SHELF (version 1.0.1)

plotfit: Plot the fitted density function for one or more experts

Description

Plots the fitted density function for one or more experts. Can also plot a fitted linear pool if more than one expert. If plotting the density function of one expert, or the linear pool only, can also indicated desired lower and upper fitted quantiles.

Usage

plotfit(fit, d = "best", int = FALSE, xl = -Inf, xu = Inf, ql = NA, qu = NA, 
lp = FALSE, ex = NA, sf = 3, ind = TRUE, lpw = 1)

Arguments

fit

The output of a fitdist command.

d

The distribution fitted to each expert's probabilities. Options are "normal", "t", "gamma", "lognormal", "logt","beta", "hist" (for a histogram fit), and "bes

int

Set int = TRUE to use interactive plotting (using the shiny package). If plotting for a single expert, the argument d is ignored, as distributions can be chosen within the display. If plotting for multiple experts, feedback quant

xl

The lower limit for the x-axis. The default is the 0.001 quantile of the fitted distribution (or the 0.001 quantile of a fitted normal distribution, if a histogram fit is chosen).

xu

The upper limit for the x-axis. The default is the 0.999 quantile of the fitted distribution (or the 0.999 quantile of a fitted normal distribution, if a histogram fit is chosen).

ql

A lower quantile to be indicated on the density function plot. Only displayed when plotting the density function for a single expert.

qu

An upper quantile to be indicated on the density function plot. Only displayed when plotting the density function for a single expert.

lp

For multiple experts, set lp=TRUE to plot a linear pool.

ex

If judgements have been elicited from multiple experts, but a density plot for one expert only is required, the expert to be used in the plot.

sf

The number of significant figures to be displayed for the parameter values.

ind

If plotting a linear pool, set ind=FALSE to suppress plotting of the individual density functions.

lpw

A vector of weights to be used in linear pool, if unequal weighting is desired.

See Also

elicit, feedback, fitdist, plinearpool, qlinearpool, roulette

Examples

# Two experts
# Expert 1 states P(X<30)=0.25, P(X<40)=0.5, P(X<50)=0.75
# Expert 2 states P(X<20)=0.25, P(X<25)=0.5, P(X<35)=0.75
# Both experts state 0<X<100. 

v <- matrix(c(30, 40, 50, 20, 25, 35), 3, 2)
p <- c(0.25, 0.5, 0.75)
myfit <- fitdist(vals = v, probs = p, lower = 0, upper = 100)

# Plot both fitted densities, using the best fitted distribution
plotfit(myfit)

# Plot a fitted beta distribution for expert 2, and show 5th and 95th percentiles
plotfit(myfit, d = "beta", ql = 0.05, qu = 0.95, ex = 2)

# Use interactive plotting for for expert 2, and show 5th and 95th percentiles
plotfit(myfit, int = T, ex = 2)

# Plot a linear pool, giving double weight to expert 1
plotfit(myfit,  lp = T, lpw = c(2,1))

# Use interactive plotting, giving double weight to expert 1, if a linear pool is displayed
plotfit(myfit,  int = T, lpw = c(2,1))

# Plot a linear pool, giving double weight to expert 1, 
# show 5th and 95th percentiles, supress plotting of individual distributions, 
# and force use of Beta distributions
plotfit(myfit, d = "beta",  lp = T, lpw = c(2,1), ql = 0.05, qu = 0.95, ind=FALSE )