[R] R - lme
Divaker
divaker at gmail.com
Mon Nov 12 03:53:31 CET 2007
Dear R gurus,
I am trying to work out the problem given in Nested design - Montgomery - Design of Experiments p.561
I have attached a pdf of the data as well the anova table. It is a mixed model with Supplier as fixed effect and batches within the supplier as random effects.
I am able to work out the error stratums as below using aov. Which agrees perfectly with the book example
process=read.table("purity.csv", sep=",", header=TRUE)
proc1=aov(Purity~Supplier+Error(Supplier/Batch), process)
#Results in
print(summary(proc1))
#Error: Supplier -corresponds to fixed effect of Supplier
# Df Sum Sq Mean Sq
#Supplier 2 15.0556 7.5278
#Error: Supplier:Batch -corresponds to random effect of Batch in Supplier
# Df Sum Sq Mean Sq F value Pr(>F)
#Residuals 9 69.917 7.769
#Error: Within --corresponds to error within replications
# Df Sum Sq Mean Sq F value Pr(>F)
#Residuals 24 63.333 2.639
But am not able to get results from the lme model as below. (The model also seems to be wrong with lot of NaNs produced)
How do I frame the model such that I can get only ~1 | Batch %in% Supplier
Or something else is wrong
Please tell me where I have gone wrong
library(nlme)
proclme=lme(Purity~Supplier,random = ~1|Supplier/Batch,process)
summary(proclme)
VarCorr(proclme)
Linear mixed-effects model fit by REML
Data: process
AIC BIC logLik
154.8440 163.823 -71.42198
Random effects:
Formula: ~1 | Supplier
(Intercept)
StdDev: 1.250514
Formula: ~1 | Batch %in% Supplier
(Intercept) Residual
StdDev: 1.307622 1.624466
Fixed effects: Purity ~ Supplier
Value Std.Error DF t-value p-value
(Intercept) -0.4166667 1.486998 24 -0.2802067 0.7817
SupplierB2 0.7500000 2.102932 0 0.3566449 NaN
SupplierB3 1.5833333 2.102932 0 0.7529169 NaN
Correlation:
(Intr) SpplB2
SupplierB2 -0.707
SupplierB3 -0.707 0.500
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-1.47513431 -0.75010146 0.08125895 0.70609383 1.87201306
Number of Observations: 36
Number of Groups:
Supplier Batch %in% Supplier
3 12
Warning message:
In pt(q, df, lower.tail, log.p) : NaNs produced
> VarCorr(proclme)
Variance StdDev
Supplier = pdLogChol(1)
(Intercept) 1.563785 1.250514
Batch = pdLogChol(1)
(Intercept) 1.709877 1.307622
Residual 2.638889 1.624466
> intervals(proclme)
Error in intervals.lme(proclme) :
Cannot get confidence intervals on var-cov components: Non-positive definite approximate variance-covariance
In addition: Warning message:
In qt(p, df, lower.tail, log.p) : NaNs produced
Divaker
Dr. C. Divaker Durairaj, ME, Ph.D
Professor, Farm Machinery
Agricultural Machinery Research Centre
Tamil Nadu Agricultural University
Coimbatore 641003, India
Ph: 91-422-6611204
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