[R] Right censored data, abundant in zeros for regression analysis.
cadeb at usgs.gov
Mon Dec 28 17:30:56 CET 2015
Tom: One possibility might be to use the censored quantile regression
implementation (crq) in the quantreg package (accommodates left or right
censoring) across a range of quantiles (e.g., 0.05 to 0.95) but where
interest is likely to be focused on estimates for quantiles greater than
the quantiles associated with the mass of zeros.
Brian S. Cade, PhD
U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8818
email: cadeb at usgs.gov <brian_cade at usgs.gov>
tel: 970 226-9326
On Thu, Dec 24, 2015 at 6:41 AM, REES T. (706713) <
t.rees.706713 at swansea.ac.uk> wrote:
> Hi there,
> Firstly forgive me if this seem obvious, if there is existing literature
> on this i can't find it.
> I am looking at conditioning to stimuli and there in the time taken to
> perform a certain task.
> The IV for this data is Conditioning periods ranging from 1-34 periods and
> the DV is the time taken for the behavioral response to occur 0-300s.
> I am aware that this could simply be looked at through a simple linear
> regression, however due to the nature of conditioning there is an abundance
> of zeros in the data.
> On top of this the response time data is right censored (i believe), in
> that they were given a five minute period to respond after this five minute
> period (300 seconds) the conditioning period was terminated, so no more
> data was recorded.
> Attached is the data (in .csv format) for time spent out, 0 indicated no
> time out and 300 indicated all time out during the 5 minutes.
> I have considered looking at zero-inflated censored regressions and others
> similar analysis but I cannot find an analysis that suits the data I have
> and actually works.
> So what is the best analysis method to deal with this data?
> Admittedly i could be completely missing the target, if that's the case
> please feel free to say so. Any help with the route that I should go down
> here would be much appreciated, even if it is blindingly obvious.
> Tom Rees
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