Predicted values based on a cusp model object.
1 2 3 4 5  ## S3 method for class 'cusp'
predict(object, newdata, se.fit = FALSE, interval =
c("none", "confidence", "prediction"), level = 0.95, type = c("response", "terms"),
terms = NULL, na.action = na.pass, pred.var = res.var/weights, weights = 1,
method = c("delay", "maxwell", "expected"), keep.linear.predictors = FALSE, ...)

object 
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 
See 
interval 
See 
level 
See 
type 
See 
terms 
See 
na.action 
See 
pred.var 
See 
weights 
See 
method 
Type of prediction convention to use. Can be abbreviated. ( 
keep.linear.predictors 
Logical. Should the linear predictors (alpha, beta, and y) be returned? 
... 
further arguments passed to or from other methods. 
predict.cusp
produces predicted values, obtained by evaluating the regression functions from the
cusp object
in the frame newdata
using predict.lm
. This results in linear
predictors for the cusp control variables alpha
, and beta
, and, if method = "delay"
,
for the behavioral cusp variable y
. These are then used to compute predicted values: If
method = "delay"
these are the points y* on the cusp surface defined by
V'(y*) = α + β y*  y*^3 = 0
that are closest to y
. If method = "maxwell"
they are
the points on the cusp surface corresponding to the minimum of the associated potential function
V(y*) = α y* + 0.5 y*^2  0.25 y*^4.
A vector of predictions. If keep.linear.predictors
the return value has a "data"
attribute
which links to newdata
augmented with the linear predictors alpha
, beta
, and, if
method = "delay"
, y
. If method = "expected"
, the expected value from the equilibrium
distribution of the stochastic process
dY_t = V'(Y_t;α, β)dt + dW_t,
where W_t is
a Wiener proces (aka Brownian motion) is returned. (This distribution is implemented in
dcusp
.)
Currently method = "expected"
should not be trusted.
Raoul Grasman
See cusppackage
.
1 2 3 4 5 6 7 8 9 10 
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