predict.manylm: Model Predictions for Multivariate Linear Models
In mvabund: Statistical Methods for Analysing Multivariate Abundance Data

Description Usage Arguments Details Value Note Author(s) See Also Examples

predict.manylm is a function for predictions from the result of the model fitting function manylm.

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## S3 method for class 'manylm'
predict(object, newdata=NULL, se.fit = FALSE, 
    type = c("response", "terms"), terms = NULL, na.action = na.pass, ...)

`object`	object of class inheriting from `manylm`.
`newdata`	an optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
`se.fit`	a switch indicating if standard errors are required.
`type`	type of prediction (response or model term), Possible values: "response", "terms".
`terms`	if type="terms", which terms (default is all terms).
`na.action`	function determining what should be done with missing values in newdata. The default is to predict `NA`.
`...`	further arguments passed to or from other methods.

predict.manylm produces predicted values, obtained by evaluating the regression function in the frame newdata (which defaults to model.frame(object). If the logical se.fit is TRUE, standard errors of the predictions are calculated. If the numeric argument scale is set (with optional df), it is used as the residual standard deviation in the computation of the standard errors, otherwise this is extracted from the model fit. Setting intervals specifies computation of confidence or prediction (tolerance) intervals at the specified level, sometimes referred to as narrow vs. wide intervals.

If the fit is rank-deficient, some of the columns of the design matrix will have been dropped. Prediction from such a fit only makes sense if newdata is contained in the same subspace as the original data. That cannot be checked accurately, so a warning is issued.

If newdata is omitted the predictions are based on the data used for the fit. In that case how cases with missing values in the original fit is determined by the na.action argument of that fit. If na.action = na.omit omitted cases will not appear in the residuals, whereas if na.action = na.exclude they will appear (in predictions, standard errors or interval limits), with residual value NA. See also napredict.

The prediction intervals are for a single observation at each case in newdata (or by default, the data used for the fit) with error variance(s) pred.var. This can be a multiple of res.var, the estimated value of sigma^2: the default is to assume that future observations have the same error variance as those used for fitting. If weights is supplied, the inverse of this is used as a scale factor. For a weighted fit, if the prediction is for the original data frame, weights defaults to the weights used for the model fit, with a warning since it might not be the intended result. If the fit was weighted and newdata is given, the default is to assume constant prediction variance, with a warning.

predict.manylm produces a matrix of predictions or if interval is set an array of predictions and bounds, where the first dimension has the names: fit, lwr, and upr. If se.fit is TRUE, a list with the following components is returned:

`fit`	vector or matrix as above
`se.fit`	a matrix with the standard errors of the predicted means
`residual.scale`	vector or matrix as a vector of residual standard deviations
`df`	numeric, the degrees of freedom for residual

Variables are first looked for in newdata and then searched for in the usual way (which will include the environment of the formula used in the fit). A warning will be given if the variables found are not of the same length as those in newdata if it was supplied.

Offsets specified by offset in the fit by lm will not be included in predictions, whereas those specified by an offset term in the formula will be.

Notice that prediction variances and prediction intervals always refer to future observations, possibly corresponding to the same predictors as used for the fit. The variance of the residuals will be smaller.

Strictly speaking, the formula used for prediction limits assumes that the degrees of freedom for the fit are the same as those for the residual variance. This may not be the case if res.var is not obtained from the fit.

Ulrike Naumann, Yi Wang and David Warton <David.Warton@unsw.edu.au>.

manylm.

data(spider)
spiddat <- mvabund(spider$abund[1:20, ])
dat = data.frame(spider$x[1:20, ])
manylm.fit <- manylm(spiddat~soil.dry+bare.sand, data=dat)
predict(manylm.fit)
predict(manylm.fit, se.fit = TRUE)

new <- data.frame(spider$x[21:28, ])
predict(manylm.fit, new, se.fit = TRUE)