Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 2 3 4 5 6 7 8 9 10 11 | ```
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, ...)
``` |

`formula` |
Object of class |

`data` |
Data.frame of class |

`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 |

`stop_criterion` |
Criterion to determine the optimal number of splits in the tree component of the model;
one out of |

`splits_max` |
Maximal number of splits in the tree component. |

`fold` |
Number of folds; only for stop criterion |

`alpha` |
Significance level; only for stop criterion |

`grid_value` |
An optional parameter; |

`min_border` |
An optional parameter; |

`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 |

`weights` |
An optional vector of prior weights to be used in the fitting process; see also |

`offset` |
An a priori known component to be included in the linear predictor during fitting; see also |

`...` |
Further arguments passed to or from other methods. |

`x, object` |
Object of class |

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`

.

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 |

Moritz Berger <Moritz.Berger@imbie.uni-bonn.de>

http://www.imbie.uni-bonn.de/personen/dr-moritz-berger/

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.

1 2 3 4 5 6 7 8 9 10 |

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