# mfp.object: Multiple Fractional Polynomial Model Object In mfp: Multivariable Fractional Polynomials

## Description

Objects returned by fitting fractional polynomial model objects.

These are objects representing fitted `mfp` models. Class `mfp` inherits from either glm or coxph depending on the type of model fitted.

## Value

In addition to the standard glm/coxph components the following components are included in a `mfp` object.

 `x` the final FP transformations that are contained in the design matrix x. The predictor "z" with 4 df would have corresponding columns "z.1" and "z.2" in x. `powers` a matrix containing the best FP powers for each predictor. If a predictor has less than two powers a NA will fill the appropriate cell of the matrix. `pvalues` a matrix containing the P-values from the closed tests. Briefly p.null is the P-value for the test of inclusion (see mfp), p.lin corresponds to the test of nonlinearity and p.FP the test of simplification. The best m=1 power (power2) and best m=2 powers (power4.1 and power4.2) are also given. `scale` all predictors are shifted and rescaled before being power transformed if nonpositive values are encountered or the range of the predictor is reasonably large. If x' would be used instead of x where x' = (x+a)/b the parameters a (shift) and b (scale) are contained in the matrix scale. `df.initial` a vector containing the degrees of freedom allocated to each predictor. `df.final` a vector containing the degrees of freedom of each predictor at convergence of the backfitting algorithm. `dev` the deviance of the final model. `dev.lin` the deviance of the model that has every predictor included with 1 df (i.e. linear). `dev.null` the deviance of the null model. `fptable` the table of the final fp transformations. `formula` the proposed formula for a call of glm/coxph. `fit` the fitted glm/coxph model using the proposed formula. This component can be used for prediction, etc.