Description Usage Arguments Details Value Warning Note Author(s) References See Also Examples

Allows the user to specify a loess fit within a GAMLSS model. This function is similar to the `lo`

function in the `gam`

implementation of package `gam`

see Chambers and Hastie (1991).

The function `vis.lo()`

allows plotting the results.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
lo(formula, control = lo.control(...), ...)
lo.control(span = 0.75, enp.target = NULL,
degree = 2, parametric = FALSE, drop.square = FALSE,
normalize = TRUE, family = c("gaussian", "symmetric"),
method = c("loess", "model.frame"),
surface = c("interpolate", "direct"),
statistics = c("approximate", "exact", "none"),
trace.hat = c("exact", "approximate"),
cell = 0.2, iterations = 4,iterTrace = FALSE, ...)
vis.lo(obj, se=-1, rug = FALSE, partial.resid = FALSE,
col.term = "darkred", col.shaded = "gray",
col.res = "lightblue", col.rug = "gray", lwd.term = 1.5,
cex.res = 1, pch.res = par("pch"),
type = c("persp", "contour"), col.surface = "gray",
nlevels = 30, n.grid = 30, image = TRUE, ...)
``` |

`formula` |
a formula specifying the explanatory variables |

`control` |
a control to be passed to the |

`...` |
extra arguments |

`span` |
the number of observations in a neighbourhood. This is the smoothing parameter for a loess fit. |

`enp.target` |
an alternative way to specify span, as the approximate equivalent number degrees of freedom to be used. See also the help file of the |

`degree` |
the degree of local polynomial; can be 1 or 2. See also the help file of |

`parametric` |
should any terms be fitted globally rather than locally? See the help file of |

`drop.square` |
for fits with more than one predictor and degree=2, should the quadratic term be dropped for particular predictors?. See also help file of |

`normalize` |
should the predictors be normalized to a common scale if there is more than one? See the help file of |

`family` |
if "gaussian" fitting is by least-squares, and if "symmetric" a re-descending M estimator is used with Tukey's biweight function. See the help file of |

`method` |
fit the model or just extract the model frame. See the help file of |

`surface` |
should the fitted surface be computed exactly or via interpolation from a kd tree? See also
the help file of |

`statistics` |
should the statistics be computed exactly or approximately? See the help file of |

`trace.hat` |
should the trace of the smoother matrix be computed exactly or approximately? See the help file of |

`cell` |
if interpolation is used this controls the accuracy of the approximation via the maximum number of points in a cell in the kd tree. See the help file of |

`iterations` |
the number of iterations used in robust fitting. See the help file of |

`iterTrace` |
logical (or integer) determining if tracing information during the robust iterations (iterations>= 2) is produced. See the help file of |

`obj` |
an |

`se` |
if |

`rug` |
whether to plot a rug in the plot |

`partial.resid` |
whether to plot the partial residuals |

`col.term` |
the colour of the line of fitted term |

`cex.res` |
the shading of standard |

`col.shaded` |
the shading of standard error intervals |

`col.res` |
the colour of partial residuals |

`col.rug` |
the colour of the rug |

`lwd.term` |
the width of the line |

`pch.res` |
The character for the partial residuals |

`type` |
The type of the plot if the x's are two dimensional |

`col.surface` |
the colour of the fitted surface |

`nlevels` |
the number of levels used in |

`n.grid` |
The number of points to evaluate the surface |

`image` |
whether to use |

Note that `lo`

itself does no smoothing; it simply sets things up for the function `gamlss.lo()`

which is used by the backfitting function `gamlss.add()`

.

a loess object is returned.

In this version the first argument is a formula NOT a list as in the previous one

Note that `lo`

itself does no smoothing; it simply sets things up for `gamlss.lo()`

to do the backfitting.

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby, (The original `lo()`

function was based on the Trevor Hastie's S-plus `lo()`

function. See also the documentation of the `loess`

function for the authorship of the function.

Chambers, J. M. and Hastie, T. J. (1991). *Statistical Models in S*, Chapman and Hall, London.

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
*Appl. Statist.*, **54**, part 3, pp 507-554.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019)
*Distributions for modeling location, scale, and shape: Using GAMLSS in R*, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
*Journal of Statistical Software*, Vol. **23**, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017)
*Flexible Regression and Smoothing: Using GAMLSS in R*, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# fitting a loess curve with span=0.4 plus the a quarterly effect
aids1<-gamlss(y~lo(~x,span=0.4)+qrt,data=aids,family=PO) #
term.plot(aids1, page=1)
## Not run:
r1 <- gamlss(R~lo(~Fl)+lo(~A), data=rent, family=GA)
term.plot(r1, pages=1)
vis.lo(getSmo(r1, which=1), partial=T)
r2 <- gamlss(R~lo(~Fl+A), data=rent, family=GA)
term.plot(r2, pages=1)
vis.lo(getSmo(r2, which=1))
vis.lo(getSmo(r2, which=1), se=1.97)
vis.lo(getSmo(r2, which=1), partial.res=T)
## End(Not run)
``` |

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