transfer: Glass Transfer, Persistence and Recovery Probabilities

Description Usage Arguments Value Author(s) References Examples

View source: R/tfer-class.R

Description

Simulate the number of glass fragments recovered given the conditions set by the user.

Usage

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transfer(N = 10000, d = 0.5, deffect = 1, lambda = 120, Q = 0.05, l0 =
0.8, u0 = 0.9, lstar0 = 0.1, ustar0 = 0.15, lj = 0.45, uj = 0.7, lstarj
= 0.05, ustarj = 0.1, lR = 0.5, uR = 0.7, t = 1.5, r = 0.5)

Arguments

N

Simulation size

d

The breaker's distance from the window

deffect

Distance effect. deffect = 1 when distance effect exists. Otherwise deffect = 0.

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)

Value

Y

The simulated values of recovered glass fragments

para

Input parameters

Author(s)

James Curran and TingYu Huang

References

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), 15-21.

Examples

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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 user-specified 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)

Example output

     8      9     10     11     12     13     14     15 
0.0354 0.0319 0.0309 0.0280 0.0293 0.0244 0.0250 0.0212 
$parameters
      N       d deffect  lambda       Q      l0      u0  lstar0  ustar0      lj 
1.0e+04 5.0e-01 1.0e+00 1.2e+02 5.0e-02 8.0e-01 9.0e-01 1.0e-01 1.5e-01 4.5e-01 
     uj  lstarj  ustarj      lR      uR       t       r 
7.0e-01 5.0e-02 1.0e-01 5.0e-01 7.0e-01 1.5e+00 5.0e-01 

$values
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    3.00    9.00   13.09   19.00   98.00 

$probability
     0      1      2      3      4      5      6      7      8      9     10 
0.1015 0.0680 0.0627 0.0538 0.0467 0.0449 0.0399 0.0350 0.0354 0.0319 0.0309 
    11     12     13     14     15     16     17     18     19     20     21 
0.0280 0.0293 0.0244 0.0250 0.0212 0.0225 0.0178 0.0188 0.0166 0.0162 0.0151 
    22     23     24     25     26     27     28     29     30     31     32 
0.0148 0.0144 0.0137 0.0115 0.0106 0.0096 0.0099 0.0103 0.0072 0.0088 0.0083 
    33     34     35     36     37     38     39     40     41     42     43 
0.0081 0.0070 0.0063 0.0048 0.0054 0.0046 0.0037 0.0045 0.0034 0.0040 0.0032 
    44     45     46     47     48     49     50     51     52     53     54 
0.0033 0.0029 0.0025 0.0037 0.0029 0.0016 0.0017 0.0016 0.0022 0.0014 0.0014 
    55     56     57     58     59     60     61     62     63     64     65 
0.0010 0.0013 0.0010 0.0015 0.0008 0.0012 0.0009 0.0008 0.0004 0.0007 0.0010 
    66     67     68     69     70     71     72     73     74     75     76 
0.0005 0.0005 0.0003 0.0003 0.0005 0.0002 0.0001 0.0003 0.0002 0.0004 0.0001 
    77     79     80     82     83     84     85     93     98 
0.0001 0.0001 0.0001 0.0002 0.0002 0.0001 0.0001 0.0001 0.0001 

tfer documentation built on May 29, 2017, 11:33 a.m.