R: Class 'RFfit'

RFfit-class {RandomFields} | R Documentation

## Class `RFfit`

### Description

Class for RandomField's representation of model estimation results

### Usage

    ## S4 method for signature 'RFfit'
    residuals(object, ..., method="ml", full=FALSE)
    ## S4 method for signature 'RFfit'
    summary(object, ..., method="ml")
    ## S4 method for signature 'RFfit,missing'
    plot(x, y, ...)

    ## S3 method for class 'RFfit'
    contour(x, ...)
    ## S3 method for class 'RFempVariog'
    contour(x, ...)

    RFhessian(model)

### Arguments

`object` | see the generic function;
`...` | `plot`: arguments to be passed to methods; mainly graphical arguments, or further models in case of class `'RMmodel'`, see Details. `summary`: see the generic function `contour` : see `RFplotEmpVariogram`
`method` | character; only for `class(x)=="RFfit"`; a vector of slot names for which the fitted covariance or variogram model is to be plotted; should be a subset of `slotNames(x)` for which the corresponding slots are of class `"RMmodelFit"`; by default, the maximum likelihood fit (`"ml"`) will be plotted
`full` | logical. if `TRUE` submodels are reported as well (if available).
`x` | object of class `RFsp` or `RFempVario` or `RFfit` or `RMmodel`; in the latter case, `x` can be any sophisticated model but it must be either stationary or a variogram model
`y` | unused
`model` | `class(x)=="RF_fit"` or `class(x)=="RFfit"`, obtained from `RFfit`

### Details

for the definition of `plot` see `RFplotEmpVariogram`.

### Creating Objects

Objects are created by the function `RFfit`

### Slots

`autostart`:

  RMmodelFit; contains the estimation results for the method 'autostart' including a likelihood value, a constant trend and the residuals

`boxcox`:

  logical; whether the parameter of a Box Cox tranformation has been estimated

`coordunits`:

  string giving the units of the coordinates, see also option `coordunits` of `RFoptions`.

`deleted`:

  integer vector. positions of the parameters that has been deleted to get the set of variables, used in the optimazation

`ev`:

  list; list of objects of class `RFempVariog`, contains the empirical variogram estimates of the data

`fixed`:

  list of two vectors. The fist gives the position where the parameters are set to zero. The second gives the position where the parameters are set to one.

`internal1`:

  RMmodelFit; analog to slot 'autostart'

`internal2`:

  RMmodelFit; analog to slot 'autostart'

`internal3`:

  RMmodelFit; analog to slot 'autostart'

`lowerbounds`:

  RMmodel; covariance model in which each parameter value gives the lower bound for the respective parameter

`ml`:

  RMmodelFit; analog to slot 'autostart'

`modelinfo`:

  Table with information on the parameters: name, boundaries, type of parameter

`n.covariates`:

  number of covariates

`n.param`:

  number of parameters (given by the user)

`n.variab`:

  number of variables (used internally); `n.variab` is always less than or equal to `n.param`

`number.of.data`:

  the number of data values passed to `RFfit` that are not `NA` or `NaN`

`number.of.parameters`:

  total number of parameters of the model that had to be estimated including variances, scales, co-variables, etc.

`p.proj`:

  vector of integers. The original position of those parameters that are used in the submodel

`plain`:

  RMmodelFit; analog to slot 'autostart'

`report`:

  if not empty, it indicates that this model should be reported and gives a standard name of the model.

  Various function, e.g. `print.RMmodelFit` uses this information if their argument `full` equals `TRUE`.

`self`:

  RMmodelFit; analog to slot 'autostart'

`sd.inv`:

  RMmodelFit; analog to slot 'autostart'

`sqrt.nr`:

  RMmodelFit; analog to slot 'autostart'

`submodels`:

  list. Sequence (in some cases even nested sequence) of models that is used to determine an initial value in

`table`:

  matrix; summary of estimation results of different methods

`transform`:

  function;

`true.tsdim`:

  time space dimension of the (original!) data, even for submodels that consider parts of separable models.

`true.vdim`:

  multivariability of the (original!) data, even for submodels that consider independent models for the multivariate components.

`upperbounds`:

  RMmodel; see slot 'lowerbounds'

`users.guess`:

  RMmodelFit; analog to slot 'autostart'

`ml`:

  RMmodelFit; analog to slot 'autostart'; with maximum likelihood method

`v.proj`:

  vector of integers. The components selected in one of the submodels

`varunits`:

  string giving the units of the variables, see also option `varunits` of `RFoptions`.

`x.proj`:

  logical or integer. If logial, it means that no separable model is considered there. If integer, then it gives the considered directions of a separable model

`Z`:

  standardized list of information on the data

### Methods

plot

  `signature(x = "RFfit")`: gives a plot of the empirical variogram together with the fitted model, for more details see `plot-method`.

show

  `signature(x = "RFfit")`: returns the structure of `x`

persp

  codesignature(obj = "RFfit"): generates `persp` plots

print

  `signature(x = "RFfit")`: identical with `show`-method, additional argument is `max.level`

[

  `signature(x = "RFfit")`: enables accessing the slots via the `"["`-operator, e.g. `x["ml"]`

as

  `signature(x = "RFfit")`: converts into other formats, only implemented for target class `RFempVariog`

anova

  performs a likelihood ratio test base on a chisq approximation

summary

  provides a summary

logLik

  provides an object of class `"logLik"`

AIC,BIC

  provides the AIC and BIC information, respectively

`signature(x = "RFfit", y = "missing")`

  Combines the plot of the empirical variogram with the estimated covariance or variogram model (theoretical) curves; further models can be added via the argument `model`.

### Further 'methods'

`AICc.RFfit(object, ..., method="ml", full=FALSE)`]

`AICc.RF_fit(object, ..., method="ml", full=TRUE)`

### Author(s)

Alexander Malinowski Alexander.Malinowski@web.de, Martin Schlather, schlather@math.uni-mannheim.de http://ms.math.uni-mannheim.de/de/publications/software

### References

AICc:

- Hurvich, C.M. and Tsai, C.-L. (1989) Regression and Time Series Model Selection in Small Samples Biometrika, 76, 297-307.

### See Also

`RFfit`, `RFempiricalvariogram`, `RMmodel-class`, `RMmodelFit-class` `plot-method`

### Examples

    # see RFfit

[Package RandomFields version 3.1.50 Index]