Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/predict.manyglm.R
Obtains predictions and optionally estimates standard errors of those predictions from a fitted manyglm object.
1 2 3 4 
object 
a fitted object of class inheriting from 
newdata 
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used. 
type 
the type of prediction required. The default is on the
scale of the linear predictors; the alternative The value of this argument can be abbreviated. 
se.fit 
logical switch indicating if standard errors are required. 
dispersion 
the dispersion of the MANYGLM fit to be assumed in
computing the standard errors. If omitted, that returned by

terms 
with 
na.action 
function determining what should be done with missing
values in 
... 
further arguments passed to or from other methods. 
predict.manyglm refits the model using glm before making predictions. In rare (usually pathological) cases this may lead to differences in predictions as compared to what would be expected if using the manyglm coefficients directly.
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 and standard errors), with residual value
NA
. See also napredict
.
If se = FALSE
, a matrix of predictions or an array of
predictions and bounds.
If se = TRUE
, a list with components
fit 
the predictions 
se.fit 
estimated standard errors 
residual.scale 
a scalar giving the square root of the dispersion used in computing the standard errors. 
Ulrike Naumann, Yi Wang and David Warton <[email protected]>.
1 2 3 4 5 6 7 8 9 10  data(spider)
spiddat < mvabund(spider$abund)
Y < spiddat[1:20, ]
X < data.frame(spider$x[1:20, ])
glm.spid.poiss < manyglm(Y~soil.dry+bare.sand, family="poisson", data=X)
glm.spid.poiss$data = X
newdata < data.frame(spider$x[21:28,])
predict(glm.spid.poiss, newdata)
pred.w.plim < predict(glm.spid.poiss, newdata, interval="prediction")
pred.w.clim < predict(glm.spid.poiss, newdata, interval="confidence")

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