R: Formula Interface For 'smooth.spline'

SmoothSpline {DescTools} | R Documentation

## Formula Interface For `smooth.spline`

### Description

smooth.spline has no formula interface, which is sometimes inconvenient, if one simply wants to copy a formula of a linear model or a plot to spline.

### Usage

    SmoothSpline(x, ...)

    ## Default S3 method:
    SmoothSpline(x, y = NULL, w = NULL, df, spar = NULL, cv = FALSE,
                 all.knots = FALSE, nknots = .nknots.smspl, keep.data = TRUE,
                 df.offset = 0, penalty = 1, control.spar = list(),
                 tol = 0.000001 * IQR(x), ...)

    ## S3 method for class 'formula'
    SmoothSpline(formula, data, subset, na.action, ...)

### Arguments

`x` | a vector giving the values of the predictor variable, or a list or a two-column matrix specifying x and y.
`y` | responses. If `y` is missing or `NULL`, the responses are assumed to be specified by `x`, with `x` the index vector.
`w` | optional vector of weights of the same length as `x`; defaults to all 1.
`df` | the desired equivalent number of degrees of freedom (trace of the smoother matrix).
`spar` | smoothing parameter, typically (but not necessarily) in (0,1]. The coefficient λ of the integral of the squared second derivative in the fit (penalized log likelihood) criterion is a monotone function of `spar`, see the details below.
`cv` | ordinary (`TRUE`) or ‘generalized’ cross-validation (GCV) when `FALSE`; setting it to `NA` skips the evaluation of leverages and any score.
`all.knots` | if `TRUE`, all distinct points in `x` are used as knots. If `FALSE` (default), a subset of `x[]` is used, specifically `x[j]` where the `nknots` indices are evenly spaced in `1:n`, see also the next argument `nknots`.
`nknots` | integer or `function` giving the number of knots to use when `all.knots = FALSE`. If a function (as by default), the number of knots is `nknots(nx)`. By default for nx > 49 this is less than nx, the number of unique `x` values, see the Note.
`keep.data` | logical specifying if the input data should be kept in the result. If `TRUE` (as per default), fitted values and residuals are available from the result.
`df.offset` | allows the degrees of freedom to be increased by `df.offset` in the GCV criterion.
`penalty` | the coefficient of the penalty for degrees of freedom in the GCV criterion.
`control.spar` | optional list with named components controlling the root finding when the smoothing parameter `spar` is computed, i.e., missing or `NULL`, see below. Note that this is partly experimental and may change with general spar computation improvements! low: lower bound for `spar`; defaults to -1.5 (used to implicitly default to 0 in R versions earlier than 1.4). high: upper bound for `spar`; defaults to +1.5. tol: the absolute precision (tolerance) used; defaults to 1e-4 (formerly 1e-3). eps: the relative precision used; defaults to 2e-8 (formerly 0.00244). trace: logical indicating if iterations should be traced. maxit: integer giving the maximal number of iterations; defaults to 500. Note that `spar` is only searched for in the interval [low, high].
`tol` | a tolerance for same-ness or uniqueness of the `x` values. The values are binned into bins of size `tol` and values which fall into the same bin are regarded as the same. Must be strictly positive (and finite).
`formula` | a formula of the form `lhs ~ rhs` where `lhs` gives the data values and rhs the corresponding groups.
`data` | The data frame from which the formula should be evaluated.
`subset` | an optional vector specifying a subset of observations to be used.
`na.action` | a function which indicates what should happen when the data contain NAs. Defaults to `getOption("na.action")`.
`...` | Other arguments to be passed to `smooth.spline`.

### Author(s)

Andri Signorell <andri@signorell.net>

### See Also

`smooth.spline`, `lines.smooth.spline`

### Examples

    plot(temperature ~ delivery_min, data=d.pizza)
    lines(SmoothSpline(temperature ~ delivery_min, data=d.pizza))

[Package DescTools version 0.99.28 Index]