b0_plotprxy: Predictive X-Y Plot
In cubfits: Codon Usage Bias Fits

Description Usage Arguments Details Value Author(s) References See Also Examples

This utility function provides a basic plot of production rates.

  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)

`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()`.

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.

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

Wei-Chen Chen wccsnow@gmail.com.

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

plotbin() and plotmodel().

## 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)