plot_smooth: Visualization of smooths.
In itsadug: Interpreting Time Series and Autocorrelated Data Using GAMMs

plot_smooth

R Documentation

Visualization of smooths.

Description

Plots a smooth from a gam or bam model based on predictions. In contrast with the default plot.gam, this function plots the summed effects and optionally removes the random effects.

Usage

plot_smooth(
  x,
  view = NULL,
  cond = list(),
  plot_all = NULL,
  rm.ranef = TRUE,
  n.grid = 30,
  rug = NULL,
  add = FALSE,
  se = 1.96,
  sim.ci = FALSE,
  shade = TRUE,
  eegAxis = FALSE,
  col = NULL,
  lwd = NULL,
  lty = NULL,
  print.summary = getOption("itsadug_print"),
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  xlim = NULL,
  ylim = NULL,
  h0 = 0,
  v0 = NULL,
  transform = NULL,
  transform.view = NULL,
  legend_plot_all = NULL,
  hide.label = FALSE,
  ...
)

Arguments

`x`	A gam object, produced by `gam` or `bam`.
`view`	Text string containing the name of the smooth to be displayed. Note that variables coerced to factors in the model formula won't work as view variables.
`cond`	A named list of the values to use for the other predictor terms (not in view). Used for choosing between smooths that share the same view predictors.
`plot_all`	A vector with a name / names of model predictors, for which all levels should be plotted.
`rm.ranef`	Logical: whether or not to remove random effects. Default is TRUE.
`n.grid`	The number of grid nodes in each direction used for calculating the plotted surface.
`rug`	Logical: when TRUE then the covariate to which the plot applies is displayed as a rug plot at the foot of each plot. By default set to NULL, which sets rug to TRUE when the dataset size is <= 10000 and FALSE otherwise. Setting to FALSE will speed up plotting for large datasets.
`add`	Logical: whether or not to add the lines to an existing plot, or start a new plot (default).
`se`	If less than or equal to zero then only the predicted surface is plotted, but if greater than zero, then the predicted values plus confidence intervals are plotted. The value of `se` will be multiplied with the standard error (i.e., 1.96 results in 95%CI and 2.58). Default is set to 1.96 (95%CI).
`sim.ci`	Logical: Using simultaneous confidence intervals or not (default set to FALSE). The implementation of simultaneous CIs follows Gavin Simpson's blog of December 15, 2016: https://fromthebottomoftheheap.net/2016/12/15/simultaneous-interval-revisited/. This interval is calculated from simulations based. Please specify a seed (e.g., `set.seed(123)`) for reproducable results. Note: in contrast with Gavin Simpson's code, here the Bayesian posterior covariance matrix of the parameters is uncertainty corrected (`unconditional=TRUE`) to reflect the uncertainty on the estimation of smoothness parameters.
`shade`	Logical: Set to TRUE to produce shaded regions as confidence bands for smooths (not avaliable for parametric terms, which are plotted using termplot).
`eegAxis`	Logical: whether or not to reverse the y-axis, plotting the negative amplitudes upwards as traditionally is done in EEG research. If eeg.axes is TRUE, labels for x- and y-axis are provided, when not provided by the user. Default value is FALSE.
`col`	The colors for the lines and the error bars of the plot.
`lwd`	The line width for the lines of the plot.
`lty`	The line type for the lines of the plot.
`print.summary`	Logical: whether or not to print summary. Default set to the print info messages option (see `infoMessages`).
`main`	Changing the main title for the plot, see also title.
`xlab`	Changing the label for the x axis, defaults to a description of x.
`ylab`	Changing the label for the y axis, defaults to a description of y.
`xlim`	the x limits of the plot.
`ylim`	the y limits of the plot.
`h0`	A vector indicating where to add solid horizontal lines for reference. By default no values provided.
`v0`	A vector indicating where to add dotted vertical lines for reference. By default no values provided.
`transform`	Function for transforming the fitted values. Default is NULL.
`transform.view`	Function for transforming the values on the x-axis. Defaults to NULL (no transformation).
`legend_plot_all`	Legend location. This could be a keyword from the list 'bottomright', 'bottom', 'bottomleft', 'left', 'topleft', 'top', 'topright', 'right' and 'center', or a list with `x` and `y` coordinate (e.g., `list(x=0,y=0)`).
`hide.label`	Logical: whether or not to hide the label (i.e., 'fitted values'). Default is FALSE.
`...`	other options to pass on to lines and plot, see `par`

