Dashed lines are the upper and lower bound of the limits-of-aggreements: z-quantile * sd(x - y) – not the standard error. The alpha level can be set using alpha. This plot is otherwise known as Tukey's mean-difference plot.

Examples

qqPlot(rnorm(10))
blandAltmanPlot(rnorm(10), rnorm(10))
qqPlot(rnorm(10))
blandAltmanPlot(rnorm(10), rnorm(10))

psiOne

Description

psiOne is a Huber influence function. getK function to compute capital K – used internally.

Usage

psiOne(u, k = 1.345, deriv = FALSE)

getK(k)
psiOne(u, k = 1.345, deriv = FALSE)

getK(k)

Arguments

`u`	standardized residuals
`k`	tuning constant
`deriv`	if `TRUE` returns the derivative

Examples

set.seed(1)
u <- rnorm(10)
psiOne(u, k = 1.345, deriv = FALSE)
set.seed(1)
u <- rnorm(10)
psiOne(u, k = 1.345, deriv = FALSE)

Robust Fay Herriot Model

Description

User interface to fit robust Fay-Herriot type models. These models are here framed as linear mixed models. The parameter estimation is robust against outliers. Available models are the standard FH model, a spatial extension, a temporal extension and a spatio-temporal extension.

Usage

rfh(formula, data, samplingVar, correlation = NULL, ...)

## S4 method for signature 'formula,data.frame,character,ANY'
rfh(formula, data,
  samplingVar, correlation, ...)

## S3 method for class 'fitrfh'
predict(object, type = "reblup", c = 1, ...)
rfh(formula, data, samplingVar, correlation = NULL, ...)

## S4 method for signature 'formula,data.frame,character,ANY'
rfh(formula, data,
  samplingVar, correlation, ...)

## S3 method for class 'fitrfh'
predict(object, type = "reblup", c = 1, ...)

Arguments

`formula`	(formula) a formula specifying the fixed effects part of the model.
`data`	(data.frame) a data set.
`samplingVar`	(character) the name of the variable in `data` containing the sampling variances.
`correlation`	an optional correlation structure, e.g. corSAR1, for the random effects part of the model. Default is no correlation, i.e. a random intercept.
`...`	arguments passed fitGenericModel
`object`	(rfh) an object of class rfh
`type`	(character) one or more in `c("linear", "reblup", "reblupbc")`
`c`	(numeric) scalar; a multiplyer constant used in the bias correction. Default is to make no correction for realisations of direct estimator within `c = 1` times the standard deviation of direct estimator.

Details

To trigger the spatial and temporal extensions you can supply an argument correlation. When corSAR1 is used the model of Petrucci and Salvati (2006); for corAR1 the model of Rao and Yu (1994) is used; and for corSAR1AR1 the model of Marhuenda et al. (2013).

The methods introducing the robust framework underpinning this implementation can be found in Warnholz (2016). They are based on the results by Sinha and Rao (2009) and Richardson and Welsh (1995).

Value

A list with the following elements:

call (language) the call generating the value
formula (formula) the formula passed as argument
samplingVar (numeric) the vector of sampling variances
coefficients (numeric) the vector of regression coefficients
variance (numeric) the vector of fitted variance parameter(s)
iterations (list) reporting each step in the optimisation
tol (numeric) the tolerance level used
maxIter (numeric) maximum overall allowed iterations
maxIterParam (numeric) maximum allowed iterations for model parameters in each overall iteration
maxIterRe (numeric) maximum allowed iterations for fitting the random effects
k (numeric) tuning constant in influence function
K (numeric) additional tuning constant; often derived from k to scale down the residual variance
y (numeric) the response vector
x (Matrix) the design matrix
re (numeric) the fitted random effects. Can be c(re1, re2)
reblup (numeric) the robust best linear unbiased prediction under the fitted model
residuals (numeric) the realised sampling errors
fitted (numeric) the fitted values using only the fixed effects part

References

Marhuenda, Y., I. Molina and D. Morales (2013). "Small area estimation with spatio-temporal Fay-Herriot models". In: Computational Statistics and Data Analysis 58, pp. 308–325.

