Description Usage Arguments Details References Examples

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 |

`.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 |

`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 |

`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.

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

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 | ```
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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.