varmod | R Documentation |

A *variance model* estimates the variance of predicted values.
It can be used to estimate prediction intervals.
See the `interval`

argument of `predict.earth`

.

A variance model is built by `earth`

if `earth`

's
`varmod.method`

argument is specified.
Results are stored in the `$varmod`

field of the `earth`

model.
See the vignette “Variance models in earth” for details.

You probably won't need to directly call
`print.varmod`

or `summary.varmod`

.
They get called internally by `summary.earth`

.

## S3 method for class 'varmod' summary( object = stop("no 'object' argument"), level = .95, style = "standard", digits = 2, newdata = NULL, ...)

`object` |
A |

`level` |
Same as |

`style` |
Determines how the coefficients of the |

`digits` |
Number of digits to print. Default is |

`newdata` |
Default |

`...` |
Dots are passed on. |

A `"varmod"`

object has the following fields:

`call`

The call used internally in the parent model to build the`varmod`

object.`parent`

The parent`earth`

model.`method`

Copy of the`varmod.method`

argument to the parent model.`package`

NULL, unless`method="gam"`

, in which case either`"gam"`

or`"mgcv"`

.`exponent`

Copy of the`varmod.exponent`

argument to the parent model.`lambda`

Currently always 1, meaning use absolute residuals.`rmethod`

Currently always "hc2", meaning correct the residuals with`1/(1-h_ii)`

.`converged`

Did the residual submodel IRLS converge?`iters`

Number of residual model IRLS iterations (1 to 50).`residmod`

The residual submodel. So for example, if`varmod.method="lm"`

, this will be an`lm`

object.`min.sd`

The predicted residual standard deviation is clamped so it will always be at least this value. This prevents prediction of negative or absurdly small variances. See`earth`

's`varmod.clamp`

argument. Clamping takes place in`predict.varmod`

, which is called by`predict.earth`

when estimating prediction intervals.`model.var`

An n x 1 matrix. The`model.var`

for an observation is the estimated model variance for that observation over all datasets, and is estimated with repeated cross validation. It is the variance of the mean out-of-fold prediction for that observation over`ncross`

repetitions.`abs.resids`

An n x 1 matrix. The absolute residuals used to build the residual model.`parent.x`

An n x p matrix. Parent earth model`x`

.`parent.y`

An n x 1 matrix. Parent earth model`y`

.`iter.rsq`

Weighted R-Squared of residual submodel`residmod`

, after IRLS iteration.`iter.stderr`

Standard errors of the coefficients of the residual submodel`residmod`

, after IRLS iteration.

`plot.varmod`

,
`predict.varmod`

data(ozone1) set.seed(1) # optional, for cross validation reproducibility # note: should really use ncross=30 below but for a quick demo we don't earth.mod <- earth(O3~temp, data=ozone1, nfold=10, ncross=3, varmod.method="lm") print(summary(earth.mod)) # note additional info on the variance model old.mfrow <- par(mfrow=c(2,2), mar=c(3, 3, 3, 1), mgp=c(1.5, 0.5, 0)) plotmo(earth.mod, do.par=FALSE, response.col=1, level=.90, main="earth model: O3~temp") plot(earth.mod, which=3, level=.90) # residual plot: note 90% pred and darker conf intervals par(par=old.mfrow)

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