Plot the graphic of a Binomial distribution.

Variables to customize:

prob <- 0.1# probabilityn <- 10 # number of independent experiments barthickness <- 20 # thickness of the graphical barscolour <- rgb (1,0,0,0.5) # colour of the bars and line

Plot the graphic of a Poisson distribution.

Variables to customize:

lambda <- 3 # lambda parameter (mean and variance)
n <- 20 # number of independent experiments
ndecimals <- 5 # number of decimal places
barthickness <- 15 # thickness of the graphical bars
colour <- rgb (1,0,0,0.5) # colour of the bars and line

Plot the graphic of an Uniform distribution and draw a set of values from the distribution.

Variables to customize:

xmin <- 0.0 # minimum value
xmax <- 1000.0 # maximum value
ndecimals <- 2 # number of decimals
nvalores <- 10 # number of values to generate by Monte Carlo simulation
discretizacao <- 20 # number of points for graphical aproximation
colour <- rgb (1,0,0,0.5) # colour of the lines

Plot the graphic of a Triangular distribution and draw a set of values from the distribution.

Variables to customize:

xmin <- 14.5 # minimum value
xmax <- 32.5 # maximum value
xmoda <- 30.0 # most likely value
ndecimals <- 2 # number of decimals
nvalores <- 100 # number of values to generate by Monte Carlo simulation
discretizacao <- 200 # number of points for graphical aproximation
colour <- rgb(1,0,0,0.5) # colour of the lines

Plot the graphic of a Normal distribution and draw a set of values from the distribution.

Variables to customize:

media <- 5.0 # mean of the normal distribution
desvp <- 2.0 # standard deviation of the normal distribution
ndecimals <- 2 # number of decimal places
nvalores <- 10000 # number of values to generate by Monte Carlo simulation
discretizacao <- 200 # number of points for graphical aproximation
colour <- rgb (1,0,0,0.5) # colour of the lines

Plot the graphic of a Lognormal distribution and draw a set of values from the distribution.

Variables to customize:

option <- 1 # can be (1) mean and standard deviation are of the normal distribution, or (2) mean and standard deviation are of the lognormal values
media <- 5.0 # mean of the normal or lognormal distribution
desvp <- 2.0 # standard deviation of the normal or lognormal distribution
ndecimals <- 2 # number of decimal places
nvalores <- 10000 # number of values to generate by Monte Carlo simulation
discretizacao <- 200 # number of points for graphical aproximation
colour <- rgb (1,0,0,0.5) # colour of the lines

Compare several Normal distributions, with different means and variances, in the same graph.

Variables to customize:

media <- c(2,2,2,2,2) # mean of several normal distributions to compare
desvp <- c(1,2,3,4,5) # standard deviation of several normal distributions to compare
discretizacao <- 100 # number of points for graphical aproximation
colours <- c("red", "green", "blue", "yellow", "magenta")# list of colours of the several normal distributions to draw

Compare several Lognormal distributions, with different means and variances, in the same graph.

Variables to customize:

media <- c(2,2,2,2,2) # mean of several lognormal distributions to compare
desvp <- c(1,2,3,4,5) # standard deviation of several lognormal distributions to compare
discretizacao <- 100 # number of points for graphical aproximation
colours <- c("red", "green", "blue", "yellow", "magenta") # list of colours of the several normal distributions to draw

Plot the graphic of a Power distribution.

Variables to customize:

xmin <- 1 # minimum value of the distribution
xmax <- 25 # maximum value of the distribution
alfa <- 2 # power value of the distribution
nclasses <- 25 # number of classes of the histogram
discretizacao <- 1000 # number of points for graphical aproximation
colour <- rgb (1,0,0,0.5) # colour of the bars and lines

Plot the empirical distribution of a quantitative variable (simple histogram and cumulative histogram) and add the normal or lognormal distribution curve with the same mean and variance of the data.

Variables to customize:

varlistindex <- 5 # index of the GD frame quantitative variable
varunit <- "(ppm)" # units of the selected variable
nclasses <- 15 # number of classes of the histogram
fitmodel <- TRUE #fit a model (TRUE) or nor (FALSE)
modeltype <- "lognormal" # type of model, normal or lognormal only
maxY <- 1.0 # maximum value for Y axis in the left graphic
discretizacao <- 40 # number of points for graphical aproximation
colourbar <- rgb(0,0,1,0.5) # colour of the histogram bars 
colourline <- rgb(1,0,0,0.5) # colour of the histogram lines

Plot the empirical distribution of a qualitative variable (simple histogram and pseudo-cumulative histogram).

Variables to customize:

varlistindex <- 4 # index of the GTD frame variable
sortbyfreq <- TRUE # (TRUE) rank histogram by frequency in decreasing order or (FALSE) present histogram by alfabetic order
colourbar <- rgb(1,0,0,0.5) # colour of the histogram bars
colourline <- rgb(1,0,0,0.5)# colour of the histogram lines

Use the Monte Carlo simulation to build a composite distribution mixing several normal distributions with different means and variances.

Variables to customize:

nsims <- 500 # number of simulations (values to generate)
probs <- c(0.25,0.25,0.5) # proportions of each normal distribution, values must sum one.
media <- c(12,15,18) # means of several normal distributions to mix
desviop <- c(1,1,1) # standard variations of several normal distributions to mix
nclasses <- 25 # number of classes in the mixure histogram
valoravalia <- 15 # evaluate the probability of being below this value 
colour <- rgb(0,0,1,0.5) # colour of the histogram bars and lines

Use the Monte Carlo simulation to build a composite distribution mixing several uniform distributions with different intervals (means and variances). The composite distribution is based on a simplified formula of Benefits B = PV x Q – CE x Q – CF

Variables to customize:

nsims <- 500 # number of simulations (values to generate)
Q <- 10000 # value for parameter 
minPV <- 4 # minimum value for PV variable uniformly distributed
maxPV <- 6 # maximum value for PV variable uniformly distributed
minCE <- 1.5 # minimum value for CE variable uniformly distributed
maxCE <- 3.5 # maximum value for CE variable uniformly distributed
minCF <- 13000 # minimum value for CF variable uniformly distributed
maxCF <- 15000 # maximum value for CF variable uniformly distributed
valoravalia <- 15 # evaluate the probability of being below this value
avalianclasses <- 10 # number of classes in the mixure 
histogramcolour <- rgb (0,0,1,0.5) # colour of the histogram bars and lines