generate variable y to produce excess zero in ZIP analysis

Subject: [R] generate variable y to produce excess zero in 
ZIP analysis

Dear Mr/Mrs

I am Lili Puspita Rahayu, student from magister third level 
of Statistics in Bogor Agriculture University. 
Mr/
Mrs, now I'm analyzing the Zero inflated Poisson (ZIP), which 
is a solution of the Poisson regression where the response 
variable (Y) has zero excess. ZIP now I was doing did not use 
real data, but using simulated data in R. Simulations by 
generating data on variables x1, x2, x3 with each size n = 
100, after which generate data on response variable (Y). 
However, when I generate the variable y, after generating 
variables x1, x2, x3, then the simulation result in the 
variable y that does not have a zero excess. Sometimes just a 
coincidence there are 23%, 25% the proportion of zero on the 
variable Y. This is because I generate variables x1, x2, x3 
with a distribution that has a small parameter values??. I've 
been consulting with my lecturer, and suggested to generate 
variable Y that can control the proportion of zero on ??ZIP 
analysis. I've been trying to make the syntax, but has not 
succeeded.I would like to ask for assistance to R to make the 
syntax to generate simulated Y variables that can control the 
proportion of zeros after generating variables x1, x2, x3 on 
ZIP analysis.Thus, I can examine more deeply to determine how 
much the proportion of zeros on response variable (Y) that 
can be used in the Poisson regression analysis, parametric 
ZIP and ZIP semiparametric.

syntax that I made previously by generating variable y 
without being controlled to produce zero excess in R :

b0=1.5
b1=-log(2)
b2=log(3)
b3=log(4)
n=100
x1<-rnorm(n, mean=5, sd=2)
x2<-runif(n, min=1, max=2)
x3<-rnorm(n, mean=10, sd=15)

y<-seq(1,n)
for(i in 1:n)

+ {
+ m<-exp(b0+b1*x1[i]+b2*x2[i]+b3*x3[i])
+ yp<-rpois(1,m)
+ y[i]<-yp
+ }


I am very
grateful for the assistance of R.
I am looking forward to hearing from you. Thank you very much.

Sincerely yours
Lili Puspita Rahayu 
	[[alternative HTML version deleted]]

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
project.org] On Behalf Of S Ellison
Sent: Wednesday, February 20, 2013 8:03 AM
To: lili puspita rahayu; r-help at R-project.org
Subject: Re: [R] generate variable y to produce excess zero in ZIP
analysis

Subject: [R] generate variable y to produce excess zero in
ZIP analysis

Maybe something like

rzip <-  function(n, lambda, zip=0.0) {
	#zip is the desired proportion of _excess_ zeros
               if(zip>1.0 || zip <0) stop("zip must be in (0,1)")
               n.zip <- ceiling(zip*n)
               n.pois <- n-n.zip
              sample( c( rpois(n.pois, lambda), rep(0,n.zip) ) )
}

#Example
rzip(50, 3.5, zip=0.5)

Having said that, I'd bet there's a package out there that already does
it better...

 S Ellison

Dear Mr/Mrs

I am Lili Puspita Rahayu, student from magister third level
of Statistics in Bogor Agriculture University.
Mr/
Mrs, now I'm analyzing the Zero inflated Poisson (ZIP), which
is a solution of the Poisson regression where the response
variable (Y) has zero excess. ZIP now I was doing did not use
real data, but using simulated data in R. Simulations by
generating data on variables x1, x2, x3 with each size n =
100, after which generate data on response variable (Y).
However, when I generate the variable y, after generating
variables x1, x2, x3, then the simulation result in the
variable y that does not have a zero excess. Sometimes just a
coincidence there are 23%, 25% the proportion of zero on the
variable Y. This is because I generate variables x1, x2, x3
with a distribution that has a small parameter values??. I've
been consulting with my lecturer, and suggested to generate
variable Y that can control the proportion of zero on ??ZIP
analysis. I've been trying to make the syntax, but has not
succeeded.I would like to ask for assistance to R to make the
syntax to generate simulated Y variables that can control the
proportion of zeros after generating variables x1, x2, x3 on
ZIP analysis.Thus, I can examine more deeply to determine how
much the proportion of zeros on response variable (Y) that
can be used in the Poisson regression analysis, parametric
ZIP and ZIP semiparametric.

syntax that I made previously by generating variable y
without being controlled to produce zero excess in R :

b0=1.5
b1=-log(2)
b2=log(3)
b3=log(4)
n=100
x1<-rnorm(n, mean=5, sd=2)
x2<-runif(n, min=1, max=2)
x3<-rnorm(n, mean=10, sd=15)

y<-seq(1,n)
for(i in 1:n)

+ {
+ m<-exp(b0+b1*x1[i]+b2*x2[i]+b3*x3[i])
+ yp<-rpois(1,m)
+ y[i]<-yp
+ }


I am very
grateful for the assistance of R.
I am looking forward to hearing from you. Thank you very much.

Sincerely yours
Lili Puspita Rahayu
	[[alternative HTML version deleted]]

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