[R-sig-ME] gamm4 error with large dataset

[Daniel Hocking]

I am trying to predict daily water temperature from air temperature primarily but ideally would include other factors such as precipitation and landscape characteristics. I have paired air and water temperatures from 600+ sites over a ~10 year period. Some sites have daily temperatures for just a couple months and others for years, and some for a couple months sporadically in different years. I am trying to use a mixed effects gamm so I can include random effects of site and year and smooth over day of the year (dOY). My dataframe is 

and I get the following error when I run this code

system.time(gamm4Full <- gamm4(temp ~ airTemp + airTempLagged1 + airTempLagged2 + prcp + prcpLagged1 + prcpLagged2 + Latitude + Longitude + Forest + Agriculture + BasinElevationM + ReachSlopePCNT + CONUSWetland + SurficialCoarseC + s(dOY) + prcp*airTemp, random = ~(1| site) + (1 | year), data = etS)) 

# Error in crossprod(root.phi %*% Zt) : 
# Cholmod error 'problem too large' at file ../Core/cholmod_aat.c, line 173
# In addition: Warning message:
#   In optwrap(optimizer, devfun, getStart(start, rho$lower, rho$pp),  :
#               convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded


My plan was to try gamm4 and if there was autocorrelation issues to switch to gamm within mgcv. I know bam is designed for large data but I’m not sure how I would code the random effects using bam. I know in general it’s s(dOY, bs = ‘re’) but I’m not sure how to relate this to site and year. Ideally I would have random slopes for airTemp effects for each site because of things like ground water inputs that we don’t measure.

Any advice would be appreciated,
Dan
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Daniel Hocking
Department of Environmental Conservation
Northeast Climate Science Center
University of Massachusetts

http://www.danieljhocking.wordpress.com
dhocking at umass.edu
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