predict_plot: Plot predictors versus response

In paulemms/datamining: Data mining

Plot predictors versus response

Description

Makes a matrix of pairwise scatterplots with lowess-type trend lines.

Usage

predict_plot(...)

Arguments

...	extra arguments passed to predict_plot.data.frame or plot.
formula	a formula specifying the response and predictor variables
data, x	a data frame with at least two columns
partial	a model from which to compute partial residuals (used by predict_plot.lm).
mcol, mlwd	If plotting partial residuals of an lm, the color and width of the model predictions.
layout	a vector c(rows,cols) specifying the desired layout of panels. Otherwise chosen automatically based on the size of the plotting window.
highlight	a logical vector specifying which predictors to highlight.
se	If TRUE, show standard errors in linecharts.
scol, slwd	color and width of trend lines.
span, degree, family	parameters for the trend line (see loess).
rtype	how a factor response should be handled when drawing a trend line.
identify.pred	A character vector of predictor names for which to interactively identify points. If TRUE, done for all predictors.
mar	margins within each panel
xaxt, yaxt	arguments to par
col	plotting color for symbols
asp	Aspect ratio for each panel. If "auto", the aspect ratio is chosen automatically based on the trend line and auto.aspect.
given, given.lab, nlevels, pretty, key, bg, color.palette, pch.palette	used for conditioning plots.
main, xlab, ylab	axis labels.

Value

If the predictor is numeric, makes a scatterplot with loess line on top. If the predictor is a factor, makes a linechart.

Author(s)

Tom Minka

See Also

loess, model.plot

Examples

data(Cars)
predict_plot(Price ~ ., CarsT)
fit <- lm(Price ~ ., CarsT)
predict_plot(Price~ ., CarsT, partial=fit)
# same thing using predict_plot.lm
predict_plot(fit, partial = TRUE)

paulemms/datamining documentation built on March 1, 2023, 4:01 p.m.