Description Usage Arguments Value Note References See Also Examples
This method for the generic function hist
is mainly
useful to plot the histogram of objects of class "expert"
. If
plot = FALSE
, the resulting object of class "histogram"
is returned for compatibility with hist.default
, but
does not contain much information not already in x
.
1 2 3 4 5 6 
x 
an object of class 
freq 
logical; if 
probability 
an alias for 
density 
the density of shading lines, in lines per inch.
The default value of 
angle 
the slope of shading lines, given as an angle in degrees (counterclockwise). 
col 
a colour to be used to fill the bars.
The default of 
border 
the color of the border around the bars. The default is to use the standard foreground color. 
main, xlab, ylab 
these arguments to 
xlim, ylim 
the range of x and y values with sensible defaults.
Note that 
axes 
logical. If 
plot 
logical. If 
labels 
logical or character. Additionally draw labels on top
of bars, if not 
... 
further graphical parameters passed to

An object of class "histogram"
which is a list with components:
breaks 
the r + 1 group boundaries. 
counts 
r integers; the frequency within each group. 
density 
the relative frequencies within each group
n[j]/n, where n[j] = 
intensities 
same as 
mids 
the r group midpoints. 
xname 
a character string with the actual 
equidist 
logical, indicating if the distances between

The resulting value does not depend on the values of
the arguments freq
(or probability
)
or plot
. This is intentionally different from S.
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (1998), Loss Models, From Data to Decisions, Wiley.
hist
and hist.default
for histograms of
individual data and fancy examples.
1 2 3 4 5 6 7 8 9 10 11 12 13  x < list(E1 < list(A1 < c(0.14, 0.22, 0.28),
A2 < c(130000, 150000, 200000),
X < c(350000, 400000, 525000)),
E2 < list(A1 < c(0.2, 0.3, 0.4),
A2 < c(165000, 205000, 250000),
X < c(550000, 600000, 650000)),
E3 < list(A1 < c(0.2, 0.4, 0.52),
A2 < c(200000, 400000, 500000),
X < c(625000, 700000, 800000)))
probs < c(0.1, 0.5, 0.9)
true.seed < c(0.27, 210000)
fit < expert(x, "cooke", probs, true.seed, 0.03)
hist(fit)

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