fitSpline | R Documentation |

`response`

in a `data.frame`

, and growth rates can be
computed using derivativesUses `smooth.spline`

to fit a natural cubic smoothing spline or `JOPS`

to fit a
P-spline to all the values of `response`

stored in `data`

.

The amount of smoothing can be controlled by tuning parameters, these being
related to the penalty. For a natural cubic smoothing spline, these are
`df`

or `lambda`

and, for a P-spline, it is `lambda`

.
For a P-spline, `npspline.segments`

also influences the smoothness of the fit.
The `smoothing.method`

provides for `direct`

and `logarithmic`

smoothing. The method of Huang (2001) for correcting the fitted spline for
estimation bias at the end-points will be applied when fitting using a
natural cubic smoothing spline if `correctBoundaries`

is `TRUE`

.

The derivatives of the fitted spline can also be obtained, and the
Absolute and Relative Growth Rates ( AGR and RGR) computed using them, provided
`correctBoundaries`

is `FALSE`

. Otherwise, growth rates can be
obtained by difference using `byIndv4Times_GRsDiff`

.

The handling of missing values in the observations is controlled via
`na.x.action`

and `na.y.action`

. If there are not
at least four distinct, nonmissing x-values, a warning is issued and
all smoothed values and derivatives are set to `NA`

.

The function `probeSmoothing`

can be used to investgate the effect
the smoothing parameters (`smoothing.method`

and `df`

or
`lambda`

) on the smooth that results.

```
fitSpline(data, response, response.smoothed, x,
smoothing.method = "direct",
spline.type = "NCSS", df = NULL, lambda = NULL,
npspline.segments = NULL, correctBoundaries = FALSE,
deriv = NULL, suffices.deriv = NULL, extra.rate = NULL,
na.x.action = "exclude", na.y.action = "trimx", ...)
```

`data` |
A |

`response` |
A |

`response.smoothed` |
A |

`x` |
A |

`smoothing.method` |
A |

`spline.type` |
A |

`df` |
A |

`lambda` |
A |

`npspline.segments` |
A |

`correctBoundaries` |
A |

`deriv` |
A |

`suffices.deriv` |
A |

.

`extra.rate` |
A named |

`na.x.action` |
A |

`na.y.action` |
A |

`...` |
allows for arguments to be passed to |

A `list`

with two components named `predictions`

and
`fit.spline`

.

The `predictions`

component is a `data.frame`

containing `x`

and the fitted smooth. The names of the columns will be the value of
`x`

and the value of `response.smoothed`

. The number of rows in
the `data.frame`

will be equal to the number of pairs that have neither
a missing `x`

or `response`

and the order of `x`

will be the
same as the order in `data`

. If `deriv`

is not `NULL`

,
columns containing the values of the derivative(s) will be added to the
`data.frame`

; the name each of these columns will be the value of
`response.smoothed`

with `.dvf`

appended, where `f`

is the
order of the derivative, or the value of `response.smoothed`

and the
corresponding element of `suffices.deriv`

appended. If `RGR`

is
not `NULL`

, the RGR is calculated as the ratio of value of the first
derivative of the fitted spline and the fitted value for the spline.

The `fit.spline`

component is a `list`

with components

`x`

:the distinct x values in increasing order;

`y`

:the fitted values, with boundary values possibly corrected, and corresponding to

`x`

;`lev`

:leverages, the diagonal values of the smoother matrix (NCSS only);

`lambda`

:the value of lambda (corresponding to

`spar`

for NCSS - see`smooth.spline`

);`df`

:the efective degrees of freedom;

`npspline.segments`

:the number of equally spaced segments used for smoothing method set to

`PS`

;`uncorrected.fit`

:the object returned by

`smooth.spline`

for`smoothing method`

set to`NCSS`

or by`JOPS::psNormal`

for`PS`

.

Chris Brien

Eilers, P.H.C and Marx, B.D. (2021) *Practical smoothing: the joys of P-splines*. Cambridge University Press, Cambridge.

Huang, C. (2001) Boundary corrected cubic smoothing splines. *Journal of Statistical Computation and Simulation*, **70**, 107-121.

`splitSplines`

, `probeSmoothing`

,
`byIndv4Times_GRsDiff`

, `smooth.spline`

,

`predict.smooth.spline`

, JOPS.

```
data(exampleData)
fit <- fitSpline(longi.dat, response="PSA", response.smoothed = "sPSA",
x="xDAP", df = 4,
deriv=c(1,2), suffices.deriv=c("AGRdv","Acc"))
fit <- fitSpline(longi.dat, response="PSA", response.smoothed = "sPSA",
x="xDAP",
spline.type = "PS", lambda = 0.1, npspline.segments = 10,
deriv=c(1,2), suffices.deriv=c("AGRdv","Acc"))
fit <- fitSpline(longi.dat, response="PSA", response.smoothed = "sPSA",
x="xDAP", df = 4,
deriv=c(1), suffices.deriv=c("AGRdv"),
extra.rate = c(RGR.dv = "RGR"))
```

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