Description Usage Arguments Details Value References Examples

plr is used to fit polygonal linear models.

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`formula` |
an object of class "formula": a symbolic description of the model to be fitted. |

`data` |
a environment that contains the variables of the study. |

`model` |
logicals. If TRUE the corresponding components of the fit are returned. |

`...` |
additional arguments to be passed to the low level polygonal linear regression fitting functions. |

Polygonal linear regression is the first model to explain the behavior of a symbolic polygonal
variable in furnction to other polygonal variables, dependent and regressors, respectively.
PLR is based on the
least squares and uses the center and radius of polygons as representation them. The model is
given by *y = Xβ + ε*, where *y, X, β*, and *ε* is the dependent
variable, matrix model, unknown parameters, and non-observed errors. In the model, the vector
*y = (y_c^T, y_r)^T*, where *y_c* and *y_r* is the center and radius of center and radius.
The matrix model *X = diag(X_c, X_r)* for *X_c* and *X_r* describing the center and radius
of regressors variables and finally, *β = (β_c^T, β_r^T)^T*. A detailed study about the
model can be found in Silva et al.(2019).

residuals is calculated as the response variable minus the fitted values.

rank the numeric rank of the fitted polygonal linear model.

call the matched call.

fitted.values the fitted mean values.

terms the `terms`

.

coefficients a named vector of coefficients.

model the matrix model for center and radius.

Silva, W.J.F, Souza, R.M.C.R, Cysneiros, F.J.A. (2019) https://www.sciencedirect.com/science/article/pii/S0950705118304052.

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