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ggsmatr 'ggplot2' based SMATR plots

Source: R/ggsmatr.R
ggsmatr.Rd

Create a scatter plot based on the coefficients from (Standardised) Major Axis Estimation fit.

Usage

ggsmatr(data, groups, xvar, yvar, sma.fit, ci = FALSE, ci.alpha = 0.2, n = 100)

Arguments

data

a dataframe.

groups

group variable.

xvar

x variable for drawing.

yvar

y variable for drawing.

sma.fit

an sma or ma object fitted in SMATR package.

ci

logical. If TRUE, add a parametric confidence ribbon for the SMA slope. Default is FALSE.

ci.alpha

Alpha transparency passed to ggplot2::geom_ribbon(). Default is 0.2.

n

Number of x values at which the confidence ribbon is evaluated in each group. Default is 100.

Value

ggplot based plot of sma.

Details

Fitted SMA/MA lines are drawn from the slope and elevation stored in sma.fit. When ci = TRUE, a ribbon is added from the slope confidence intervals already computed by smatr::sma() (Slope_lowCI and Slope_highCI in sma.fit$groupsummary). The confidence level is the one used in sma(..., alpha = ).

Given that SMA lines pass through the group centroid \((\bar{x}, \bar{y})\), slope and intercept are not independent. The ribbon is therefore a family of lines through that centroid: $$y = \bar{y} + b_{\mathrm{CI}}(x - \bar{x}).$$ ymin and ymax are obtained with pmin()/pmax(), because the lower slope produces the higher line when \(x < \bar{x}\). The envelope pinches at the group mean and is not related with the OLS band of geom_smooth(). It does not combine intercept CIs with slope CIs independently, and it is not a bootstrap prediction interval.

References

Warton, D. I., Wright, I. J., Falster, D. S. and Westoby, M. (2006). Bivariate line-fitting methods for allometry. Biological Reviews 81, 259–291.

Warton, D. I., Duursma, R. A., Falster, D. S. and Taskinen, S. (2012). smatr 3 – an R package for estimation and inference about allometric lines. Methods in Ecology and Evolution 3, 257–259.

Examples

datafile <- system.file("iris.csv", package = "ggsmatr")
df.iris <- read.csv(datafile, encoding = "UTF-8")

library(ggsmatr)
library(ggplot2)
library(smatr)
fit <- sma(Sepal.Length ~ Sepal.Width + Species,
           data = df.iris, shift = TRUE, elev.test = TRUE, alpha = 0.05)

ggsmatr(data = df.iris, groups = "Species",
        xvar = "Sepal.Width", yvar = "Sepal.Length",
        sma.fit = fit) +
  theme(legend.position = "top", legend.title = element_blank()) +
  ylab("Sepal.Length") +
  xlab("Sepal.Width")

# parametric slope-CI ribbon, using the intervals already stored in the sma fit
ggsmatr(data = df.iris, groups = "Species",
        xvar = "Sepal.Width", yvar = "Sepal.Length",
        sma.fit = fit, ci = TRUE) +
  theme(legend.position = "top", legend.title = element_blank()) +
  ylab("Sepal.Length") +
  xlab("Sepal.Width")