plot( density( rnorm(n=20)), xlim=c(-5,5), ylim=c(0,1))
for( i in 2:50) lines( density( rnorm(n=20)))
emma hartnett wrote:
I want to overlay 50 denisty plots on a single plot.
For each plot there are 10,000 data points and i want
the empirical density of the data. I have not been
able to
Two alternatives to the zero inflated Poisson (ZIP) model are mentioned
in Jung, Jhun, and Lee (Biometrics, vol 61, no 2, June 2005, p626):
Although the ZIP model is more general than the standard Poisson, count
data with many zeros are often more dispersed than the ZIP model. In
this case,
Joseph J. Gazaille wrote:
Hi!
I'm doing Monte Carlo analyses of the distribution
of the t-statistics of the parameters of models evaluated
with the lm( ) function.
Is there an easy way to recover the t-statistics
(similarly to using coef to recover the coefficients)?
Thanks,
joseph
[EMAIL PROTECTED] wrote:
I have been lurking in this list a while and searching in the archives to
find out how one learns to write fast R code. One solution seems to be to
write part of the code not in R but in C. However after finding a benchmark
article
# Why does expressing one function
require(ctest)
t.test
# return only
function (x, ...)
UseMethod(t.test)
environment: namespace:ctest
# but expressing another function
shapiro.test
# returns more complete code?
function (x)
{
DNAME - deparse(substitute(x))
x -
Wolfgang Zocher wrote:
Hi,
using par() a window is opened which is too large for my monitor. Is there any
chance to change the size of this window?
Thanks,
Wolfgang
__
[EMAIL PROTECTED] mailing list
# another vote for 107
n - 1:500
x - 8
m - 11
totaldrawn - 78
MLE - floor(m * totaldrawn / x)
likelihood - choose(m,x)*choose(n-m,totaldrawn-x)/choose(n,totaldrawn)
plot(n, likelihood)
abline(v=MLE)
[EMAIL PROTECTED] wrote:
I'm not sure I understand your notation:
(1) We recently conducted an
