plsmo {Hmisc} | R Documentation

## Plot smoothed estimates

### Description

Plot smoothed estimates of x vs. y, handling missing data for lowess or supsmu, and adding axis labels. Optionally suppresses plotting extrapolated estimates. An optional `group` variable can be specified to compute and plot the smooth curves by levels of `group`. When `group` is present, the `datadensity` option will draw tick marks showing the location of the raw `x`-values, separately for each curve. `plsmo` has an option to plot connected points for raw data, with no smoothing. The non-panel version of `plsmo` allows `y` to be a matrix, for which smoothing is done separately over its columns. If both `group` and multi-column `y` are used, the number of curves plotted is the product of the number of groups and the number of `y` columns.

`method='intervals'` is often used when y is binary, as it may be tricky to specify a reasonable smoothing parameter to `lowess` or `supsmu` in this case. The `'intervals'` method uses the `cut2` function to form intervals of x containing a target of `mobs` observations. For each interval the `ifun` function summarizes y, with the default being the mean (proportions for binary y). The results are plotted as step functions, with vertical discontinuities drawn with a saturation of 0.15 of the original color. A plus sign is drawn at the mean x within each interval. For this approach, the default x-range is the entire raw data range, and `trim` and `evaluate` are ignored. For `panel.plsmo` it is best to specify `type='l'` when using `'intervals'`.

`panel.plsmo` is a `panel` function for `trellis` for the `xyplot` function that uses `plsmo` and its options to draw one or more nonparametric function estimates on each panel. This has advantages over using `xyplot` with `panel.xyplot` and `panel.loess`: (1) by default it will invoke `labcurve` to label the curves where they are most separated, (2) the `datadensity` option will put rug plots on each curve (instead of a single rug plot at the bottom of the graph), and (3) when `panel.plsmo` invokes `plsmo` it can use the "super smoother" (`supsmu` function) instead of `lowess`, or pass `method='intervals'`. `panel.plsmo` senses when a `group` variable is specified to `xyplot` so that it can invoke `panel.superpose` instead of `panel.xyplot`. Using `panel.plsmo` through `trellis` has some advantages over calling `plsmo` directly in that conditioning variables are allowed and `trellis` uses nicer fonts etc.

When a `group` variable was used, `panel.plsmo` creates a function `Key` in the session frame that the user can invoke to draw a key for individual data point symbols used for the `group`s. By default, the key is positioned at the upper right corner of the graph. If `Key(locator(1))` is specified, the key will appear so that its upper left corner is at the coordinates of the mouse click.

For `ggplot2` graphics the counterparts are `stat_plsmo` and `histSpikeg`.

### Usage

    plsmo(x, y, method=c("lowess","supsmu","raw","intervals"), xlab, ylab,
          add=FALSE, lty=1 : lc, col=par("col"), lwd=par("lwd"),
          iter=if(length(unique(y))>2) 3 else 0, bass=0, f=2/3, mobs=30, trim,
          fun, ifun=mean, group, prefix, xlim, ylim,
          label.curves=TRUE, datadensity=FALSE, scat1d.opts=NULL,
          lines.=TRUE, subset=TRUE,
          grid=FALSE, evaluate=NULL, ...)

    #To use panel function:
    #xyplot(formula=y ~ x | conditioningvars, groups,
    #       panel=panel.plsmo, type='b',
    #       label.curves=TRUE,
    #       lwd = superpose.line$lwd,
    #       lty = superpose.line$lty,
    #       pch = superpose.symbol$pch,
    #       cex = superpose.symbol$cex,
    #       font = superpose.symbol$font,
    #       col = NULL, scat1d.opts=NULL, \dots)

