Hi:
Try this (and note the use of vectorization rather than a loop):
rate <- 3
dta <- -log(1 - runif(1000))/rate
hist(dta, nclass = 30, probability = TRUE)
x <- c(0.001, seq(0, 3, by = 0.01))
lines(x, dexp(x, rate = 3))
This is the difference in timings between the vectorized and iterative
methods of generating the samples:
> system.time(replicate(1000, -log(1 - runif(1000))/rate))
user system elapsed
0.10 0.00 0.09
> system.time(replicate(1000, { dt <- numeric(1000)
+ i <- 1
+ for(i in 1:1000){
+ r <- runif(1)
+ dt[i] <- log(1-r)/(-rate)
+ i <- i+1
+ } }))
user system elapsed
9.35 0.00 9.40
Vectorization is usually your friend in R, and it pays to use it when
available. All of the d*, p*, q* and r* functions, where * denotes the
suffix for a distribution, are vectorized, as are most of the
functions in base R. A happy by-product is that it also makes for more
easily readable code.
HTH,
Dennis
On Tue, Apr 26, 2011 at 3:19 PM, Juanjuan Chai <ch...@umail.iu.edu> wrote:
> Hi all,
>
> I tried to plot the density curve using the data from simulation. I am sure
> that the data should be exponentially distributed, but the plot of density
> curve always starts from (0,0) which is not the case for exponential
> distribution. Is there any way around this, to keep the curve dropping at 0?
>
> Thanks.
>
> The following are the codes I tested:
>
> data <- vector()
> rate <- 3
> i <- 1
> for(i in 1:1000){
> r <- runif(1)
> data[i] <- log(1-r)/(-rate)
> i <- i+1
> }
> plot(density(data))
>
> -JJ
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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