plotblock.Rd
Function to plot effects for model terms including factor, or group variables for random effects.
plotblock(x, residuals = FALSE, range = c(0.3, 0.3), col.residuals = "black", col.lines = "black", c.select = NULL, fill.select = NULL , col.polygons = NULL, data = NULL, shift = NULL, trans = NULL, labels = NULL, ...)
x  Either a 

residuals  If set to 
range  Numeric vector, specifying the left and right bound of the block. 
col.residuals  The color of the partial residuals. 
col.lines  Vector of maximum length of columns of 
c.select  Integer vector of maximum length of columns of 
fill.select  Integer vector, select pairwise the columns of the resulting data matrix
that should form one polygon with a certain background color specified in argument 
col.polygons  Specify the background color for the upper and lower confidence bands, e.g.

data  If 
shift  Numeric constant to be added to the smooth before plotting. 
trans  Function to be applied to the smooth before plotting, e.g., to transform the plot to the response scale. 
labels  Character, labels for the factor levels. 
…  Graphical parameters, please see the details. 
Function plotblock
draws for every factor or group the effect as a "block" in one graphic,
i.e., similar to boxplots, estimated fitted effects, e.g., containing quantiles of MCMC samples,
are drawn as one block, where the upper lines represent upper quantiles, the
middle line the mean or median, and lower lines lower quantiles, also see the examples. The
following graphical parameters may be supplied additionally:
cex
: Specify the size of partial residuals,
lty
: The line type for each column that is plotted, e.g. lty = c(1, 2)
,
lwd
: The line width for each column that is plotted, e.g. lwd = c(1, 2)
,
poly.lty
: The line type to be used for the polygons,
poly.lwd
: The line width to be used for the polygons,
density
angle
, border
: See polygon
,
…
: Other graphical parameters, see function plot
.
## Generate some data. set.seed(111) n < 500 ## Regressors. d < data.frame(fac = factor(rep(1:10, n/10))) ## Response. d$y < with(d, 1.5 + rnorm(10, sd = 0.6)[fac] + rnorm(n, sd = 0.6))# NOT RUN { ## Estimate model. b < bamlss(y ~ s(fac,bs="re"), data = d) summary(b) ## Plot random effects. plot(b) ## Extract fitted values. f < fitted(b, model = "mu", term = "fac") f < cbind(d["fac"], f) ## Now use plotblock. plotblock(f) ## Variations. plotblock(f, fill.select = c(0, 1, 0, 1), col.poly = "red") plotblock(f, fill.select = c(0, 1, 0, 1), col.poly = "lightgray", lty = c(2, 1, 2), lwd = c(2, 1, 2)) # }d < data.frame(fac = factor(rep(1:10, n/10))) d$y < with(d, c(2.67, 5, 6, 3, 4, 2, 6, 7, 9, 7.5)[fac]) plotblock(y ~ fac, data = d)