Pratesi, M. and N. Salvati (2008). "Small area estimation: the EBLUP estimator based on spatially correlated random area effects". In: Statistical Methods & Applications 17, pp. 113–141.

Rao, J. N. K. and M. Yu (1994). "Small-Area Estimation by Combining Time-Series and Cross-Sectional Data". In: Canadian Journal of Statistics 22.4, pp. 511–528.

Richardson, A. M. and A. H. Welsh (1995). "Robust Restricted Maximum Likelihood in Mixed Linear Models". In: Biometrics 51 (4), pp. 1429–1439.

Sinha, S. K. and J. N. K. Rao (2009). "Robust Small Area Estimation". In: The Canadian Journal of Statistics 37 (3), pp. 381–399.

Warnholz, S. (2016): "Small Area Estimaiton Using Robust Extension to Area Level Models". Dissertation. https://refubium.fu-berlin.de/handle/fub188/9706.

Examples


# Non-temporal models:
data("grapes", package = "sae")
data("grapesprox", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

fitRFH
summary(fitRFH)

plot(fitRFH)
plot(predict(fitRFH))
plot(mse(fitRFH))

## Not run: 
# And the same including a spatial structure:
fitRSFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var",
  corSAR1(as.matrix(grapesprox))
)

# Use the same methods, e.g. plot, for all these implementations:
data("spacetime", package = "sae")
data("spacetimeprox", package = "sae")
nTime <- length(unique(spacetime$Time))

fitRTFH <- rfh(
  Y ~ X1 + X2,
  spacetime,
  "Var",
  corAR1(nTime = nTime)
)

fitRSTFH <- rfh(
  Y ~ X1 + X2,
  spacetime,
  "Var",
  corSAR1AR1(W = as.matrix(spacetimeprox), nTime = nTime)
)

## End(Not run)
# Non-temporal models:
data("grapes", package = "sae")
data("grapesprox", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

fitRFH
summary(fitRFH)

plot(fitRFH)
plot(predict(fitRFH))
plot(mse(fitRFH))

## Not run: 
# And the same including a spatial structure:
fitRSFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var",
  corSAR1(as.matrix(grapesprox))
)

# Use the same methods, e.g. plot, for all these implementations:
data("spacetime", package = "sae")
data("spacetimeprox", package = "sae")
nTime <- length(unique(spacetime$Time))

fitRTFH <- rfh(
  Y ~ X1 + X2,
  spacetime,
  "Var",
  corAR1(nTime = nTime)
)

fitRSTFH <- rfh(
  Y ~ X1 + X2,
  spacetime,
  "Var",
  corSAR1AR1(W = as.matrix(spacetimeprox), nTime = nTime)
)

## End(Not run)

Compute values of robust score functions

Description

Can be used to compute the values of the robust estimation equations at their 'solution'.

Usage

score(object, filter, ...)
score(object, filter, ...)

Arguments

`object`	a fitted object
`filter`	(character) a selection of values to be computed
`...`	arguments passed to methods

Examples

data("grapes", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

score(fitRFH)
data("grapes", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

score(fitRFH)

Construction of test data

Description

Construction of test data

Usage

testMatX(...)

testResponse0(x, beta = rep(1, ncol(x)))

testResponse(y0, k = 1:4, .sd = sd(y0))

testRook(n)
testMatX(...)

testResponse0(x, beta = rep(1, ncol(x)))

testResponse(y0, k = 1:4, .sd = sd(y0))

testRook(n)

Arguments

`...`	matrices
`x`	a matrix
`beta`	a vector with parameters
`y0`	a response vector (numeric)
`k`	values in 1 to 4 (integer)
`.sd`	the standard deviation used for random numbers
`n`	dimension

References

Weihs / Mersmann / Ligges (2014): Foundations of Statistical Algorithms: With References to R Packages

