predict.feis | R Documentation |
Predicted values based on linear model object.
## S3 method for class 'feis' predict( object, newdata = NULL, se.fit = FALSE, vcov = NULL, interval = c("none", "confidence", "prediction"), level = 0.95, pred.var = sigma_sq, ... )
object |
an object of class " |
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. |
vcov |
optional variance-covariance matrix for std.err. calculation. |
interval |
type of interval calculation. |
level |
tolerance/confidence level. |
pred.var |
the variance for future observations to be assumed for prediction intervals. By default, equals the residual variance |
... |
further arguments. |
predict.lm
produces predicted values, obtained by evaluating the regression function
in the frame newdata (which defaults to model.matrix(object)
). If the logical se.fit
is
TRUE
, standard errors of the predictions are calculated. If the vcov
is not provided,
the object$vcov
is used, thus allowing for robust variance-covariance matrices.
Setting intervals specifies computation of confidence or prediction (tolerance) intervals
at the specified level
.
Note: Currently, predictions are based on the transformed (de-trended) data.
A vector of predictions or a matrix of predictions and bounds with column names
fit
, lwr
, and upr
if interval
is set.
predict.lm, predict
feis.mod <- feis(lnw ~ age | exp, data = mwp, id = "id", robust = TRUE) new <- data.frame(age = seq(-10, 10, 1)) feis.pred <- predict(feis.mod, newdata = new, se.fit = TRUE, interval = "confidence") ## Not run: matplot(new$age, feis.pred$fit, lty = c(1,2,2), type = "l", ylab = "predicted y") ## End(Not run)
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