eda_rfs: Residual-Fit Spread Plot

In mgimond/tukeyedar: Tukey Inspired Exploratory Data Analysis Functions

Residual-Fit Spread Plot

Description

eda_rfs generates a Cleveland residual-fit spread plot for univariate or bivariate data.

Usage

eda_rfs(
  dat,
  x = NULL,
  grp = NULL,
  p = 1L,
  tukey = FALSE,
  show.par = TRUE,
  stat = mean,
  grey = 0.7,
  pch = 21,
  p.col = "grey50",
  p.fill = "grey80",
  inner = 0.9,
  q = FALSE,
  size = 0.8,
  alpha = 0.7,
  ylim = NULL,
  bar = FALSE
)

Arguments

dat	An eda_lm model, an lm model or a dataframe of univariate data.
x	Column of values if dat is a dataframe, ignored otherwise.
grp	Column of categorical variable if dat is a dataframe, ignored otherwise.
p	Power transformation to apply to univariate data. Ignored if linear model is passed to function.
tukey	Boolean determining if a Tukey transformation should be adopted (TRUE) or if a Box-Cox transformation should be adopted (FALSE).
show.par	Boolean determining if the power transformation used with the data should be displayed in the plot's upper-right corner.
stat	Choice of summary statistic to use when centering the fitted values around 0. The stat parameter is also used for fitting univariate values (i.e. for summarizing groups). stat can be either mean or median.
grey	Grey level to apply to plot elements (0 to 1 with 1 = black).
pch	Point symbol type.
p.col	Color for point symbol.
p.fill	Point fill color passed to bg (Only used for pch ranging from 21-25).
inner	Fraction of values that should be captured by the shaded region. Defaults to inner 90 percent of values.
q	Boolean determining if grey quantile boxes should be plotted.
size	Point size (0-1)
alpha	Point transparency (0 = transparent, 1 = opaque). Only applicable if rgb() is not used to define point colors.
ylim	Define custom y axis limits.
bar	Boolean determining if spread comparison stacked bars should be plotted.

Details

The eda_rfs function generates a residual-fit spread plot for univariate and bivariate data. Input can be a dataframe with one column storing the continuous variable and another column storing the categorical (grouping) variable or, for a bivariate dataset, a model output from an lm(), eda_lm() or eda_rline() function.

The stat argument only applies to univariate data and allows the user to choose the summary statistic to fit to the data (either mean or median). This statistic is also used to recenter the fitted values in the rfs plot.

The q argument, when set to TRUE, will add a shaded region to the residual quantile plot highlighting the mid portion of the data defined by the inner argument (set to 90 percent of the mid values, by default). The range defined by the mid portion of the data is highlighted in the left plot for comparison with the the full range defined by the fitted values.

The bar option, when set to TRUE, adds a narrow stacked barplot that compares the spread covered by the residuals (red bar) with the spread covered by the fitted values (green bar). The residual spread is computed for the portion of the residuals defined by the inner argument. The values outputted in the console are those used in computing the vertical bars. The red bar is the relative spread of the residuals and the green bar is the relative spread of the fitted values. The stacked bar plot can be helpful in quickly gauging the effect the fitted values have in explaining the variability in the data. The longer the green bar relative to the red bar, the greater the grouping variable's (for univariate data) or linear model's (for bivariate data) effect in minimizing the uncertainty in the estimated value.

Value

No values are returned

References

• William S. Cleveland. Visualizing Data. Hobart Press (1993)

• Residual-fit spread article

Examples

# Generate a basic residual-fit spread plot
eda_rfs(mtcars,mpg, cyl)

# Add inner 90% region to residuals (grey boxes in plot)
# Vertical grey box shows matching y-values
eda_rfs(mtcars,mpg, cyl, q = TRUE)

# Change guide to encompass mid 75% of residual values
eda_rfs(mtcars,mpg, cyl, q = TRUE, inner = 0.75)

# Use median instead of the mean to compute group summaries and to
# recenter the fitted values around 0.
eda_rfs(mtcars,mpg, cyl, stat = median)

# Apply power transformation of -1 to mpg. Defaults to box-cox method.
eda_rfs(mtcars,mpg, cyl, p = -1)

# Display a stacked bar plot showing relative importance in spreads
# between fitted values and residuals.
eda_rfs(mtcars,mpg, cyl, bar = TRUE)

# Generate rfs plot for bivariate model output. Model can be generated from
# lm(), eda_lm() or eda_rline()
M1 <- lm(hp ~ mpg, mtcars)
eda_rfs(M1,q =TRUE)

M2 <- eda_lm(mtcars, mpg, hp)
eda_rfs(M2,q =TRUE)

M3 <- eda_rline(mtcars, mpg, hp)
eda_rfs(M3, q =TRUE)

mgimond/tukeyedar documentation built on Feb. 1, 2025, 4:02 a.m.