Examples

## Examples from Weihs et. al. (2014) p. 108
library("Matrix")
testMatX(Matrix(998), Matrix(998))
Z <- Matrix(c(998, 0, 0, 0), 2, 2)
testMatX(Z, Z)
testResponse0(testMatX(Matrix(1)))
library("magrittr")
Matrix(1) %>% testMatX %>% testResponse0 %>% testResponse
## Examples from Weihs et. al. (2014) p. 108
library("Matrix")
testMatX(Matrix(998), Matrix(998))
Z <- Matrix(c(998, 0, 0, 0), 2, 2)
testMatX(Z, Z)
testResponse0(testMatX(Matrix(1)))
library("magrittr")
Matrix(1) %>% testMatX %>% testResponse0 %>% testResponse

Update a fitted object

Description

This is a method which can be used to update a rfh result object and refit it. The fitted parameter values from the current object are used as starting values, then update.default is called.

Usage

## S4 method for signature 'rfh'
update(object, formula, ..., where = parent.frame(2))

## S4 method for signature 'fitrfh'
update(object, ...)
## S4 method for signature 'rfh'
update(object, formula, ..., where = parent.frame(2))

## S4 method for signature 'fitrfh'
update(object, ...)

Arguments

`object`	(rfh) an object fitted by rfh
`formula`	see update.formula
`...`	arguments passed to update.default
`where`	(environment) should not be specified by the user

Construct variance

Description

A generic function to construct the different variance components of an object. You may want to use this in conjunction with bootstrap.

Usage

variance(.object, ...)

## S3 method for class 'fitrfh'
variance(.object, ...)

## S3 method for class 'fitrsfh'
variance(.object, ...)

## S3 method for class 'fitrtfh'
variance(.object, ...)

## S3 method for class 'fitrstfh'
variance(.object, ...)

## S3 method for class 'fitrfh'
weights(object, c = 1, ...)
variance(.object, ...)

## S3 method for class 'fitrfh'
variance(.object, ...)

## S3 method for class 'fitrsfh'
variance(.object, ...)

## S3 method for class 'fitrtfh'
variance(.object, ...)

## S3 method for class 'fitrstfh'
variance(.object, ...)

## S3 method for class 'fitrfh'
weights(object, c = 1, ...)

Arguments

`.object`, `object`	an object
`...`	arguments passed to method
`c`	(numeric) scalar

Examples

data("grapes", package = "sae")
data("grapesprox", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

# The variance component of a mixed linear model:
matV <- variance(fitRFH)
# The full variance matrix:
matV$V()

# The sampling error component
matV$Ve()

# the random effects component
matV$Vu()
data("grapes", package = "sae")
data("grapesprox", package = "sae")

fitRFH <- rfh(
  grapehect ~ area + workdays - 1,
  data = grapes,
  samplingVar = "var"
)

# The variance component of a mixed linear model:
matV <- variance(fitRFH)
# The full variance matrix:
matV$V()

# The sampling error component
matV$Ve()

# the random effects component
matV$Vu()

Package 'saeRobust'

Help Index

Fit model on Bootstrap sample

Description

Usage

Arguments

Examples

Correlation Structure

Description

Usage

Arguments

Details

Slots

Fitting Precedures

Description

Usage

Arguments

Details

Examples

Fixed Point Algorithm Infrastructure

Description

Usage

Arguments

Details

Examples

makeXY

Description

Usage

Arguments

Examples

Matrix constructor functions

Description

Usage

Arguments

Details

References

Examples

Compute the Mean Squared Error of an Estimator

Description

Usage

Arguments

Details

References

Examples

Plots

Description

Usage

Arguments

Details

Examples

psiOne

Description

Usage

Arguments

Examples

Robust Fay Herriot Model

Description

Usage

Arguments

Details

Value

References

Examples

Compute values of robust score functions

Description

Usage

Arguments

Examples

Construction of test data

Description

Usage

Arguments

References

Examples

Update a fitted object

Description

Usage

Arguments

Construct variance

Description