# Predict method for Cusp Model Fits

### Description

Predicted values based on a cusp model object.

### Usage

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, ...)
``` |

### Arguments

`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. |

### Details

`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*.

### Value

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`

.)

### Note

Currently `method = "expected"`

should not be trusted.

### Author(s)

Raoul Grasman

### References

See `cusp-package`

.

### See Also

`cusp-package`

, `predict.lm`

.

### Examples

1 2 3 4 5 6 7 8 9 10 |