# Plot smoothed estimates 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 `cutGn` function to form intervals of x containing a minimum 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`. ```r 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 minimum 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 `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. ## Returns `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`, `cutGn` ## Examples ```r 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, then a proportion (mean of binary y) xyplot(survival.time ~ age | sex, panel=panel.plsmo, type='l', method='intervals', mobs=25, ifun=median) ybinary <- ifelse(runif(length(sex)) < 0.5, 1, 0) xyplot(ybinary ~ age, groups=sex, panel=panel.plsmo, type='l', method='intervals', mobs=75, ifun=mean, xlim=c(0, 120)) #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 ```

plsmo function