# Quick overview of plot functions

#### Jacolien van Rij

#### 2025-12-23

The table present the different plot functions in the packages `mgcv` (Wood 2006, 2011) and `itsadug` for visualizing GAMM models.

| Partial effect | Sum of all effects | Sum of “fixed” effects1
--- | --- | --- | ---
surface | `plot.gam()` | `vis.gam()` |
`pvisgam()` | `fvisgam()`
smooth | `plot.gam()` | `plot_smooth()`
group estimates | `plot.gam()2` | `plot_parametric()`
random smooths | `get_random()`, `inspect_random()` |

1: include `rm.ranef=TRUE` to zero all random effects.

2: include `all.terms=TRUE` to visualize parametric terms.

# Examples

    library(itsadug)
    library(mgcv)
    data(simdat)

    ## Not run:
    # Model with random effect and interactions:
    m1 <- bam(Y ~ Group + te(Time, Trial, by=Group)
        + s(Time, Subject, bs='fs', m=1, k=5),
        data=simdat)
    # Simple model with smooth:
    m2 <-  bam(Y ~ Group + s(Time, by=Group)
        + s(Subject, bs='re')
        + s(Subject, Time, bs='re'),
        data=simdat)

Summary model `m1`:

    gamtabs(m1, type='html')

A. parametric coefficients | Estimate | Std. Error | t-value | p-value
(Intercept) | 2.0505 | 0.6881 | 2.9799 | 0.0029
GroupAdults | 3.1204 | 0.9731 | 3.2065 | 0.0013
B. smooth terms | edf | Ref.df | F-value | p-value
te(Time,Trial):GroupChildren | 19.7347 | 21.9398 | 1651.5571 | < 0.0001
te(Time,Trial):GroupAdults | 19.6302 | 21.8781 | 1466.8432 | < 0.0001
s(Time,Subject) | 175.2256 | 178.0000 | 5803.4508 | < 0.0001

Summary model `m2`:

    gamtabs(m2, type='html')

A. parametric coefficients | Estimate | Std. Error | t-value | p-value
(Intercept) | 2.0575 | 2.9497 | 0.6975 | 0.4855
GroupAdults | 3.1264 | 4.1715 | 0.7495 | 0.4536
B. smooth terms | edf | Ref.df | F-value | p-value
s(Time):GroupChildren | 8.6203 | 8.9584 | 1630.0297 | < 0.0001
s(Time):GroupAdults | 8.8372 | 8.9921 | 8798.1199 | < 0.0001
s(Subject) | 33.9912 | 34.0000 | 125335342.8798 | < 0.0001
s(Subject,Time) | 33.9878 | 34.0000 | 125318454.9121 | < 0.0001

## a. Surfaces

Plotting the partial effects of `te(Time,Trial):GroupAdults` and `te(Time,Trial):GroupChildren`.

### pvisgam()

    par(mfrow=c(1,2), cex=1.1)

    pvisgam(m1, view=c("Time", "Trial"), select=1,
            main="Children", zlim=c(-12,12))
    pvisgam(m1, view=c("Time", "Trial"), select=2,
            main="Adults", zlim=c(-12,12))

Notes:

- Plots same data as `plot(m1, select=1)`: partial effects plot, i.e., the plot does not include intercept or any other effects.

- Make sure to set the zlim values the same when comparing surfaces

- Use the argument `cond` to specify the value of other predictors in a more complex interaction. For example, for plotting a modelterm `te(A,B,C)` use something like `pvisgam(model, view=c("A", "B"), select=1, cond=list(C=5))`.

### fvisgam()

Plotting the fitted effects of `te(Time,Trial):GroupAdults` and `te(Time,Trial):GroupChildren`.

    par(mfrow=c(1,2), cex=1.1)

    fvisgam(m1, view=c("Time", "Trial"), cond=list(Group="Children"),
            main="Children", zlim=c(-12,12), rm.ranef=TRUE)
    fvisgam(m1, view=c("Time", "Trial"), cond=list(Group="Adults"),
            main="Adults", zlim=c(-12,12), rm.ranef=TRUE)

Notes:

- Plots the fitted effects, i.e., the plot includes intercept and estimates for the other modelterms.

- Make sure to set the zlim values the same when comparing surfaces

- Use the argument `cond` to specify the value of other predictors in the model.

- The argument `rm.ranef` cancels the contribution of random effects.

- The argument `transform` accepts a function for transforming the fitted values into the original scale.

## b. Smooths

### plot.gam()

Plotting the partial effects of `s(Time):GroupAdults` and `s(Time):GroupChildren`.

    par(mfrow=c(1,2), cex=1.1)

    plot(m2, select=1, shade=TRUE, rug=FALSE, ylim=c(-15,10))
    abline(h=0)
    plot(m2, select=2, shade=TRUE, rug=FALSE, ylim=c(-15,10))
    abline(h=0)

Notes:

- Not possible to combine different smooth terms in a single plot.

