regionInfl: Identify regions of significance reversal and influence...

In reverseR: Linear Regression Stability to Significance Reversal

Identify regions of significance reversal and influence measure threshold

Description

Identifies regions of an (univariate) linear model in which a future data point would result in either
a) significance reversal, or
b) any selected influence measure as given in crit exceed its threshold value.
This is intended mainly for visual/didactical purposes.

Usage

regionInfl(model, div.x = 20, div.y = 20, grid = TRUE, pred.int = TRUE,
       crit = c("P", "dfb.Slope", "dffit", "cov.r", "cook.d", "hat", "hadi",
       "sR", "cdr", "Si"), cex.grid = 0.5, alpha = 0.05, xlim = NULL, ylim = NULL, ...)

Arguments

model	the linear model of class lm.
div.x	the number of grid division for the x-axis.
div.y	the number of grid division for the y-axis.
grid	logical. Show the grid lines on the plot or not.
pred.int	logical. Show the 95% prediction interval on the plot or not.
crit	the criterion to use. Either "P" for significance reversal or any of the influence measures given there.
cex.grid	size of the grid points.
alpha	the \alpha-level to be set as threshold.
xlim	similar to xlim, a 2-element vector for the x-axis limits, overrides fac.x.
ylim	similar to ylim, a 2-element vector for the y-axis limits, overrides fac.y.
...	other parameters to be supplied to plot or lmInfl.

Details

For a given linear model y_i = \beta_0 + \beta_1 x_i + \varepsilon, each (a, b) pair from a grid of values (a_1 \ldots a_j, b_1 \ldots b_k) is added to the data, and an updated model (y_i, b_k) = \beta_0 + \beta_1 (x_i, a_j) + \varepsilon is created. If the updated model's p \leq \alpha or any of the influence measures does not exceed its published threshold, it is plotted in green, otherwise in orange. If outlier = TRUE, a possible reverser is eliminated prior to analysis but visualized in the plot.

Value

A plot with the regions marked in orange or green, and the grid matrix (grid) including the criterion outcome in 1 (green) or 0 (orange).

Author(s)

Andrej-Nikolai Spiess

Examples

## Model with p = 0.014 
set.seed(7)
N <- 20
x <- runif(N, 1, 100)
y <- 0.05 * x + rnorm(N, 0, 2)
LM1 <- lm(y ~ x)
summary(LM1)
regionInfl(LM1, crit = "P", div.x = 20, div.y = 20, cex.grid = 1, 
           xlim = c(-20, 120), ylim = c(-5, 10))

reverseR documentation built on Sept. 12, 2024, 7:32 a.m.