| plot_prediction_simple | R Documentation |
Take two vectors corresponding to assemblage performances modelled by component clustering model, or assemblage performances predicted by cross-validation, and reference, observed assemblage performances, then plot modelled assemblage performances versus observed assemblage performances.
plot_prediction_simple(Fx, Fy,
assMotif = rep(1, length(Fx)),
xylab = c("Observed performance",
"Modelled performance"),
xylim = range(Fx),
figs = rep(fsymbols()[1], length(Fx)),
cols = rep(fcolours()[1], length(Fx)),
nbcl = 1,
main = "",
opt.mean = "amean",
pvalue = fpvalue())
Fx, Fy |
two numeric vector of |
assMotif |
an integer vector of |
xylab |
a vector of two strings.
The strings are the labels of |
xylim |
a vector of two numerics.
The numerics are extrem values of |
figs, cols |
two integer vectors of |
nbcl |
an integer.
The integer indicates the number of clusters of components.
The theoretical number |
main |
a string. The string is used as graph title. |
opt.mean |
a character equals to |
pvalue |
a probability,
used as threshold
in the variance analysis. Then |
All options are default values. If all options are omitted,
the function plot Fy vs Fx, in red circle,
with labels of x-axis xlab = "Observed performances" and
y-axis ylab = "Simulated performances".
The two dashed blue lines are mean(Fy) and mean(Fx).
Nothing. It is a procedure.