plot_modelfit | R Documentation |

Plots the fitted values and the data for `n`

trials of time series data. For example, plots `n`

trials
of the same participant.

plot_modelfit( x, view, event = NULL, n = 3, random = TRUE, cond = NULL, col = c(alpha(1), "red"), add = FALSE, eegAxis = FALSE, fill = FALSE, main = NULL, xlab = NULL, ylab = NULL, ylim = NULL, h0 = 0, v0 = NULL, transform = NULL, hide.label = FALSE, hide.legend = FALSE, print.summary = getOption("itsadug_print"), ... )

`x` |
A lm or gam object, produced by |

`view` |
Text string containing the predictor or column in the data to be displayed on the x-axis. Note that variables coerced to factors in the model formula won't work as view variables. |

`event` |
column name from the data
that specifies the time series from which |

`n` |
Number of time series to plot. Default is 3. Set to -1 for plotting all time series (which may take a considerable time). |

`random` |
Numeric: if set to TRUE (default), |

`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. |

`col` |
Two value vector specifiying the colors for the data and the modelfit respectively. |

`add` |
Logical: whether or not to add the lines to an existing plot, or start a new plot (default). |

`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. |

`fill` |
Logical: whether or not to fill the area between the data and the fitted values with shading. Default 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. |

`transform` |
Function for transforming the fitted values. Default is NULL. |

`hide.label` |
Logical: whether or not to hide the label (i.e., 'fitted values'). Default is FALSE. |

`hide.legend` |
Logical: whether or not to hide the legend. Default is FALSE. |

`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

Other Model evaluation:
`check_resid()`

,
`diagnostics()`

data(simdat) # Create grouping predictor for time series: simdat$Event <- interaction(simdat$Subject, simdat$Trial) # model without random effects: m1 <- bam(Y ~ te(Time, Trial), data=simdat) plot_modelfit(m1, view='Time', event=simdat$Event) # colorizing residuals: plot_modelfit(m1, view='Time', event=simdat$Event, fill=TRUE) # All trials of one subject: ## Not run: # this produces error: plot_modelfit(m1, view='Time', event=simdat$Event, cond=list(Subject='a01'), n=-1) ## End(Not run) # instead try this: simdat$Subj <- ifelse(simdat$Subject=='a01', TRUE, FALSE) plot_modelfit(m1, view='Time', event=simdat$Event, cond=list(Subject=simdat$Subj), n=-1) ## Not run: # Model with random intercepts for subjects: m2 <- bam(Y ~ te(Time, Trial)+s(Subject, bs='re'), data=simdat) # now selecting a subject works, because it is in the model: plot_modelfit(m2, view='Time', event=simdat$Event, cond=list(Subject='a01'), n=-1, ylim=c(-13,13)) # Model with random effect and interactions: m3 <- bam(Y ~ te(Time, Trial)+s(Time, Subject, bs='fs', m=1), data=simdat) plot_modelfit(m3, view='Time', event=simdat$Event, cond=list(Subject='a01'), n=-1, ylim=c(-13,13)) ## End(Not run)

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