### Arguments

`x` | vector of x-values, NAs allowed
`y` | vector or matrix of y-values, NAs allowed
`method` | `"lowess"` (the default), `"supsmu"`, `"raw"` to not smooth at all, or `"intervals"` to use intervals (see above)
`xlab` | x-axis label iff add=F. Defaults of label(x) or argument name.
`ylab` | y-axis label, like xlab.
`add` | Set to T to call lines instead of plot. Assumes axes already labeled.
`lty` | line type, default=1,2,3,..., corresponding to columns of `y` and `group` combinations
`col` | color for each curve, corresponding to `group`. Default is current `par("col")`.
`lwd` | vector of line widths for the curves, corresponding to `group`. Default is current `par("lwd")`. `lwd` can also be specified as an element of `label.curves` if `label.curves` is a list.
`iter` | iter parameter if `method="lowess"`, default=0 if `y` is binary, and 3 otherwise.
`bass` | bass parameter if `method="supsmu"`, default=0.
`f` | passed to the `lowess` function, for `method="lowess"`
`mobs` | for `method='intervals'`, the target number of observations per interval
`trim` | only plots smoothed estimates between trim and 1-trim quantiles of x. Default is to use 10th smallest to 10th largest x in the group if the number of observations in the group exceeds 200 (0 otherwise). Specify trim=0 to plot over entire range.
`fun` | after computing the smoothed estimates, if `fun` is given the y-values are transformed by `fun()`
`ifun` | a summary statistic function to apply to the `y`-variable for `method='intervals'`. Default is `mean`.
`group` | a variable, either a `factor` vector or one that will be converted to `factor` by `plsmo`, that is used to stratify the data so that separate smooths may be computed
`prefix` | a character string to appear in group of group labels. The presence of `prefix` ensures that `labcurve` will be called even when `add=TRUE`.
`xlim` | a vector of 2 x-axis limits. Default is observed range.
`ylim` | a vector of 2 y-axis limits. Default is observed range.
`label.curves` | set to `FALSE` to prevent `labcurve` from being called to label multiple curves corresponding to `group`s. Set to a list to pass options to `labcurve`. `lty` and `col` are passed to `labcurve` automatically.
`datadensity` | set to `TRUE` to draw tick marks on each curve, using x-coordinates of the raw data `x` values. This is done using `scat1d`.
`scat1d.opts` | a list of options to hand to `scat1d`
`lines.` | set to `FALSE` to suppress smoothed curves from being drawn. This can make sense if `datadensity=TRUE`.
`subset` | a logical or integer vector specifying a subset to use for processing, with respect too all variables being analyzed
`grid` | set to `TRUE` if the R `grid` package drew the current plot
`evaluate` | number of points to keep from smoother. If specified, an equally-spaced grid of `evaluate` `x` values will be obtained from the smoother using linear interpolation. This will keep from plotting an enormous number of points if the dataset contains a very large number of unique `x` values.
`...` | optional arguments that are passed to `scat1d`, or optional parameters to pass to `plsmo` from `panel.plsmo`. See optional arguments for `plsmo` above.
`type` | set to `p` to have `panel.plsmo` plot points (and not call `plsmo`), `l` to call `plsmo` and not plot points, or use the default `b` to plot both.
`pch,cex,font` | vectors of graphical parameters corresponding to the `group`s (scalars if `group` is absent). By default, the parameters set up by `trellis` will be used.

### Value

`plsmo` returns a list of curves (x and y coordinates) that was passed to `labcurve`

### Side Effects

plots, and `panel.plsmo` creates the `Key` function in the session frame.

### See Also

`lowess`, `supsmu`, `label`, `quantile`, `labcurve`, `scat1d`, `xyplot`, `panel.superpose`, `panel.xyplot`, `stat_plsmo`, `histSpikeg`

### Examples

    set.seed(1)
    x <- 1:100
    y <- x + runif(100, -10, 10)
    plsmo(x, y, "supsmu", xlab="Time of Entry")
    #Use label(y) or "y" for ylab

    plsmo(x, y, add=TRUE, lty=2)
    #Add lowess smooth to existing plot, with different line type

    age <- rnorm(500, 50, 15)
    survival.time <- rexp(500)
    sex <- sample(c('female','male'), 500, TRUE)
    race <- sample(c('black','non-black'), 500, TRUE)
    plsmo(age, survival.time < 1, fun=qlogis, group=sex) # plot logit by sex

    #Bivariate Y
    sbp <- 120 + (age - 50)/10 + rnorm(500, 0, 8) + 5 * (sex == 'male')
    dbp <-  80 + (age - 50)/10 + rnorm(500, 0, 8) - 5 * (sex == 'male')
    Y <- cbind(sbp, dbp)
    plsmo(age, Y)
    plsmo(age, Y, group=sex)

    #Plot points and smooth trend line using trellis
    # (add type='l' to suppress points or type='p' to suppress trend lines)
    require(lattice)
    xyplot(survival.time ~ age, panel=panel.plsmo)

    #Do this for multiple panels
    xyplot(survival.time ~ age | sex, panel=panel.plsmo)

    #Repeat this using equal sample size intervals (n=25 each) summarized by
    #the median
    xyplot(survival.time ~ age | sex, panel=panel.plsmo, type='l',
           method='intervals', mobs=25, ifun=median)

    #Do this for subgroups of points on each panel, show the data
    #density on each curve, and draw a key at the default location
    xyplot(survival.time ~ age | sex, groups=race, panel=panel.plsmo,
           datadensity=TRUE)
    Key()

    #Use wloess.noiter to do a fast weighted smooth
    plot(x, y)
    lines(wtd.loess.noiter(x, y))
    lines(wtd.loess.noiter(x, y, weights=c(rep(1,50), 100, rep(1,49))), col=2)
    points(51, y[51], pch=18)   # show overly weighted point
    #Try to duplicate this smooth by replicating 51st observation 100 times
    lines(wtd.loess.noiter(c(x,rep(x[51],99)),c(y,rep(y[51],99)),
          type='ordered all'), col=3)
    #Note: These two don't agree exactly

[Package Hmisc version 3.17-4 Index]