R: BAMLSS Fitted Values

fitted.bamlss {bamlss} | R Documentation

## BAMLSS Fitted Values

### Description

Function to compute fitted values for `bamlss` models. The function calls `predict.bamlss` to compute fitted values from samples.

### Usage

    ## S3 method for class 'bamlss'
    fitted(object, model = NULL, term = NULL,
      type = c("link", "parameter"), samples = TRUE,
      FUN = c95, nsamps = NULL, ...)

### Arguments

`object` | An object of class `"bamlss"`
`model` | Character or integer, specifies the model for which fitted values should be computed.
`term` | Character or integer, specifies the model terms for which fitted values are required. Note that if `samples = TRUE`, e.g., `term = c("s(x1)", "x2")` will compute the combined fitted values `s(x1) + x2`.
`type` | If `type = "link"` the predictor of the corresponding `model` is returned. If `type = "parameter"` fitted values on the distributional parameter scale are returned.
`samples` | Should fitted values be computed using samples of parameters or estimated parameters as returned from optimizer functions (e.g., function `bfit` returns `"fitted.values"`). The former results in a call to `predict.bamlss`, the latter simply extracts the `"fitted.values"` of the `bamlss` object and is not model term specific.
`FUN` | A function that should be applied on the samples of predictors or parameters, depending on argument `type`.
`nsamps` | If the fitted `bamlss` object contains samples of parameters, computing fitted values may take quite some time. Therefore, to get a first feeling it can be useful to compute fitted values only based on `nsamps` samples, i.e., `nsamps` specifies the number of samples which are extracted on equidistant intervals.
`...` | Arguments passed to function `predict.bamlss`.

### Value

Depending on arguments `model`, `FUN` and the structure of the `bamlss` model, a list of fitted values or simple vectors or matrices of fitted values.

### See Also

`bamlss`, `predict.bamlss`.

### Examples

    ## Not run: ## Generate some data.
    d <- GAMart()

    ## Model formula.
    f <- list(
      num ~ s(x1) + s(x2) + s(x3) + te(lon,lat),
      sigma ~ s(x1) + s(x2) + s(x3) + te(lon,lat)
    )

    ## Estimate model.
    b <- bamlss(f, data = d)

    ## Fitted values returned from optimizer.
    f1 <- fitted(b, model = "mu", samples = FALSE)

    ## Fitted values returned from sampler.
    f2 <- fitted(b, model = "mu", samples = TRUE, FUN = mean)

    plot(f1, f2)

    ## End(Not run)

[Package bamlss version 1.1-6 Index]