plotxs {condvis}  R Documentation 
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 
model 
A fitted model object, or a list of such objects. 
model.colour 
Colours for fitted models. If 
model.lwd 
Line weight for fitted models. If 
model.lty 
Line style for fitted models. If 
model.name 
Character labels for models, for legend. 
yhat 
Fitted values for the observations in 
mar 
Margins for plot. 
col 
Colours for observed data. Should be of length 
weights 
Similarity weights for observed data. Should be of length

view3d 
Logical; if 
theta3d, phi3d 
Angles defining the viewing direction. 
xs.grid 
The grid of values defining the part of the section to visualise. Calculated if not provided. 
prednew 
The 
conf 
Logical; if 
probs 
Logical; if 
pch 
Plot symbols for observed data 
residuals 
Logical; if 
main 
Character title for plot, default is

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. 120. <URL:http://dx.doi.org/10.18637/jss.v081.i05>.
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))