cubfits (version 0.1-2)

Plotprxy: Predictive X-Y Plot

Description

This utility function provides a basic plot of production rates.

Usage

plotprxy(x, y, x.ci = NULL, y.ci = NULL, log10.x = TRUE, log10.y = TRUE, add.lm = TRUE, add.one.to.one = TRUE, weights = NULL, add.legend = TRUE, xlim = NULL, ylim = NULL, xlab = "Predicted Production Rate (log10)", ylab = "Observed Production Rate (log10)", main = NULL)

Arguments

x

expression values.

y

expression values, of the same length of x.

x.ci

confidence interval of x, of dimension length{x} * 2, for outliers labeling.

y.ci

confidence interval of y, of dimension length{y} * 2, for outliers labeling.

log10.x

log10() and mean transformation of x axis.

log10.y

log10() and mean transformation of y axis.

add.lm

if add lm() fit.

add.one.to.one

if add one-to-one line.

weights

weights to lm().

add.legend

if add default legend.

xlim

limits of x-axis.

ylim

limits of y-axis.

xlab

an option passed to plot().

ylab

an option passed to plot().

main

an option passed to plot().

Value

A scatter plot with a fitted lm() line and R squared value.

Details

As the usual X-Y plot where x and y are expression values.

If add.lm = TRUE and weights are given, then both ordinary and weighted least squares results will be plotted.

References

https://github.com/snoweye/cubfits/

See Also

plotbin() and plotmodel().

Examples

## Not run: 
# suppressMessages(library(cubfits, quietly = TRUE))
# 
# y.scuo <- convert.y.to.scuo(ex.train$y)
# SCUO <- calc_scuo_values(y.scuo)$SCUO
# plotprxy(ex.train$phi.Obs, SCUO)
# ## End(Not run)