FP | R Documentation |

Fractional polynomials transformation for continuous covariates.

FP(x, p = c(-2, -1, -0.5, 0.5, 1, 2, 3), scaling = TRUE)

`x` |
a numeric vector. |

`p` |
all powers of |

`scaling` |
a logical indicating if the measurements are scaled prior to model fitting. |

A fractional polynomial refers to a model
*∑_{j = 1}^k (β_j x^{p_j} + γ_j x^{p_j} \log(x)) +
β_{k + 1} \log(x) + γ_{k + 1} \log(x)^2*,
where the degree of the fractional polynomial is the number of non-zero regression coefficients
*β* and *γ*.

A matrix including all powers `p`

of `x`

,
all powers `p`

of `log(x)`

, and `log(x)`

.

Willi Sauerbrei and Patrick Royston (1999), Building multivariable prognostic and
diagnostic models: transformation of the predictors by using fractional polynomials.
*Journal of the Royal Statistical Society A*, **162**, 71–94.

`gamboost`

to fit smooth models, `bbs`

for P-spline base-learners

data("bodyfat", package = "TH.data") tbodyfat <- bodyfat ### map covariates into [1, 2] indep <- names(tbodyfat)[-2] tbodyfat[indep] <- lapply(bodyfat[indep], function(x) { x <- x - min(x) x / max(x) + 1 }) ### generate formula fpfm <- as.formula(paste("DEXfat ~ ", paste("FP(", indep, ", scaling = FALSE)", collapse = "+"))) fpfm ### fit linear model bf_fp <- glmboost(fpfm, data = tbodyfat, control = boost_control(mstop = 3000)) ### when to stop mstop(aic <- AIC(bf_fp)) plot(aic) ### coefficients cf <- coef(bf_fp[mstop(aic)]) length(cf) cf[abs(cf) > 0]

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