plot_data  R Documentation 
Plots the data, fitted values, or residuals.
plot_data( model, view, split_by = NULL, cond = NULL, input = "data", rm.ranef = NULL, alpha = NULL, col = NULL, add = FALSE, eegAxis = FALSE, main = NULL, xlab = NULL, ylab = NULL, ylim = NULL, h0 = 0, v0 = NULL, hide.label = FALSE, transform = NULL, transform.view = NULL, print.summary = getOption("itsadug_print"), ... )
model 
A lm or gam object, produced by 
view 
Text string containing the predictor or column in the data to be displayed on the xaxis. Note that variables coerced to factors in the model formula won't work as view variables. 
split_by 
Vector with names of model predictors that determine
the time series in the data, or should be used to split the ACF plot by.
Alternatively, 
cond 
A named list of the values to use for the other predictor terms (not in view) or to select specific trials or time series to plot. 
input 
Text string: 'data' (default) plots the data, 'resid' plots the model residuals, and 'fitted' plots the fitted values. 
rm.ranef 
Logical: whether or not to include the random effects in
the model predictions. Default is TRUE. Relevant for 
alpha 
Value between 0 and 1 indicating the transparency. A value of 0 is completely transparant, whereas a value of 1 is completely untransparant. 
col 
Vector with one color value (i.e., all data points will have the
same color), color values for each grouping condition specified in

add 
Logical: whether or not to add the lines/points to an existing plot, or start a new plot (default). 
eegAxis 
Logical: whether or not to reverse the yaxis, plotting the negative amplitudes upwards as traditionally is done in EEG research. If eeg.axes is TRUE, labels for x and yaxis are provided, when not provided by the user. Default value is FALSE. 
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. 
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. 
hide.label 
Logical: whether or not to hide the label (i.e., 'fitted values'). Default is FALSE. 
transform 
Function for transforming the fitted values. Default is NULL. 
transform.view 
Function for transforming the view values. Default is NULL. 
print.summary 
Logical: whether or not to print a summary.
Default set to the print info messages option
(see 
... 
other options to pass on to lines and plot,
see 
This function plots the fitted effects, including intercept and other predictors.
Jacolien van Rij, idea of Tino Sering
Other Functions for model inspection:
dispersion()
,
fvisgam()
,
gamtabs()
,
inspect_random()
,
plot_parametric()
,
plot_smooth()
,
plot_topo()
,
pvisgam()
data(simdat) ## Not run: # Create grouping predictor for time series: simdat$Event < interaction(simdat$Subject, simdat$Trial) # model without random effects: m1 < bam(Y ~ te(Time, Trial) + s(Subject, bs='re'), data=simdat) # All data points, without clustering: plot_data(m1, view='Time') # All data, clustered by Trial (very small dots): plot_data(m1, view='Time', split_by='Trial', cex=.25) # Add a smooth for each trial: plot_smooth(m1, view='Time', plot_all='Trial', add=TRUE, rm.ranef=TRUE) # Add the model predictions in same color: plot_smooth(m1, view='Time', plot_all='Trial', add=TRUE, rm.ranef=TRUE) # Alternatively, use data to select events: plot_data(m1, view='Time', split_by=list(Event=simdat$Event), type='l') # which is the same as: plot_data(m1, view='Time', split_by=list(Subject=simdat$Subject, Trial=simdat$Trial), type='l') # Only for Trial=0 plot_data(m1, view='Time', split_by=list(Event=simdat$Event), cond=list(Trial=0), type='l') # This is the same: plot_data(m1, view='Time', split_by='Subject', cond=list(Trial=0), type='l') # Add subject smooths: plot_smooth(m1, view='Time', plot_all='Subject', cond=list(Trial=0), add=TRUE) # Change the colors: plot_data(m1, view='Time', split_by='Subject', cond=list(Trial=0), type='l', col='gray', alpha=1) ## End(Not run)
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