movStats: movStats
In Hmisc: Harrell Miscellaneous

movStats

R Documentation

movStats

Description

Moving Estimates Using Overlapping Windows

Usage

movStats(
  formula,
  stat = NULL,
  discrete = FALSE,
  space = c("n", "x"),
  eps = if (space == "n") 15,
  varyeps = FALSE,
  xinc = NULL,
  xlim = NULL,
  times = NULL,
  tunits = "year",
  msmooth = c("smoothed", "raw", "both"),
  tsmooth = c("supsmu", "lowess"),
  bass = 8,
  span = 1/4,
  maxdim = 6,
  penalty = NULL,
  trans = function(x) x,
  itrans = function(x) x,
  loess = FALSE,
  ols = FALSE,
  qreg = FALSE,
  lrm = FALSE,
  orm = FALSE,
  hare = FALSE,
  family = "logistic",
  k = 5,
  tau = (1:3)/4,
  melt = FALSE,
  data = environment(formula),
  pr = c("none", "kable", "plain", "margin")
)

Arguments

`formula`	a formula with the analysis variable on the left and the x-variable on the right, following by optional stratification variables
`stat`	function of one argument that returns a named list of computed values. Defaults to computing mean and quartiles + N except when y is binary in which case it computes moving proportions. If y has two columns the default statistics are Kaplan-Meier estimates of cumulative incidence at a vector of `times`.
`discrete`	set to `TRUE` if x-axis variable is discrete and no intervals should be created for windows
`space`	defines whether intervals used fixed width or fixed sample size
`eps`	tolerance for window (half width of window). For `space='x'` is in data units, otherwise is the sample size for half the window, not counting the middle target point.
`varyeps`	applies to `space='n'` and causes a smaller `eps` to be used in strata with fewer than “ observations so as to arrive at three x points
`xinc`	increment in x to evaluate stats, default is xlim range/100 for `space='x'`. For `space='n'` `xinc` defaults to m observations, where m = max(n/200, 1).
`xlim`	2-vector of limits to evaluate if `space='x'` (default is 10th to 10th)
`times`	vector of times for evaluating one minus Kaplan-Meier estimates
`tunits`	time units when `times` is given
`msmooth`	set to `'smoothed'` or `'both'` to compute `lowess`-smooth moving estimates. `msmooth='both'` will display both. `'raw'` will display only the moving statistics. `msmooth='smoothed'` (the default) will display only he smoothed moving estimates.
`tsmooth`	defaults to the super-smoother `'supsmu'` for after-moving smoothing. Use `tsmooth='lowess'` to instead use `lowess`.
`bass`	the `supsmu` `bass` parameter used to smooth the moving statistics if `tsmooth='supsmu'`. The default of 8 represents quite heavy smoothing.
`span`	the `lowess` `span` used to smooth the moving statistics
`maxdim`	passed to `hare`, default is 6
`penalty`	passed to `hare`, default is to use BIC. Specify 2 to use AIC.
`trans`	transformation to apply to x
`itrans`	inverse transformation
`loess`	set to TRUE to also compute loess estimates
`ols`	set to TRUE to include rcspline estimate of mean using ols
`qreg`	set to TRUE to include quantile regression estimates w rcspline
`lrm`	set to TRUE to include logistic regression estimates w rcspline
`orm`	set to TRUE to include ordinal logistic regression estimates w rcspline (mean + quantiles in `tau`)
`hare`	set to TRUE to include hazard regression estimtes of incidence at `times`, using the `polspline` package
`family`	link function for ordinal regression (see `rms::orm`)
`k`	number of knots to use for ols and/or qreg rcspline
`tau`	quantile numbers to estimate with quantile regression
`melt`	set to TRUE to melt data table and derive Type and Statistic
`data`	data.table or data.frame, default is calling frame
`pr`	defaults to no printing of window information. Use `pr='plain'` to print in the ordinary way, `⁠pr='kable⁠` to convert the object to `knitr::kable` and print, or `pr='margin'` to convert to `kable` and place in the `Quarto` right margin. For the latter two `results='asis'` must be in the chunk header.

Details

Function to compute moving averages and other statistics as a function of a continuous variable, possibly stratified by other variables. Estimates are made by creating overlapping moving windows and computing the statistics defined in the stat function for each window. The default method, space='n' creates varying-width intervals each having a sample size of 2*eps +1, and the smooth estimates are made every xinc observations. Outer intervals are not symmetric in sample size (but the mean x in those intervals will reflect that) unless eps=10, as outer intervals are centered at observations 10 and n - 10 + 1. The mean x-variable within each windows is taken to represent that window. If trans and itrans are given, x means are computed on the trans(x) scale and then itrans'd. For space='x', by default estimates are made on to the 10th smallest to the 10th largest observed values of the x variable to avoid extrapolation and to help getting the moving statistics off on an adequate start for the left tail. Also by default the moving estimates are smoothed using supsmu. When melt=TRUE you can feed the result into ggplot like this: ⁠ggplot(w, aes(x=age, y=crea, col=Type)) + geom_line() +⁠ facet_wrap(~ Statistic)

See here for several examples.

Value

a data table, with attribute infon which is a data frame with rows corresponding to strata and columns N, Wmean, Wmin, Wmax if stat computed N. These summarize the number of observations used in the windows. If varyeps=TRUE there is an additional column eps with the computed per-stratum eps. When space='n' and xinc is not given, the computed xinc also appears as a column. An additional attribute info is a kable object ready for printing to describe the window characteristics.