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predicted values

4 messages · Felipe Carrillo, Joshua Wiley, Bert Gunter

Joshua Wiley
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Dear Felipe,

That is a normal behavior --- The prediction for that simple model
decreases over time, and ends up negative.  If the outcome cannot take
on negative values, treating it as a continuous gaussian may not be
optimal --- perhaps some transformation, like using a log link so that
the expoentiated values are always positive would be better?
Alternately, if the predictions are going negative, not because the
data is over all, but say there is a quick decrease in values in the
first part of time but later on it slows, but if you have an overly
simplisitic time model, it may just keep decreasing.  Using a smoother
with a higher basis dimensions may help more accurately model the
function over the span of time in your dataset and then not have
predicted values.

I do not think that there would be any straight forward 'force' the
model to be positive only.

Best,

Joshua


On Sat, Feb 1, 2014 at 5:05 PM, Felipe Carrillo
<mazatlanmexico at yahoo.com> wrote:

  
    

      
      
    

  


  


      

    

    
1 day later

    

      
      

        
      

    

    

      
      

        


  

        

      Bert Gunter
    

    

      
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... but do note that doing what you describe (using predicted values
for missings) can mess up inference: it obviously results in
underestimating error variability. If you're not doing inference, then
probably no harm, no foul. If you are, then here's to
irreproducibility! If you want to handle missings and still get
meaningful inference (an oxymoron?), then find someone expert in such
matters to consult. R has several packages devoted to this (but I'm
not the person to advise about them).

Also note that often scientists treat censoring as missing. That's
another booboo. And my humble apology if this is not you.

Finally note that graphics often handles missings sensibly, gracefully
ignoring them. So if graphs are what you seek, maybe you don't need to
worry about it.

And, it should go without saying that given my complete ignorance of
what you're up to, all the above should be taken with the appropriate
dose of salt.

Cheers,
Bert





Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374

"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
H. Gilbert Welch




On Mon, Feb 3, 2014 at 2:23 PM, Felipe Carrillo

        
<mazatlanmexico at yahoo.com> wrote: