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
<mazatlanmex...@yahoo.com> wrote:
> Consider this dummy dataset.
> My real dataset with over 1000 records has
> scatter large and small values.
> I want to predict for values with NA but I
> get negative predictions. Is this a normal
> behaviour or I am missing a gam argument
> to force the model to predict positive values.
> library(mgcv)
> test <- data.frame(iddate=seq(as.Date("2014-01-01"),
> as.Date("2014-01-12"), by="days"),
> value=c(300,29,22,NA,128,24,15,1,3,30,NA,2))
> test
> str(test)
> mod <- gam(value ~ s(as.numeric(iddate)),data=test)
> # Predict for values with NA's
> test$pred <- with(test,ifelse(is.na(value),predict(mod,test),value))
> test
> [[alternative HTML version deleted]]
>
>
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>
--
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://joshuawiley.com/
Senior Analyst - Elkhart Group Ltd.
http://elkhartgroup.com
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