Displaying 3 results from an estimated 3 matches for "pedictors".
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predictors
2004 Dec 01
2
step.gam
Dear R-users:
Im trying (using gam package) to develop a stepwise analysis. My gam
object contains five pedictor variables (a,b,c,d,e,f). I define the
step.gam:
step.gam(gamobject, scope=list("a"= ~s(a,4), "b"= ~s(b,4), "c"= ~s(c,4),
"d"= ~s(d,4), "e"= ~s(e,4), "f"= ~s(f,4)))
However, the result shows a formula containing the whole
2011 Dec 16
1
simulation
...git),nAGQ=1))
where modelformula is set up in another part of the program.? Locations are
treated as random and the model is random intercept only.? The program is
set?to run 1000 simulations.
?
I have temperature, five levels of gestational age (GA), birth wieght (BW) and?four?
other categorical pedictors, all binary.? I scaled everything so that all my slopes are in the
range of -5.2 to 1.6 and?variances from .01 to .08.? I have a couple of categories
of GA that have small probabilities (<.10).? I'm using a structured sampling approach
looking at 20, 60, 100, and 140 locations with a total...
2013 Apr 17
1
mgcv: how select significant predictor vars when using gam(...select=TRUE) using automatic optimization
I have 11 possible predictor variables and use them to model quite a few
target variables.
In search for a consistent manner and possibly non-manual manner to identify
the significant predictor vars out of the eleven I thought the option
"select=T" might do.
Example: (here only 4 pedictors)
first is vanilla with "select=F"
> fit1<-gam(target~s(mgs)+s(gsd)+s(mud)+s(ssCmax),family=quasi(link=log),data=wspe1,select=F)
> summary(fit1)
Family: quasi
Link function: log
Formula:
target ~ s(mgs) + s(gsd) + s(mud) + s(ssCmax)
Parametric coefficients:
Estima...