Re: [R] a repetition of simulation
Hello,
In addition to my question a few days ago,
Now I have a matrix of the coefficients,
how can I see all the P.Values (Pr(|z|)) of the covariates from the 1000
iterations?
I tried names(log_v) and couldn'n find it.
Thank you,
Sigalit.
On 11/13/07, Julian Burgos [EMAIL PROTECTED] wrote:
Well, the obvious (but perhaps not the most elegant) solution is put
everything in a loop and run it 600 times.
coefficients=matrix(NA,ncol=3,nrow=600)
for (loop in 1:600){
[all your code here]
coefficients[loop,]=coef(log_v)
}
That will give you a matrix with the coefficients of each model run in
each row.
Julian
sigalit mangut-leiba wrote:
I want to repeat the simulation 600 times and to get a vector of 600
coefficients for every covariate: aps and tiss.
Sigalit.
On 11/13/07, Julian Burgos [EMAIL PROTECTED] wrote:
And what is your question?
Julian
sigalit mangut-leiba wrote:
Hello,
I have a simple (?) simulation problem.
I'm doing a simulation with logistic model and I want to reapet it 600
times.
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751) else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
I want to do a repetition of this 600 times (I want to have 600
logistic
models), and see all the coefficients of the covariates aps tiss.
Thanks in advance,
Sigalit.
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Re: [R] a repetition of simulation
Excuse me, but I think your code deserves some comments. Unfortunately,
the history of postings is in reverse order, so I'll address your
first question first:
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
If I'm getting this correctly, you don't need z and x later on?
Then
y - rbinom(500, 1, .6*.95)
should do the trick.
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751) else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
tiss - ifelse(y, rnorm(500, mean=20.731, sd=9.751), rnorm(500, mean=18.531,
sd=9.499))
Likewise for aps.
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
Makes me wonder what you need aps and tiss for. Let's assume for a moment that
they are the coefficients. I do not see the necessity to put everything into a
data frame, so
log_v - glm(y ~ aps+tiss, family=binomial)
should be sufficient and
summary(log_v)$coefficients[,Pr(|z|)]
extracts the p value.
how can I see all the P.Values (Pr(|z|)) of the covariates from the 1000
iterations?
I tried names(log_v) and couldn'n find it.
Try str(summary(log_v)) and you see the whole structure.
--
Johannes H�sing There is something fascinating about science.
One gets such wholesale returns of conjecture
mailto:[EMAIL PROTECTED] from such a trifling investment of fact.
http://derwisch.wikidot.com (Mark Twain, Life on the Mississippi)
__
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Re: [R] a repetition of simulation
summary(log_v)
Julian
sigalit mangut-leiba wrote:
Hello,
In addition to my question a few days ago,
Now I have a matrix of the coefficients,
how can I see all the P.Values (Pr(|z|)) of the covariates from the 1000
iterations?
I tried names(log_v) and couldn'n find it.
Thank you,
Sigalit.
On 11/13/07, Julian Burgos [EMAIL PROTECTED] wrote:
Well, the obvious (but perhaps not the most elegant) solution is put
everything in a loop and run it 600 times.
coefficients=matrix(NA,ncol=3,nrow=600)
for (loop in 1:600){
[all your code here]
coefficients[loop,]=coef(log_v)
}
That will give you a matrix with the coefficients of each model run in
each row.
Julian
sigalit mangut-leiba wrote:
I want to repeat the simulation 600 times and to get a vector of 600
coefficients for every covariate: aps and tiss.
Sigalit.
On 11/13/07, Julian Burgos [EMAIL PROTECTED] wrote:
And what is your question?
Julian
sigalit mangut-leiba wrote:
Hello,
I have a simple (?) simulation problem.
I'm doing a simulation with logistic model and I want to reapet it 600
times.
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751) else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
I want to do a repetition of this 600 times (I want to have 600
logistic
models), and see all the coefficients of the covariates aps tiss.
Thanks in advance,
Sigalit.
[[alternative HTML version deleted]]
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Re: [R] a repetition of simulation
Thank you for all your comments,
Sigalit.
On 11/15/07, Johannes Hüsing [EMAIL PROTECTED] wrote:
Excuse me, but I think your code deserves some comments. Unfortunately,
the history of postings is in reverse order, so I'll address your
first question first:
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
If I'm getting this correctly, you don't need z and x later on?
Then
y - rbinom(500, 1, .6*.95)
should do the trick.
