density plot of simulated exponential distributed data

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

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

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