structree: Tree-Structured Clustering

In structree: Tree-Structured Clustering

Description

Fusion of categories of ordinal or nominal predictors or fusion of measurement units by tree-structured clustering.

Usage

structree(formula, data, family = gaussian, stop_criterion = c("AIC",
  "BIC", "CV", "pvalue"), splits_max = NULL, fold = 5, alpha = 0.05,
  grid_value = NULL, min_border = NULL, ridge = FALSE,
  constant_covs = FALSE, trace = TRUE, plot = TRUE, k = 10,
  weights = NULL, offset = NULL, ...)

## S3 method for class 'structree'
print(x, ...)

## S3 method for class 'structree'
coef(object, ...)

Arguments

formula	Object of class formula: a symbolic description of the model to be fitted. See detail.
data	Data.frame of class data.frame containing the variables of the model.
family	a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. See family for details of family functions.
stop_criterion	Criterion to determine the optimal number of splits in the tree component of the model; one out of "AIC", "BIC", "CV" and "pvalue".
splits_max	Maximal number of splits in the tree component.
fold	Number of folds; only for stop criterion "CV".
alpha	Significance level; only for stop criterion "pvalue".
grid_value	An optional parameter; grid_value is a scalar giving the minimal distance between two adjacent observation units that are used as candidates for splitting; only for repeated measurements.
min_border	An optional parameter; min_border is a integer giving the minimal size of the outer nodes of the tree; only for repeated measurements.
ridge	If true, a small ridge penalty is added to obtain the order of measurement units; only for repeated measurements.
constant_covs	Must be set to true, if constant covariates are available; only for repeated measurments (currently only available for Gaussian response).
trace	If true, information about the estimation progress is printed.
plot	If true, the smooth components of the model are plottet; only for categorical predictors.
k	Dimension of the B-spline basis that is used to fit smooth components. For details see s; only for categorical predictors.
weights	An optional vector of prior weights to be used in the fitting process; see also glm.
offset	An a priori known component to be included in the linear predictor during fitting; see also glm.
...	Further arguments passed to or from other methods.
x, object	Object of class "structree".

Details

A typical formula has the form response ~ predictors, where response is the name of the response variable and predictors is a series of terms that specify the predictor of the model.

For an ordinal or nominal predictors z one has to enter tr(x) into the formula.

For smooth components x one has to enter s(x) into the formula; currently not implemented for repeated measurements.

For fixed effects z of observation units u one has to enter tr(z|u) into the formula. An unit-specific intercept is specified by tr(1|u).

The framework only allows for categorical predictors or observations units in the tree component, but not both. All other predictors with a linear term are entered as usual by x1+...+xp.

Value

Object of class "structree". An object of class "structree" is a list containing the following components:

coefs_end	all coefficients of the estimated model
partitions	list of matrices containing the partitions of the predictors in the tree component including all iterations
beta_hat	list of matrices with the fitted coefficients in the tree component including all iterations
which_opt	number of the optimal model (total number of splits-1)
opts	number of splits per predictor in the tree component
order	list of ordered split-points of the predictors in the tree component
tune_values	value of the stopping criterion that determine the optimal model
group_ID	list of the group IDs for each observations
coefs_group	list of coefficients of the estimated model
y	Response vector
DM_kov	Design matrix

Author(s)

Moritz Berger <[e-mail]>
http://www.imbie.uni-bonn.de/personen/dr-moritz-berger/

References

Tutz, Gerhard and Berger, Moritz (2018): Tree-structured modelling of categorical predictors in regression, Advances in Data Analysis and Classification 12(3), 737-758.

Berger, Moritz and Tutz, Gerhard (2018): Tree-structured clustering in fixed effects models, Journal of Computational and Graphical Statistics 27(2), 380-392.

See Also

plot.structree

Examples

data(rent)

## Not run: 
mod <- structree(nmqm~tr(bez)+tr(bj)+tr(rooms)+badkach0,data=rent,
                 family=gaussian,stop_criterion="CV")

print(mod)
coef(mod)

## End(Not run)

structree documentation built on July 2, 2020, 12:27 a.m.