additive {additive}  R Documentation 
additive()
is a way to generate a specification of a model
before fitting and allows the model to be created using
mgcv package in R.
additive(
mode = "regression",
engine = "mgcv",
fitfunc = NULL,
formula.override = NULL,
family = NULL,
method = NULL,
optimizer = NULL,
control = NULL,
scale = NULL,
gamma = NULL,
knots = NULL,
sp = NULL,
min.sp = NULL,
paraPen = NULL,
chunk.size = NULL,
rho = NULL,
AR.start = NULL,
H = NULL,
G = NULL,
offset = NULL,
weights = NULL,
subset = NULL,
start = NULL,
etastart = NULL,
mustart = NULL,
drop.intercept = NULL,
drop.unused.levels = NULL,
cluster = NULL,
nthreads = NULL,
gc.level = NULL,
use.chol = NULL,
samfrac = NULL,
coef = NULL,
discrete = NULL,
select = NULL,
fit = NULL
)
## S3 method for class 'additive'
update(
object,
parameters = NULL,
fitfunc = NULL,
formula.override = NULL,
family = NULL,
method = NULL,
optimizer = NULL,
control = NULL,
scale = NULL,
gamma = NULL,
knots = NULL,
sp = NULL,
min.sp = NULL,
paraPen = NULL,
chunk.size = NULL,
rho = NULL,
AR.start = NULL,
H = NULL,
G = NULL,
offset = NULL,
weights = NULL,
subset = NULL,
start = NULL,
etastart = NULL,
mustart = NULL,
drop.intercept = NULL,
drop.unused.levels = NULL,
cluster = NULL,
nthreads = NULL,
gc.level = NULL,
use.chol = NULL,
samfrac = NULL,
coef = NULL,
discrete = NULL,
select = NULL,
fit = NULL,
fresh = FALSE,
...
)
additive_fit(formula, data, ...)
mode 
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification". 
engine 
A single character string specifying what computational
engine to use for fitting. Possible engines are listed below.
The default for this model is 
fitfunc 
A named character vector that describes how to call
a function for fitting a generalized additive model. This defaults
to 
formula.override 
Overrides the formula; for details see

family 
This is a family object specifying the distribution and link to use in
fitting etc (see 
method 
The smoothing parameter estimation method. 
optimizer 
An array specifying the numerical optimization method to use to optimize the smoothing
parameter estimation criterion (given by 
control 
A list of fit control parameters to replace defaults returned by

scale 
If this is positive then it is taken as the known scale parameter. Negative signals that the scale parameter is unknown. 0 signals that the scale parameter is 1 for Poisson and binomial and unknown otherwise. Note that (RE)ML methods can only work with scale parameter 1 for the Poisson and binomial cases. 
gamma 
Increase this beyond 1 to produce smoother models. 
knots 
this is an optional list containing user specified knot values to be used for basis construction.
For most bases the user simply supplies the knots to be used, which must match up with the 
sp 
A vector of smoothing parameters can be provided here.
Smoothing parameters must be supplied in the order that the smooth terms appear in the model
formula. Negative elements indicate that the parameter should be estimated, and hence a mixture
of fixed and estimated parameters is possible. If smooths share smoothing parameters then 
min.sp 
Lower bounds can be supplied for the smoothing parameters. Note
that if this option is used then the smoothing parameters 
paraPen 
optional list specifying any penalties to be applied to parametric model terms.

chunk.size 
The model matrix is created in chunks of this size, rather than ever being formed whole.
Reset to 
rho 
An AR1 error model can be used for the residuals (based on dataframe order), of Gaussianidentity
link models. This is the AR1 correlation parameter. Standardized residuals (approximately
uncorrelated under correct model) returned in

AR.start 
logical variable of same length as data, 
H 
A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied, with this as its coefficient matrix. A common use of this term is to add a ridge penalty to the parameters of the GAM in circumstances in which the model is close to unidentifiable on the scale of the linear predictor, but perfectly well defined on the response scale. 
G 
Usually 
offset 
Can be used to supply a model offset for use in fitting. Note
that this offset will always be completely ignored when predicting, unlike an offset
included in 
weights 
prior weights on the contribution of the data to the log likelihood. Note that a weight of 2, for example,
is equivalent to having made exactly the same observation twice. If you want to reweight the contributions
of each datum without changing the overall magnitude of the log likelihood, then you should normalize the weights
(e.g. 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
start 
Initial values for the model coefficients. 
etastart 
Initial values for the linear predictor. 
mustart 
Initial values for the expected response. 
drop.intercept 
Set to 
drop.unused.levels 
by default unused levels are dropped from factors before fitting. For some smooths involving factor variables you might want to turn this off. Only do so if you know what you are doing. 
cluster 

nthreads 
Number of threads to use for noncluster computation (e.g. combining results from cluster nodes).
If 
gc.level 
to keep the memory footprint down, it can help to call the garbage collector often, but this takes a substatial amount of time. Setting this to zero means that garbage collection only happens when R decides it should. Setting to 2 gives frequent garbage collection. 1 is in between. Not as much of a problem as it used to be. 
use.chol 
By default 
samfrac 
For very large sample size Generalized additive models the number of iterations needed for the model fit can
be reduced by first fitting a model to a random sample of the data, and using the results to supply starting values. This initial fit is run with sloppy convergence tolerances, so is typically very low cost. 
coef 
initial values for model coefficients 
discrete 
experimental option for setting up models for use with discrete methods employed in 
select 
If this is 
fit 
If this argument is 
object 
A Generalized Additive Model (GAM) specification. 
parameters 
A 1row tibble or named list with main
parameters to update. If the individual arguments are used,
these will supersede the values in 
fresh 
A logical for whether the arguments should be modified inplace of or replaced wholesale. 
... 
Other arguments passed to internal functions. 
formula 
A GAM formula, or a list of formulae (see 
data 
A data frame or list containing the model response variable and
covariates required by the formula. By default the variables are taken
from 
The arguments are converted to their specific names at the
time that the model is fit. Other options and argument can be
set using set_engine()
. If left to their defaults
here (NULL
), the values are taken from the underlying model
functions. If parameters need to be modified, update()
can be
used in lieu of recreating the object from scratch.
The data given to the function are not saved and are only used
to determine the mode of the model. For additive()
, the
possible modes are "regression" and "classification".
The model can be created by the fit()
function using the
following engines:
mgcv: "mgcv"
An updated model specification.
Engines may have preset default arguments when executing the model fit call. For this type of model, the template of the fit calls are:
additive() %>% set_engine("mgcv") %>% translate()
## Generalized Additive Model (GAM) Specification (regression) ## ## Computational engine: mgcv ## ## Model fit template: ## additive::additive_fit(formula = missing_arg(), data = missing_arg())
mgcvpackage
,
gam
,
bam
,
gamObject
,
gam.models
,
smooth.terms
,
predict.gam
,
plot.gam
,
summary.gam
,
gam.side
,
gam.selection
,
gam.control
,
gam.check
,
vis.gam
,
family.mgcv
,
formula.gam
,
family
,
formula
,
update.formula
.
additive()
show_model_info("additive")
additive(mode = "classification")
additive(mode = "regression")
set.seed(2020)
dat < gamSim(1, n = 400, dist = "normal", scale = 2)
additive_mod <
additive() %>%
set_engine("mgcv") %>%
fit(
y ~ s(x0) + s(x1) + s(x2) + s(x3),
data = dat
)
summary(additive_mod$fit)
model < additive(select = FALSE)
model
update(model, select = TRUE)
update(model, select = TRUE, fresh = TRUE)