gamtree | R Documentation |
gamtree
recursively partitions a dataset into subgroups with
penalized GAMs, characterized by differences in the parameter estimates.
gamtree(
formula,
data,
weights = NULL,
REML = TRUE,
method = "mob",
cluster = NULL,
offset = NULL,
verbose = FALSE,
parm = c(1, 2, 4),
gam_ctrl = list(),
tree_ctrl = list(),
alt_formula = NULL,
...
)
formula |
specifies the model formula, consisting of three
parts: the response variable followed by a tilde ('~'); the terms for the
node-specific GAMs, followed by a vertical bar ('|') and the potential
partitioning variables (separated by a '+').
The 'by' argument of function
|
data |
|
weights |
numeric vector of length |
REML |
logical, defaults to |
method |
character, one of |
cluster |
optional, a name refering to a colum of |
offset |
numeric vector of length |
verbose |
logical. Should progress be printed to the commande line in every iteration? If true, the iteration number, information on the splitting procedure, and the log-likelihood (with df) value of the fitted full mixed-effects gam model is printed. |
parm |
vector of one or more integers, indicating which parameters should be
included in the parameter stability tests. The default |
gam_ctrl |
a list of fit control parameters to replace defaults returned by
|
tree_ctrl |
a |
alt_formula |
list with two elements, for specifying non-standard model formulae
for GAM. E.g., the formula list required for use of the |
... |
additional arguments to be passed to function |
MOB is short for model-based recursive
partitioning, ctree is short for conditional inference tree. MOB is
based more strongly on parametric theory, thereby allowing for easy inclusion
of clustering structures into the estimation procedure (see also argument
cluster
), yielding similar to a GEE-type approach for estimation of
multilevel and longitudinal data structures. Yet, computation time for MOB is much
larger than for ctree, which is mostly due to how it searches for
the optimal splitting value, after the variable for splitting
has been selected. ctree uses tests based on permutation theory,
and thereby offers a less parametrically oriented approach. It is much
faster than MOB, but does not provide a natural way of accounting
for multilevel or longitudinal data structures.
Returns an object of class "gamtree"
. This is a list, containing
(amongst others) the GAM-based recursive partition (in $tree
).
The following methods are available to extract information from the fitted object:
predict.gamtree
, for obtaining predicted values for training and new
observations; plot.gamtree
for plotting the tree and variables' effects;
coef.gamtree
, fixef.gamtree
and ranef.gamtree
for extracting estimated coefficients. VarCorr.gamtree
for extracting
random-effects (co)variances, summary.gamtree
for a summary of the
fitted models.
predict.gamtree
plot.gamtree
coef.gamtree
summary.gamtree
gt_m <- gamtree(Pn ~ s(PAR, k = 5L) | Species, data = eco, cluster = Specimen)
summary(gt_m)
gt_c <- gamtree(Pn ~ s(PAR, k = 5L) | Species, data = eco, method = "ctree")
summary(gt_c)
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