[R-sig-ME] Non-linear mixed model
Juan Pablo Edwards Molina
edwardsmolina at gmail.com
Thu Sep 14 14:57:39 CEST 2017
Dear list members,
I´m trying to test the effect of the climate region classification on
the in vitro growth of a sample (n =20) of fungus races. I grew them
in several temperatures (20, 22, 25, 28, 31) that I knew they could
have the maximum growth:
race state Clima1 Kopp Kopp2 temp rep diam
1 TO F Aw B 20 1 4.4
1 TO F Aw B 20 2 4.1
1 TO F Aw B 20 3 4.3
1 TO F Aw B 22 1 4.8
1 TO F Aw B 22 2 4.5
1 TO F Aw B 22 3 4.4
..
The approach that I´m considering is to fitt a non- linear model:
diam ~ thy * exp (thq*(temp-thx)² + thc*(temp-thx)³)
# thx: Optimum temperature
# thy: Diameter at optimum
# thq: Curvature
# thc: Skewness
Since I have particular interest on "thx": How should I include the
effect of my climate classiification variables on that coefficient?
This is my try in nlme:
df <- groupedData(diam~temp|race, data=d, order=FALSE)
n1 <- nlme(diam ~ thy * exp(thq * (temp - thx)^2 + thc * (temp - thx)^3),
fixed = thy + thq + thx + thc ~ 1,
random = thy + thq + thx + thc ~ 1 | race,
start = c(thy = 5.5, thq = -0.08, thx = 25, thc = -0.01),
data = df)
The overall model converged and this is the summary:
======================================================
Nonlinear mixed-effects model fit by maximum likelihood
Model: diam ~ thy * exp(thq * (temp - thx)^2 + thc * (temp - thx)^3)
Data: df
AIC BIC logLik
619.2972 652.1712 -301.6486
Random effects:
Formula: list(thy ~ 1, thx ~ 1)
Level: race
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
thy 0.00002186836 thy
thx 0.00001466761 0
Residual 0.47302438540
Fixed effects: thy + thq + thx + thc ~ 1
Value Std.Error DF t-value p-value
thy 5.456386 0.03598277 427 151.63885 0.0000
thq -0.011081 0.00043084 427 -25.71992 0.0000
thx 25.908119 0.17218070 427 150.47052 0.0000
thc 0.000458 0.00015103 427 3.03271 0.0026
Correlation:
thy thq thx
thq -0.567
thx 0.217 0.289
thc -0.231 -0.192 -0.924
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-3.53487665 -0.64456754 0.06126737 0.67103195 2.17757223
Number of Observations: 450
Number of Groups: 20
=========================================================
Thanks in advance... Any help would be very helpful!
J. Edwards
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