R: Marginal Model Plotting

mmps {car} | R Documentation

## Marginal Model Plotting

### Description

For a regression object, draw a plot of the response on the vertical axis versus a linear combination u of regressors in the mean function on the horizontal axis. Added to the plot are a smooth for the graph, along with a smooth from the plot of the fitted values on u. `mmps` is an alias for `marginalModelPlots`, and `mmp` is an alias for `marginalModelPlot`.

### Usage

    marginalModelPlots(...)

    mmps(model, terms= ~ ., fitted=TRUE, layout=NULL, ask,
            main, groups, key=TRUE, ...)

    marginalModelPlot(...)

    ## S3 method for class 'lm'
    mmp(model, variable, sd = FALSE,
        xlab = deparse(substitute(variable)),
        smoother = loessLine, smoother.args=list(span=2/3),
        key=TRUE, pch, groups=NULL, ...)

    ## Default S3 method:
    mmp(model, variable, sd = FALSE,
        xlab = deparse(substitute(variable)), smoother=loessLine,
        smoother.args, key=TRUE, pch, groups=NULL,
        col.line = palette()[c(4, 2)], col=palette()[1],
        labels, id.method="y",
        id.n=if(id.method[1]=="identify") Inf else 0,
        id.cex=1, id.col=palette()[1], id.location="lr", grid=TRUE, ...)

    ## S3 method for class 'glm'
    mmp(model, variable, sd = FALSE,
        xlab = deparse(substitute(variable)), smoother=gamLine,
        smoother.args=list(k=3), key=TRUE, pch, groups=NULL,
        col.line = palette()[c(4, 2)], col=palette()[1],
        labels, id.method="y",
        id.n=if(id.method[1]=="identify") Inf else 0,
        id.cex=1, id.col=palette()[1], id.location="lr", grid=TRUE, ...)

### Arguments

`model` | A regression object, usually of class either `lm` or `glm`, for which there is a `predict` method defined.
`terms` | A one-sided formula. A marginal model plot will be drawn for each term on the right-side of this formula that is not a factor. The default is `~ .`, which specifies that all the terms in `formula(object)` will be used. If a conditioning argument is given, eg `terms = ~. | a`, then separate colors and smoothers are used for each unique non-missing value of `a`. See examples below.
`fitted` | If the default `TRUE`, then a marginal model plot in the direction of the fitted values or linear predictor of a generalized linear model will be drawn.
`layout` | If set to a value like `c(1, 1)` or `c(4, 3)`, the layout of the graph will have this many rows and columns. If not set, the program will select an appropriate layout. If the number of graphs exceed nine, you must select the layout yourself, or you will get a maximum of nine per page. If `layout=NA`, the function does not set the layout and the user can use the `par` function to control the layout, for example to have plots from two models in the same graphics window.
`ask` | If `TRUE`, ask before clearing the graph window to draw more plots.
`main` | Main title for the array of plots. Use `main=""` to suppress the title; if missing, a title will be supplied.
`...` | Additional arguments passed from `mmps` to `mmp` and then to `plot`. Users should generally use `mmps`, or equivalently `marginalModelPlots`.
`variable` | The quantity to be plotted on the horizontal axis. The default is the predicted values `predict(object)`. It can be any other vector of length equal to the number of observations in the object. Thus the `mmp` function can be used to get a marginal model plot versus any regressor or predictor while the `mmps` function can be used only to get marginal model plots for the first-order regressors in the formula. In particular, terms defined by a spline basis are skipped by `mmps`, but you can use `mmp` to get the plot for the variable used to define the splines.
`sd` | If `TRUE`, display sd smooths. For a binomial regression with all sample sizes equal to one, this argument is ignored as the SD bounds don't make any sense.
`xlab` | label for horizontal axis
`smoother` | the name of the smoother to use, selected from the choices described at `ScatterplotSmoothers`. For linear models and the default method, the default smoother is the function is the function `loessLine`. For generalized linear models the default is `gamLine`, using the `gam` package and using splines.
`smoother.args` | arguments passed to the smoother. For linear models the defaults match the smoother used before September 2012, and may be changed later. See `ScatterplotSmoothers`.
`groups` | The name of a vector that specifies a grouping variable for separate colors/smoothers. This can also be specified as a conditioning argument on the `terms` argument.
`key` | If `TRUE`, include a key at the top of the plot, if `FALSE` omit the key. If grouping is present, the key is only printed for the upper-left plot.
`id.method,labels,id.n,id.cex,id.col,id.location` | Arguments for labeling points. The default `id.n=0` suppresses labeling, and setting this argument greater than zero will include labeling. See `showLabels` for these arguments.
`pch` | plotting character to use if no grouping is present.
`col.line` | colors for data and model smooth, respectively. Using the default palette, these are blue and red.
`col` | color(s) for the plotted points.
`grid` | If TRUE, the default, a light-gray background grid is put on the graph

### Details

`mmp` and `marginalModelPlot` draw one marginal model plot against whatever is specified as the horizontal axis. `mmps` and `marginalModelPlots` draws marginal model plots versus each of the terms in the `terms` argument and versus fitted values. `mmps` skips factors and interactions if they are specified in the `terms` argument. Terms based on polynomials or on splines (or potentially any term that is represented by a matrix of regressors) will be used to form a marginal model plot by returning a linear combination of the terms. For example, if you specify `terms = ~ X1 + poly(X2, 3)` and `poly(X2, 3)` was part of the original model formula, the horizontal axis of the marginal model plot for `X2` will be the value of `predict(model, type="terms")[, "poly(X2, 3)"])`. If the `predict` method for the model you are using doesn't support `type="terms"`, then the polynomial/spline term is skipped. Adding a conditioning variable, e.g., `terms = ~ a + b | c`, will produce marginal model plots for `a` and `b` with different colors and smoothers for each unique non-missing value of `c`.

The smoothers used were changed in September 2012. For linear models, the default smoother is still loess with the same smoothing parameters as were used in the past, but these can be changed with the argument `smoother.args`. For generalized linear models, the default smoother uses `gamLine`, fitting a generalized additive model with the same family, link and weights as the fit of the model. SD smooths are not computed for for generalized linear models.

For generalized linear models the default number of elements in the spline basis is `k=3`; this is done to allow fitting for predictors with just a few support points. If you have many support points you may wish to set `k` to a higher number, or `k=-1` for the default used by `gam`.

### Value

Used for its side effect of producing plots.

### Author(s)

Sanford Weisberg, sandy@umn.edu

### References

Cook, R. D., & Weisberg, S. (1997). Graphics for assessing the adequacy of regression models. Journal of the American Statistical Association, 92(438), 490-499.

Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition. Sage.

Weisberg, S. (2005) Applied Linear Regression, Third Edition, Wiley, Section 8.4.

### See Also

`ScatterplotSmoothers`, `plot`

### Examples

    ## Not run:
    c1 <- lm(infant.mortality ~ gdp, UN)
    mmps(c1)
    c2 <- update(c1, ~ poly(gdp, 4), data=na.omit(UN))
    # plot against predict(c2, type="terms")[, "poly(gdp, 4)"] and
    # and against gdp
    mmps(c2, ~ poly(gdp,4) + gdp)
    # include SD lines
    p1 <- lm(prestige ~ income + education, Prestige)
    mmps(p1, sd=TRUE)
    # condition on type:
    mmps(p1, ~. | type)
    # logisitic regression example
    # smoothers return warning messages.
    # fit a separate smoother and color for each type of occupation.

    m1 <- glm(lfp ~ ., family=binomial, data=Mroz)
    mmps(m1)

    ## End(Not run)

[Package car version 2.1-4 Index]