Displaying 4 results from an estimated 4 matches for "mod2a".
Did you mean:
mod2
2005 Feb 01
3
polynomials REML and ML in nlme
...n’t have access to a copy
of Bates and Pinheiro. It is probably quite obvious but the answer might
be of general interest.
If I fit a fixed effect with an added quadratic term and then do it as
an orthogonal polynomial using maximum likelihood I get the expected
result- they have the same logLik.
mod2a<-lme(wthole~nplants+I(nplants^2),data=d3,random=~1|field/subplot,m
ethod="ML")
mod2b<-lme(wthole~poly(nplants,2),data=d3,random=~1|field/subplot,method
="ML")
> anova(mod2a,mod2b)
Model df AIC BIC logLik
mod2a 1 6 6698.231 6723.869 -3343.116
mo...
2008 Jul 03
1
lines() warning message
...0.2106
1971,3394,760,0.22098
1972,1697,1354,0.39461
1973,25159,1308,0.19696
[truncated]
with program
#Ricker Curve
mod2=nls(Recruit~(Spawner*exp((delta+echo*Spawner)+(foxtrot*Mtempcv))), data=box48,
start=list(delta=4, echo=0, foxtrot=-7), trace=TRUE)
plot(Recruit~Spawner, data=box48, pch=19)
mod2a=seq(369, 3000)
lines(mod2a, predict(mod2, list(Spawner=mod2a)), col="red", lty=2)
R has no problem finding a solution to the nls() model (only 7 iterations are needed), but when I try to plot the line on the requested plot, I get the warning message "Warning message:
In (delta + ech...
2010 Nov 15
1
Executing Command on Multiple R Objects
Hello Everyone -
I want to print a number of results from lme function objects out to a txt
file. How could I do this more efficiently than what you see here:
out2 <- capture.output(summary(mod2a))
out3 <- capture.output(summary(mod3))
out4 <- capture.output(summary(mod5))
out5 <- capture.output(summary(mod6))
out6 <- capture.output(summary(mod7))
cat(out2,file="out.txt",sep="\n",append=TRUE)
cat(out3,file="out.txt",sep="\n",append=TRUE)
c...
2010 Nov 18
1
lme Random Effects and Covariates
...fects. I've grouped the data on
subject (grid) but want to use lme to build the model without subject as a
RE then add it and do anova between the 2 models. This is the result I get
and it appears it's adding Random Effects.
tmp.dat4 <- groupedData(Trials ~ 1 | grid, data = tmp.dat4)
mod2a <- lme(Trials ~ factor(group_id) + reversal, data = tmp.dat4,
na.action = na.omit, method = "REML")
> summary(mod2a)
Linear mixed-effects model fit by REML
Data: tmp.dat4
AIC BIC logLik
4544.054 4587.718 -2262.027
Random effects:
Formula: ~factor(group_id) + re...