# lo: Specify a loess fit in a GAM formula In gam: Generalized Additive Models

## Description

A symbolic wrapper to indicate a smooth term in a formala argument to gam

## Usage

 ```1 2``` ```lo(..., span=0.5, degree=1) gam.lo(x, y, w, span, degree, ncols, xeval) ```

## Arguments

 `...` the unspecified `...` can be a comma-separated list of numeric vectors, numeric matrix, or expressions that evaluate to either of these. If it is a list of vectors, they must all have the same length. `span` the number of observations in a neighborhood. This is the smoothing parameter for a `loess` fit. If specified, the full argument name `span` must be written. `degree` the degree of local polynomial to be fit; currently restricted to be `1` or `2`. If specified, the full argument name `degree` must be written. `x` for `gam.lo`, the appropriate basis of polynomials generated from the arguments to `lo`. These are also the variables that receive linear coefficients in the GAM fit. `y` a response variable passed to `gam.lo` during backfitting `w` weights `ncols` for `gam.lo` the number of columns in `x` used as the smoothing inputs to local regression. For example, if `degree=2`, then `x` has two columns defining a degree-2 polynomial basis. Both are needed for the parameteric part of the fit, but `ncol=1` telling the local regression routine that the first column is the actually smoothing variable. `xeval` If this argument is present, then `gam.lo` produces a prediction at `xeval`.

## Details

A smoother in gam separates out the parametric part of the fit from the non-parametric part. For local regression, the parametric part of the fit is specified by the particular polynomial being fit locally. The workhorse function `gam.lo` fits the local polynomial, then strips off this parametric part. All the parametric pieces from all the terms in the additive model are fit simultaneously in one operation for each loop of the backfitting algorithm.

## Value

`lo` returns a numeric matrix. The simplest case is when there is a single argument to `lo` and `degree=1`; a one-column matrix is returned, consisting of a normalized version of the vector. If `degree=2` in this case, a two-column matrix is returned, consisting of a degree-2 polynomial basis. Similarly, if there are two arguments, or the single argument is a two-column matrix, either a two-column matrix is returned if `degree=1`, or a five-column matrix consisting of powers and products up to degree `2`. Any dimensional argument is allowed, but typically one or two vectors are used in practice.

The matrix is endowed with a number of attributes; the matrix itself is used in the construction of the model matrix, while the attributes are needed for the backfitting algorithms `general.wam` (weighted additive model) or `lo.wam` (currently not implemented). Local-linear curve or surface fits reproduce linear responses, while local-quadratic fits reproduce quadratic curves or surfaces. These parts of the `loess` fit are computed exactly together with the other parametric linear parts

When two or more smoothing variables are given, the user should make sure they are in a commensurable scale; `lo()` does no normalization. This can make a difference, since `lo()` uses a spherical (isotropic) neighborhood when establishing the nearest neighbors.

Note that `lo` itself does no smoothing; it simply sets things up for `gam`; `gam.lo` does the actual smoothing. of the model.

One important attribute is named `call`. For example, `lo(x)` has a call component `gam.lo(data[["lo(x)"]], z, w, span = 0.5, degree = 1, ncols = 1)`. This is an expression that gets evaluated repeatedly in `general.wam` (the backfitting algorithm).

`gam.lo` returns an object with components

 `residuals` The residuals from the smooth fit. Note that the smoother removes the parametric part of the fit (using a linear fit with the columns in `x`), so these residual represent the nonlinear part of the fit. `nl.df` the nonlinear degrees of freedom `var` the pointwise variance for the nonlinear fit

When `gam.lo` is evaluated with an `xeval` argument, it returns a matrix of predictions.

## Author(s)

Written by Trevor Hastie, following closely the design in the "Generalized Additive Models" chapter (Hastie, 1992) in Chambers and Hastie (1992).

## References

Hastie, T. J. (1992) Generalized additive models. Chapter 7 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth \& Brooks/Cole.

Hastie, T. and Tibshirani, R. (1990) Generalized Additive Models. London: Chapman and Hall.

`s`, `bs`, `ns`, `poly`, `loess`

## Examples

 ```1 2 3 4``` ```y ~ Age + lo(Start) # fit Start using a loess smooth with a (default) span of 0.5. y ~ lo(Age) + lo(Start, Number) y ~ lo(Age, span=0.3) # the argument name span cannot be abbreviated. ```

### Example output

```Loading required package: splines