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751)
else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
tiss - ifelse(y, rnorm(500, mean=20.731, sd=9.751), rnorm(500, mean=
18.531, sd=9.499))
Likewise for aps.
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
Makes me wonder what you need aps and tiss for. Let's assume for a moment
that
they are the coefficients. I do not see the necessity to put everything
into a
data frame, so
log_v - glm(y ~ aps+tiss, family=binomial)
should be sufficient and
summary(log_v)$coefficients[,Pr(|z|)]
extracts the p value.
how can I see all the P.Values (Pr(|z|)) of the covariates from the
1000
iterations?
I tried names(log_v) and couldn'n find it.
Try str(summary(log_v)) and you see the whole structure.
--
Johannes Hüsing There is something fascinating about
science.
One gets such wholesale returns of conjecture
mailto:[EMAIL PROTECTED] from such a trifling investment of fact.
http://derwisch.wikidot.com (Mark Twain, Life on the
Mississippi)
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
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and provide commented, minimal, self-contained, reproducible code.
[R] a repetition of simulation
From: sigalit mangut-leiba [EMAIL PROTECTED]
Date: Nov 15, 2007 3:24 PM
Subject: Re: [R] a repetition of simulation
To: r-help [EMAIL PROTECTED]
Hello,
In addition to my question a few days ago,
Now I have a matrix of the coefficients,
how can I see all the P.Values ( Pr(|z|)) of the covariates from the 1000
iterations?
I tried names(log_v) and couldn'n find it.
Thank you,
Sigalit.
On 11/13/07, Julian Burgos [EMAIL PROTECTED] wrote:
Well, the obvious (but perhaps not the most elegant) solution is put
everything in a loop and run it 600 times.
coefficients=matrix(NA,ncol=3,nrow=600)
for (loop in 1:600){
[all your code here]
coefficients[loop,]=coef(log_v)
}
That will give you a matrix with the coefficients of each model run in
each row.
Julian
sigalit mangut-leiba wrote:
I want to repeat the simulation 600 times and to get a vector of 600
coefficients for every covariate: aps and tiss.
Sigalit.
On 11/13/07, Julian Burgos [EMAIL PROTECTED] wrote:
And what is your question?
Julian
sigalit mangut-leiba wrote:
Hello,
I have a simple (?) simulation problem.
I'm doing a simulation with logistic model and I want to reapet it 600
times.
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean= 20.731,sd=9.751) else tiss[i]
-
rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
I want to do a repetition of this 600 times (I want to have 600
logistic
models), and see all the coefficients of the covariates aps tiss.
Thanks in advance,
Sigalit.
[[alternative HTML version deleted]]
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and provide commented, minimal, self-contained, reproducible code.
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] a repetition of simulation
I want to repeat the simulation 600 times and to get a vector of 600
coefficients for every covariate: aps and tiss.
Sigalit.
On 11/13/07, Julian Burgos [EMAIL PROTECTED] wrote:
And what is your question?
Julian
sigalit mangut-leiba wrote:
Hello,
I have a simple (?) simulation problem.
I'm doing a simulation with logistic model and I want to reapet it 600
times.
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751) else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
I want to do a repetition of this 600 times (I want to have 600 logistic
models), and see all the coefficients of the covariates aps tiss.
Thanks in advance,
Sigalit.
[[alternative HTML version deleted]]
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Re: [R] a repetition of simulation
Thank you, I changed that and it's much more efficient.
Sigalit.
On 11/13/07, Phil Spector [EMAIL PROTECTED] wrote:
You can use the replicate function to do your simulation. First,
put the code to do one repetition in a function:
dosim0 = function(n=500){
x - 0
y - 0
z - 0
aps - 0
tiss - 0
for (i in 1:n){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751) else tiss[i]
- rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
glm(y~., family=binomial, data=v)$coef
}
Now you can run
results = t(replicate(1000,dosim0()))
to get a 1000x3 matrix with the coefficients from each simulation.
Before you actually do the simulations, you might want to rewrite
your function so that it's not so inefficient. Functions in R are
vectorized, and you should take advantage of that whenever possible.
