plotxs: Visualise a section in data space
In condvis: Conditional Visualization for Statistical Models

Description Usage Arguments Value References See Also Examples

Visualise a section in data space, showing fitted models where they intersect the section, and nearby observations. The weights for observations can be calculated with similarityweight. This function is mainly for use in ceplot and condtour.

plotxs(xs, y, xc.cond, model, model.colour = NULL, model.lwd = NULL,
  model.lty = NULL, model.name = NULL, yhat = NULL, mar = NULL,
  col = "black", weights = NULL, view3d = FALSE, theta3d = 45,
  phi3d = 20, xs.grid = NULL, prednew = NULL, conf = FALSE,
  probs = FALSE, pch = 1, residuals = FALSE, main = NULL, xlim = NULL,
  ylim = NULL)

`xs`	A dataframe with one or two columns.
`y`	A dataframe with one column.
`xc.cond`	A dataframe with a single row, with all columns required for passing to `predict` methods of models in `model`.
`model`	A fitted model object, or a list of such objects.
`model.colour`	Colours for fitted models. If `model` is a list, this should be of same length as `model`.
`model.lwd`	Line weight for fitted models. If `model` is a list, this should be of same length as `model`.
`model.lty`	Line style for fitted models. If `model` is a list, this should be of same length as `model`.
`model.name`	Character labels for models, for legend.
`yhat`	Fitted values for the observations in `y`. Calculated if needed and not provided. Only used if showing residuals, or `xs` has two columns.
`mar`	Margins for plot.
`col`	Colours for observed data. Should be of length `nrow(xs)`.
`weights`	Similarity weights for observed data. Should be of length `nrow(xs)`. Usually calculated with `similarityweight`.
`view3d`	Logical; if `TRUE` plots a three-dimensional regression surface if possible.
`theta3d, phi3d`	Angles defining the viewing direction. `theta3d` gives the azimuthal direction and `phi3d` the colatitude. See `persp`.
`xs.grid`	The grid of values defining the part of the section to visualise. Calculated if not provided.
`prednew`	The `y` values where the models in `model` intersect the section. Useful when providing `theta3d`, `phi3d`, or `weights`, where the predict methods have been called elsewhere.
`conf`	Logical; if `TRUE` plots confidence bounds (or equivalent) for models which provide this.
`probs`	Logical; if `TRUE`, shows predicted class probabilities instead of just predicted classes. Only available if `xs` contains two numeric predictors and the model's predict method provides this.
`pch`	Plot symbols for observed data
`residuals`	Logical; if `TRUE`, plots a residual versus predictor plot instead of the usual scale of raw response.
`main`	Character title for plot, default is `"Conditional expectation"`.
`xlim`	Graphical parameter passed to plotting functions.
`ylim`	Graphical parameter passed to plotting functions.

A list containing relevant information for updating the plot.

O'Connell M, Hurley CB and Domijan K (2017). “Conditional Visualization for Statistical Models: An Introduction to the condvis Package in R.”Journal of Statistical Software, 81(5), pp. 1-20. <URL:http://dx.doi.org/10.18637/jss.v081.i05>.

plotxc, ceplot, condtour

data(mtcars)
model <- lm(mpg ~ ., data = mtcars)
plotxs(xs = mtcars[, "wt", drop = FALSE], y = mtcars[, "mpg", drop = FALSE],
  xc.cond = mtcars[1, ], model = list(model))