calibrate.rrvglm {VGAM} | R Documentation

## Calibration for CLO models (RR-VGLMs)

### Description

Performs maximum likelihood calibration for constrained and unconstrained quadratic and additive ordination models (CQO and CAO models are better known as QRR-VGLMs and RR-VGAMs respectively).

### Usage

    calibrate.rrvglm(object, newdata = NULL,
            type = c("latvar", "predictors", "response", "vcov", "all3or4"),
            initial.vals = NULL, ...)

### Arguments

`object` | The fitted `rrvglm` model.
`newdata` | A data frame with new response data (e.g., new species data). The default is to use the original data used to fit the model; however, the calibration may take a long time to compute because the computations are expensive.
`type` | Same as `calibrate.qrrvglm`. The `"all3or4"` is for all of them, i.e., all `type`s. For CLO models, `"vcov"` is unavailable, so all 3 are returned.
`initial.vals` | Same as `calibrate.qrrvglm`. The default is a grid defined by arguments in `calibrate.rrvglm.control`.
`...` | Arguments that are fed into `calibrate.rrvglm.control`.

### Details

Given a fitted regression CLO model, maximum likelihood calibration is theoretically easy and elegant. However, the method assumes that all responses are independent. More details and references are given in Yee (2012).

The function `optim` is used to search for the maximum likelihood solution. Good initial values are needed, and `calibrate.rrvglm.control` allows the user some control over the choice of these.

It is found empirically that the stereotype model (also known as a reduced-rank `multinomial` logit model) calibrates well only with grouped data. When the response vector is all 0s except for one 1, then the MLE will probably be at `-Inf` or `+Inf`.

### Value

See `calibrate.qrrvglm`.

### Warning

This function assumes that the prior weights are all unity; see `weightsvglm`.

This function is computationally expensive for `Rank >= 1`, and since it uses a `for()` loop several times it can be slow. Setting `trace = TRUE` to get a running log is a good idea.

### Author(s)

T. W. Yee

### See Also

`calibrate.qrrvglm`, `calibrate`, `rrvglm`, `weightsvglm`.

### Examples

    ## Not run:
    nona.xs.nz <- na.omit(xs.nz)  # Overkill!!
    nona.xs.nz$dmd       <- with(nona.xs.nz, round(drinkmaxday))
    nona.xs.nz$feethr    <- with(nona.xs.nz, round(feethour))
    nona.xs.nz$sleephr   <- with(nona.xs.nz, round(sleep))
    nona.xs.nz$beats     <- with(nona.xs.nz, round(pulse))

    p2 <- rrvglm(cbind(dmd, feethr, sleephr, beats) ~ age + smokenow +
      depressed + embarrassed + fedup + hurt + miserable +  # 11 psychological
      nofriend + moody + nervous + tense + worry + worrier, # variables
      noRRR = ~ age + smokenow, trace = FALSE, poissonff, data = nona.xs.nz,
             Rank = 2)
    cp2 <- calibrate(p2, new = head(nona.xs.nz, 9), type = "all", trace = TRUE)
    cp2

    two.cases <- nona.xs.nz[1:2, ]  # Another example
    two.cases$dmd       <- c(4, 10)
    two.cases$feethr    <- c(4, 7)
    two.cases$sleephr   <- c(7, 8)
    two.cases$beats     <- c(62, 71)
    cp2b <- calibrate(p2, new = two.cases, type = "all")
    cp2b

    ## End(Not run)

[Package VGAM version 1.0-4 Index]