Alternatively:

    par(mfrow=c(1,1), cex=1.1)

    # Get model term data:
    st1 <- get_modelterm(m2, select=1)
    st2 <- get_modelterm(m2, select=2)

    # plot model terms:
    emptyPlot(2000, c(-15,10), h=0,
        main='s(Time)',
        xmark = TRUE, ymark = TRUE, las=1)
    plot_error(st1$Time, st1$fit, st1$se.fit, shade=TRUE)
    plot_error(st2$Time, st2$fit, st2$se.fit, shade=TRUE, col='red', lty=4, lwd=2)

    # add legend:
    legend('bottomleft',
      legend=c('Children', 'Adults'),
      fill=c(alpha('black'), alpha('red')),
      bty='n')

### plot_smooth()

Plotting the fitted effects of `te(Time,Trial):GroupAdults` and `te(Time,Trial):GroupChildren` i.e., the plot includes intercept and estimates for the other modelterms.

    par(mfrow=c(1,2), cex=1.1)

    plot_smooth(m1, view="Time", cond=list(Group="Children"),
        rm.ranef=TRUE, ylim=c(-6,10))
    plot_smooth(m1, view="Time", cond=list(Group="Adults"),
        col="red", rug=FALSE, add=TRUE,
        rm.ranef=TRUE)

    # or alternatively:
    plot_smooth(m1, view="Time", plot_all="Group",
        rm.ranef=TRUE)

Notes:

- Use the argument `cond` to specify the value of other predictors in the model.

- The argument `rm.ranef` cancels the contribution of random effects.

- The argument `transform` accepts a function for transforming the fitted values into the original scale.

- The argument `plot_all` plots all levels for the given predictor(s).

## c. Group estimates

### plot.gam()

Plotting the partial effect of grouping predictors such as `Group`:

    par(mfrow=c(1,1), cex=1.1)

    plot.gam(m1, select=4, all.terms=TRUE, rug=FALSE)

Alternatively, use `get_coefs()` to extract the coefficients and plot these:

    coefs <- get_coefs(m1)
    coefs

    par(mfrow=c(1,2), cex=1.1)

    b <- barplot(coefs[,1], beside=TRUE,
                 main="Parametric terms",
                 ylim=c(0,5))
    errorBars(b, coefs[,1], coefs[,2], xpd=TRUE)

    # Note that the effect of Group is a *difference* estimate
    # between intercept (=GroupChildren) and Group Adults

    b2 <- barplot(coefs[1,1], beside=TRUE,
                 main="Estimate for Group",
                 ylim=c(0,5), xlim=c(0.1,2.5))
    mtext(row.names(coefs), at=b, side=1, line=1)
    abline(h=coefs[1,1], lty=2)
    rect(b[2]-.4, coefs[1,1], b[2]+.4, coefs[1,1]+coefs[2,1],
         col='gray')
    errorBars(b, coefs[,1]+c(0,coefs[1,1]), coefs[,2], xpd=TRUE)

    ##             Estimate Std. Error
    ## (Intercept) 2.050539  0.6881195
    ## GroupAdults 3.120351  0.9731479

Notes:

- For large models `get_coefs()` is faster than `summary(model)`.

### plot_parametric()

Plotting the fitted effects of grouping predictors such as `Group`:

    pp <- plot_parametric(m1, pred=list(Group=c("Children", "Adults")) )

    pp

    ## $fv
    ##      Group    Time Trial Subject       fit       CI        rm.ranef
    ## 1 Children 989.899     0     a01  4.182214 1.705797 s(Time,Subject)
    ## 2   Adults 989.899     0     a01 11.216176 1.789386 s(Time,Subject)

## Random effects

### get_random()

For extracting the random effects coefficients (random adjustments of intercept and slopes):

    par(mfrow=c(1,2), cex=1.1)
    plot(m2, select=3)
    plot(m2, select=4)

Or alternatively:

    pp <- get_random(m2)

    emptyPlot(range(pp[[1]]), range(pp[[2]]), h=0,v=0,
         xlab='Random intercepts', ylab='Random slopes',
         main='Correlation')

    text(pp[[1]], pp[[2]], labels=names(pp[[1]]),
         col='steelblue', xpd=TRUE)

### inspect_random()

For plotting and extracting the random smooths:

    par(mfrow=c(1,2), cex=1.1)

    inspect_random(m1, select=3, main='s(Time, Subject)')

    children <- unique(simdat[simdat$Group=="Children", "Subject"])
    adults   <- unique(simdat[simdat$Group=="Adults", "Subject"])

    inspect_random(m1, select=3, main='Averages',
          fun=mean,
          cond=list(Subject=children))
    inspect_random(m1, select=3,
          fun=mean, cond=list(Subject=adults),
          add=TRUE, col='red', lty=5)

    # add legend:
    legend('bottomleft',
      legend=c('Children', 'Adults'),
      col=c('black', 'red'), lty=c(1,5),
      bty='n')

# References

Wood, Simon N. 2006. Generalized Additive Models: An Introduction with R. Chapman; Hall/CRC.
Wood, Simon N. 2011. “Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models.” Journal of the Royal Statistical Society (B) 73 (1): 3–36.