# summary.plr: Summarizing Polygonal Linear Regression In psda: Polygonal Symbolic Data Analysis

## Description

summary method for class plr.

## Usage

 1 2 3 ## S3 method for class 'plr' summary(object, digits = max(3L, getOption("digits") - 3L), ...) 

## Arguments

 object an object of the class plr, usually, a result of a call to plr. digits a non-null value for digits specifies the minimum number of significant digits to be printed in values. ... further arguments passed to or from other methods.

## Value

residuals calculated as the response variable minus the fitted values.

sigma the given by square root of the estimated variance of the random error

σ^2 = \frac{∑{i = 1}^{n} (y_i - \hat{y}_i)^2}{n - p - 1}

where p is two times the number of independent variables.

call the matched call.

aliased named logical vector showing if the original coefficients are aliased.

terms the terms.

coefficients a p x 4 matrix with columns for the estimated coefficient, its standard error, z-statistic and corresponding (two-sided) p-value.

## Examples

 1 2 3 4 5 6 7 8 9 yp <- psim(50, 10) #simulate 50 polygons of 10 sides xp1 <- psim(50, 10) #simulate 50 polygons of 10 sides xp2 <- psim(50, 10) #simulate 50 polygons of 10 sides e <- new.env() e$yp <- yp e$xp1 <- xp1 e\$xp2 <- xp2 fit <- plr(yp~xp1 + xp2, data = e) s <- summary(fit) 

psda documentation built on June 25, 2019, 1:03 a.m.