Here's a vectorized version of your simulation function:
dosim = function(n=500){
z = rbinom(n,1,.6)
x = rbinom(n,1,.95)
y = z * x
aps = ifelse(y == 1,rnorm(n,mean=13.4,sd=7.09),rnorm(n,mean=12.67, sd=
6.82))
tiss = ifelse(y == 1,rnorm(n,mean=20.731,sd=9.751),rnorm(n,mean=18.531
,sd=9.499))
glm(y~.,family=binomial,data=data.frame(y,aps,tiss))$coef
}
Now, you can run
results = t(replicate(1000,dosim()))
to get a similar result. Notice the difference in time for the
two functions:
system.time(results - t(replicate(1000,dosim0(
user system elapsed
55.123 0.004 55.239
system.time(results - t(replicate(1000,dosim(
user system elapsed
20.297 0.000 20.295
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
[EMAIL PROTECTED]
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Re: [R] a repetition of simulation
Well, the obvious (but perhaps not the most elegant) solution is put
everything in a loop and run it 600 times.
coefficients=matrix(NA,ncol=3,nrow=600)
for (loop in 1:600){
[all your code here]
coefficients[loop,]=coef(log_v)
}
That will give you a matrix with the coefficients of each model run in
each row.
Julian
sigalit mangut-leiba wrote:
I want to repeat the simulation 600 times and to get a vector of 600
coefficients for every covariate: aps and tiss.
Sigalit.
On 11/13/07, Julian Burgos [EMAIL PROTECTED] wrote:
And what is your question?
Julian
sigalit mangut-leiba wrote:
Hello,
I have a simple (?) simulation problem.
I'm doing a simulation with logistic model and I want to reapet it 600
times.
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751) else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
I want to do a repetition of this 600 times (I want to have 600 logistic
models), and see all the coefficients of the covariates aps tiss.
Thanks in advance,
Sigalit.
[[alternative HTML version deleted]]
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http://www.R-project.org/posting-guide.html
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[R] a repetition of simulation
Hello,
I have a simple (?) simulation problem.
I'm doing a simulation with logistic model and I want to reapet it 600
times.
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751) else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
I want to do a repetition of this 600 times (I want to have 600 logistic
models), and see all the coefficients of the covariates aps tiss.
Thanks in advance,
Sigalit.
[[alternative HTML version deleted]]
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Re: [R] a repetition of simulation
Dear Sigalit,
On Nov 12, 2007 2:18 PM, sigalit mangut-leiba [EMAIL PROTECTED] wrote:
Hello,
I have a simple (?) simulation problem.
I'm doing a simulation with logistic model and I want to reapet it 600
times.
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09) else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] - rnorm(1,mean=20.731,sd=9.751) else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
I want to do a repetition of this 600 times (I want to have 600 logistic
models), and see all the coefficients of the covariates aps tiss.
Thanks in advance,
Sigalit.
I won't comment on your general approach to simulation but does this
answer your question?
### Create an empty matrix to hold the results as they
### are created
Results - matrix(NA, ncol = 3, nrow = 600)
for(i in 1:600) {
generate your data
Results[i, ] - coef(glm(y~., family=binomial, data=v))
}
summary(Results)
Stephen
--
Rochester, Minn. USA
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Re: [R] a repetition of simulation
Thank you,
Sigalit.
On 11/13/07, Moshe Olshansky [EMAIL PROTECTED] wrote:
Hi,
Look at names(log_v) in your notation to see what you
really need.
Then you could do something like:
results - list(600)
for (ij in 1:600) {
do what you did
results[[ij]] - log_v
}
So now you have a list of 600 results and can do
anything you need (look at lapply/sapply).
Regards,
Moshe.
--- sigalit mangut-leiba [EMAIL PROTECTED] wrote:
Hello,
I have a simple (?) simulation problem.
I'm doing a simulation with logistic model and I
want to reapet it 600
times.
The simulation looks like this:
z - 0
x - 0
y - 0
aps - 0
tiss - 0
for (i in 1:500){
z[i] - rbinom(1, 1, .6)
x[i] - rbinom(1, 1, .95)
y[i] - z[i]*x[i]
if (y[i]==1) aps[i] - rnorm(1,mean=13.4, sd=7.09)
else aps[i] -
rnorm(1,mean=12.67, sd=6.82)
if (y[i]==1) tiss[i] -
rnorm(1,mean=20.731,sd=9.751) else tiss[i] -
rnorm(1,mean=18.531,sd=9.499)
}
v - data.frame(y, aps, tiss)
log_v - glm(y~., family=binomial, data=v)
summary(log_v)
I want to do a repetition of this 600 times (I want
to have 600 logistic
models), and see all the coefficients of the
covariates aps tiss.
Thanks in advance,
Sigalit.
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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.
