methods | R Documentation |
geoGAM
objects
Methods for models fitted by geoGAM()
.
## S3 method for class 'geoGAM'
summary(object, ..., what = c("final", "path"))
## S3 method for class 'geoGAM'
print(x, ...)
## S3 method for class 'geoGAM'
plot(x, ..., what = c("final", "path"))
object |
an object of class |
x |
an object of class |
... |
other arguments passed to |
what |
print summary or plot partial effects of |
summary
with what = "final"
calls summary.gam
to display a summary of the final (geo)additive model. plot
with what = "final"
calls plot.gam
to plot partial residual plots of the final model.
summary
with what = "path"
give a summary of covariates selected in each step of model building.
plot
with what = "path"
calls plot.mboost
to plot the path of the gradient boosting algorithm.
For what == "final"
summary returns a list of 3:
summary.gam |
containing the values of |
summary.validation$cv |
cross validation statistics. |
summary.validation$validation |
validation set statistics. |
For what == "path"
summary returns a list of 13:
response |
name of response. |
family |
family used for |
n.obs |
number of observations used for model fitting. |
n.obs.val |
number of observations used for model validation. |
n.covariates |
number of initial covariates including factors. |
n.cov.chosen |
number of covariates in final model. |
list.factors |
list of factors chosen as offset. |
mstop |
number of optimal iterations of gradient boosting. |
list.baselearners |
list of covariate names selected by gradient boosting. |
list.effect.size |
list of covariate names after cross validation of effect size in gradient boosting. |
list.backward |
list of covariate names after backward selection. |
list.aggregation |
list of aggregated factor levels. |
list.gam.final |
list of covariate names in final model. |
M. Nussbaum
Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A.: Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models, SOIL, 3, 191-210, doi:10.5194/soil-3-191-2017, 2017.
geoGAM
, gam
, predict.gam
### small example with earthquake data
data(quakes)
set.seed(2)
quakes <- quakes[ sample(1:nrow(quakes), 50), ]
quakes.geogam <- geoGAM(response = "mag",
covariates = c("depth", "stations"),
data = quakes,
seed = 2,
max.stop = 5,
cores = 1)
summary(quakes.geogam)
summary(quakes.geogam, what = "path")
plot(quakes.geogam)
plot(quakes.geogam, what = "path")
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