Simulate the number of glass fragments recovered given the conditions set by the user.
1 2 3 
N 
Simulation size 
d 
The breaker's distance from the window 
deffect 
Distance effect. 
lambda 
The average number of glass fragments transferred to the breaker's clothing. 
Q 
Proportion of high persistence fragments. 
l0 
Lower bound on the percentage of fragments lost in the first hour 
u0 
Upper bound on the percentage of fragments lost in the first hour 
lstar0 
Lower bound on the percentage of high persistence fragments lost in the first hour 
ustar0 
Upper bound on the percentage of high persistence fragments lost in the first hour 
lj 
Lower bound on the percentage of fragments lost in the j'th hour 
uj 
Upper bound on the percentage of fragments lost in the j'th hour 
lstarj 
Lower bound on the percentage of high persistence fragments lost in the j'th hour 
ustarj 
Upper bound on the percentage of high persistence fragments lost in the j'th hour 
lR 
Lower bound on the percentage of fragments expected to be detected in the lab 
uR 
Upper bound on the percentage of fragments expected to be detected in the lab 
t 
Time between commission of crime and apprehension of suspect 
r 
Probability r in ti ~ NegBinom(t, r) 
Y 
The simulated values of recovered glass fragments 
para 
Input parameters 
James Curran and TingYu Huang
Curran, J. M., Hicks, T. N. & Buckleton, J. S. (2000). Forensic interpretation of glass evidence. Boca Raton, FL: CRC Press.
Curran, J. M., Triggs, C. M., Buckleton, J. S., Walsh, K. A. J. & Hicks T. N. (January, 1998). Assessing transfer probabilities in a Bayesian interpretation of forensic glass evidence. Science & Justice, 38(1), 1521.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  library(tfer)
## create a transfer object using default arguments
y = transfer()
## probability table
probs = tprob(y)
## extract the probabilities of recovering 8 to 15
## glass fragments given the userspecified arguments
tprob(y, 8:15)
## produce a summary table for a transfer object
summary(y)
## barplot of probabilities (default)
plot(y, ptype = 0)
plot(y)
## barplot of transfer frequencies
plot(y, ptype = 1)
## histogram
plot(y, ptype = 2)

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