Among various possibilities, you might consider a logistic or probit regression
model with ARMA errors specified via Gaussian copula. This approach is
implemented in the package gcmr ("Gaussian Copula Marginal Regression").
Example: logistic model with covariates S1 and S2 and AR(1,2) errors
fit <- gcmr( Response ~ S1 + S2 + ... , data = your.data, marginal =
binomial.marg, cormat = arma.cormat(1,2), options( nrep=1000 ) )
See help(gcmr) for more details.
Cristiano
---------------------------------------------
Cristiano Varin <cristiano.va...@unive.it>
Department of Environmental Sciences,
Informatics and Statistics
Ca' Foscari University of Venice
San Giobbe, Cannaregio 873, 30121 Venezia, Italy
Tel: +39 0412347439 Fax: +39 0412347444
http://cristianovarin.weebly.com
> Hi,
>
> I have a dichotomous data where some my independent variables are
> categorical, some are continuous and some are binary(0/1)
>
> My dependent is a binary response (Fail/NoFail,0/1) .
>
> The data is some readings collected everyday over a period of time.
>
> The goal is to use this data and see if we can figure out cause of failure
> ,the end response.
>
> Example data format
>
>
> Date, Type,Mileage,S1,S2,S3.... , Response
>
> 03/02/2013,A,32000,1,0,1,......, 1
>
> 03/03/2013,B,32400,0,0,0,.......,0
>
> 03/04/2013,C,45000,0,1,1,......,1
>
>
> Can we do Time series modeling?? Any suggesstions on what type of other
> exploratory analysis can be used to figure out patterns in data ??
>
> Thanks
> shi
>
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