R help - Aug 2010 - question about bayesian model selection for quantile regression

Hi All:

Recently I am researching my dissertation about the quantile model selection
by bayesian approach. I have the dependent variable(return) and 16
independent variables and I need to select the best variable for each
quantile of return. And the method I used is the bayesian approach, which is
based on calculating the posterior distibution of model identifier. In other
words, I need to obtain the posterior model probabilities of all competing
models (2^16 models) and ranking the models according to these
probabilities. Then inferences can be based on the most probable model .
However, I am not sure which package in R I should use. Could you help me
and which exact packages I need to use? Many many thanks

Best wishes
XIN LI

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>
> Hello:
>
  I try to use function "bms" to select the best model with the
biggest
posterior probability for different quantile. I have one return and 16
variables. However, I can just select for the linear model, how to change
the code for quantile:

>  >bma1=bms(rq(y~.,tau=0.25,data=EQT), burn = 1000, iter = 3000,
mcmc="bd")
> Error in mpparam[[1]] : subscript out of bounds"
>
The code for linear model is sucessful:

> > bma1=bms(EQT, burn = 1000, iter = 3000, mcmc="bd")
> where EQT is my data
>
  > beta.draws.bma(bma1[1:5])

how to use monte carlo to select the best model for quantile? Or which other
function should be used?

>
> Best wishes
> XIN LI
>
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Hi All:
the package "MuMIn" can be used to select the model based on AIC or
AICc.
The code is as follows:

data(Cement)
lm1 <- lm(y ~ ., data = Cement)
dd <- dredge(lm1,rank="AIC")
print(dd)

If I want to select the model by BIC, what code do I need to use? And when
to select the best model based on AIC, what the differences between the
function "dredge" in package"MuMIn" and the function
"stepAIC" in package
"MASS"

Many thanks

best wishes

XIN LI

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