[R] A coding question involving variable assignments in ifelse()

[xpRt.wannabe]

I made a few slight modifications to the original model in an effort
to see the inner workings of the code:

deductible <- 1
coverage.limit <- 2
insurance.threshold <- deductible + coverage.limit

<snip>

set.seed(123)
loss <- abs(rnorm(rpois(1, 5), 1, 3))
n <- length(loss)
accept <- runif(n) < 0.8
payout <- runif(n) < 0.999
sum(ifelse(accept & payout, ifelse(loss > insurance.threshold,
loss - coverage.limit, pmin(loss, deductible)), 0))

[1] 6.188817

<snip>

To tease out the data as well as to see the effect of 'accept &
payout', I did the following:

> loss
[1] 3.401663 4.570620 4.068667 4.718488
> accept
[1]  TRUE FALSE  TRUE  TRUE  # The second loss claim is NOT accepted
by the insurance company.
> payout
[1] TRUE TRUE TRUE TRUE
> accept & payout
[1]  TRUE FALSE  TRUE  TRUE  # The second entry is FALSE because of
the second entry in 'accept.'

Based on the inner ifelse() expression, the original loss numbers
become : 1.401663, 2.570620, 2.068667, 2.718488, respectively (which
is fine and what I wanted).

Because the second entry in 'accept & payout' is FALSE, the second
altered loss number (2.570620) becomes 0, making sum(...) equal
6.188817.  Unfortunately this is _not_ what I want, and I apologize
for not being clear in the first place.  What I want is: for any FALSE
entry, the original loss number is unaltered, as opposed to become 0.
So in the example above, the four numbers that should have been added
are: 1.401663, 4.570620, 2.068667, 2.718488, yielding 10.759438
instead of 6.188817.

Any further suggestions would be greatly appreciated.

On 4/26/07, Duncan Murdoch <murdoch at stats.uwo.ca> wrote:
> On 4/26/2007 2:31 PM, xpRt.wannabe wrote:
> > Just to be sure, is what I have below the right intepretation of your
> > suggestion:
>
> Yes, that's what I suggested.
>
> Duncan Murdoch
>
> > deductible <- 15
> > coverage.limit <- 75
> > insurance.threshold <- deductible + coverage.limit
> >
> > tmpf <- function() {
> > loss <- rlnorm(rpois(1, 3), 2, 5)
> > n <- length(loss)
> > accept <- runif(n) < 0.8
> > payout <- runif(n) < 0.999
> > sum(ifelse(accept & payout, ifelse(loss > insurance.threshold, loss -
> > coverage.limit, pmin(loss, deductible)), 0))
> > }
> > net <- replicate(1000000, tmpf())
> >
> > On 4/26/07, Duncan Murdoch <murdoch at stats.uwo.ca> wrote:
> >> On 4/26/2007 12:48 PM, xpRt.wannabe wrote:
> >> > Dear List,
> >> >
> >> > Below is a simple, standard loss model that takes into account the
> >> > terms of an insurance policy:
> >> >
> >> > deductible <- 15
> >> > coverage.limit <- 75
> >> > insurance.threshold <- deductible + coverage.limit
> >> >
> >> > tmpf <- function() {
> >> > loss <- rlnorm(rpois(1, 3), 2, 5)
> >> > sum(ifelse(loss > insurance.threshold, loss - coverage.limit,
> >> > pmin(loss, deductible)))
> >> > }
> >> > net <- replicate(1000000, tmpf())
> >> >
> >> > Now, I would like to enhance the model by incorporating the following
> >> > two probabilities:
> >> >
> >> > 1. Probability of claim being accepted by the insurance company, say, 0.8
> >> > 2. Probability of payout by the insurance company, say, 0.999
> >> >
> >> > Could anyone suggest how one might do this?
> >>
> >> A general way to generate events with probability p is runif(n) < p.  So
> >> I'd add
> >>
> >> n <- length(loss)
> >> accept <- runif(n) < 0.8
> >> payout <- runif(n) < 0.999
> >>
> >> and then require "accept & payout"  before any payment at all, e.g.
> >>
> >> sum(ifelse(accept & payout, [ your old ifelse expression ], 0))
> >>
> >> There are a lot of implicit independence assumptions here; they may not
> >> be very realistic.
> >>
> >> Duncan Murdoch
> >>
> >
> > ______________________________________________
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>
>