Notes

This function plots the summed effects, including intercept and other predictors. For plotting partial effects, see the function plot.gam, or see the examples with get_modelterm for more flexibility (e.g., plotting using the lattice package or ggplots).

Author(s)

Jacolien van Rij and Martijn Wieling.

Examples

data(simdat)

## Not run: 
# Model with random effect and interactions:
m1 <- bam(Y ~ te(Time, Trial)+s(Time, Subject, bs='fs', m=1),
    data=simdat, discrete=TRUE)

# Default plot produces only surface of Time x Trial:
plot(m1, select=1)
# Only the Time component:
plot_smooth(m1, view='Time')
# Note the summary that is printed.

# without random effects:
plot_smooth(m1, view='Time', rm.ranef=TRUE)

# Plot summed effects:
dev.new(width=8, height=4) # use x11(,8,4) on Linux
par(mfrow=c(1,2))
fvisgam(m1, view=c('Time', 'Trial'), 
    plot.type='contour', color='topo', main='interaction',
    rm.ranef=TRUE)
arrows(x0=0, x1=2200, y0=-5, y1=-5, col='red', 
    code=2, length=.1, lwd=2, xpd=TRUE)
plot_smooth(m1, view='Time', cond=list(Trial=-5),
    main='Trial=-5', rm.ranef=TRUE)


# Model with random effect and interactions:
m2 <- bam(Y ~ Group + s(Time, by=Group)
    +s(Time, Subject, bs='fs', m=1),
    data=simdat, discrete=TRUE)

# Plot all levels of a predictor:
plot_smooth(m2, view='Time', plot_all='Group',
    rm.ranef=TRUE)
# It also possible to combine predictors in plot_all.
# Note: this is not a meaningfull plot, because Subjects
# fall in only one group. Just for illustration purposes!
plot_smooth(m2, view='Time', plot_all=c('Group', 'Subject'))
# Clearly visible difference in confidence interval, because  
# a01 does not occur in Group 'Children':
# (Note that this plot generates warning)
plot_smooth(m2, view='Time', plot_all=c('Group', 'Subject'), cond=list(Subject='a01'))

# Using sim.ci: simultaneous CI instead of pointwise CI
dev.new(width=8, height=4) # use x11(,8,4) on Linux
par(mfrow=c(1,2))
plot_smooth(m2, view='Time', plot_all='Group', rm.ranef=TRUE)
plot_smooth(m2, view='Time', rm.ranef=TRUE, plot_all='Group', sim.ci=TRUE, 
    add=TRUE, shade=FALSE, xpd=TRUE)
plot_smooth(m2, view='Time', rm.ranef=TRUE, sim.ci=TRUE, col='red')



# Using transform
# Plot log-transformed dependent predictor on original scale:
plot_smooth(m1, view='Time', rm.ranef=TRUE, transform=exp)

# Notes on transform.view: 
# This will generate an error, because x-values <= 0 will result in NaN:
plot_smooth(m1, view='Time', rm.ranef=TRUE, transform.view=log)
# adjusting the x-axis helps:
plot_smooth(m1, view='Time', rm.ranef=TRUE, transform.view=log,
   xlim=c(1,2000))


## End(Not run)

# and for a quick overview of plotfunctions:
vignette('overview', package='itsadug')

itsadug documentation built on June 17, 2022, 5:05 p.m.