I would create two 0/1 variables for sor level 1 and sor level 2 and
use those as in
> mark$sor1 <- ifelse(mark$sor == 1, 1, 0)
> mark$sor2 <- ifelse(mark$sor == 2, 1, 0)
> (fm1 <- lmer(y ~ sor + (0+sor1|lot) + (0+sor2|lot) + (1|wafer:lot),
mark))
Linear mixed-effects model fit by REML
Formula: y ~ sor + (0 + sor1 | lot) + (0 + sor2 | lot) + (1 | wafer:lot)
Data: mark
AIC BIC logLik MLdeviance REMLdeviance
455.7631 469.4231 -221.8816 453.5174 443.7631
Random effects:
Groups Name Variance Std.Dev.
wafer:lot (Intercept) 35.866 5.9888
lot sor2 222.709 14.9234
lot sor1 17.076 4.1323
Residual 12.569 3.5453
# of obs: 72, groups: wafer:lot, 24; lot, 8; lot, 8
Fixed effects:
Estimate Std. Error DF t value Pr(>|t|)
(Intercept) 1995.1111 2.7581 70 723.3703 <2e-16 ***
sor2 10.0833 8.1622 70 1.2354 0.2208
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1
On 9/29/05, Mark Lyman <mlyman at byu.edu> wrote:> I have one fixed effect, sor, with two levels. I have eight lots and
> three wafers from each lot. I have included the data below.
>
> I would like to fit a mixed model that estimates a covariance parameter
> for wafer, which is nested in lot, and two covariance parameters for
> lot, one for each level of sor. The following command fits the model
> that I want, except for it estimates the correlation between the two
> covariance parameters for lot. Is there anyway to make R not estimate
> this correlation? Thank you.
>
> lmer(y~sor+(sor-1|lot)+(1|wafer:lot),wafer)
>
> For those familiar with proc mixed the following SAS code fits the model
> that I want:
>
> proc mixed scoring=4;
> class sor lot wafer site;
> model y= sor/ddfm=satterth;
> random lot(sor)/group=sor;
> random wafer(lot);
> run;
>
> sor lot wafer site y
> 1 1 1 1 1 2006
> 2 1 1 1 2 1999
> 3 1 1 1 3 2007
> 4 1 1 2 1 1980
> 5 1 1 2 2 1988
> 6 1 1 2 3 1982
> 7 1 1 3 1 2000
> 8 1 1 3 2 1998
> 9 1 1 3 3 2007
> 10 1 2 1 1 1991
> 11 1 2 1 2 1990
> 12 1 2 1 3 1988
> 13 1 2 2 1 1987
> 14 1 2 2 2 1989
> 15 1 2 2 3 1988
> 16 1 2 3 1 1985
> 17 1 2 3 2 1983
> 18 1 2 3 3 1989
> 19 1 3 1 1 2000
> 20 1 3 1 2 2004
> 21 1 3 1 3 2004
> 22 1 3 2 1 2001
> 23 1 3 2 2 1996
> 24 1 3 2 3 2004
> 25 1 3 3 1 1999
> 26 1 3 3 2 2000
> 27 1 3 3 3 2002
> 28 1 4 1 1 1997
> 29 1 4 1 2 1994
> 30 1 4 1 3 1996
> 31 1 4 2 1 1996
> 32 1 4 2 2 2000
> 33 1 4 2 3 2002
> 34 1 4 3 1 1987
> 35 1 4 3 2 1990
> 36 1 4 3 3 1995
> 37 2 5 1 1 2013
> 38 2 5 1 2 2004
> 39 2 5 1 3 2009
> 40 2 5 2 1 2023
> 41 2 5 2 2 2018
> 42 2 5 2 3 2010
> 43 2 5 3 1 2020
> 44 2 5 3 2 2023
> 45 2 5 3 3 2015
> 46 2 6 1 1 2032
> 47 2 6 1 2 2036
> 48 2 6 1 3 2030
> 49 2 6 2 1 2018
> 50 2 6 2 2 2022
> 51 2 6 2 3 2026
> 52 2 6 3 1 2009
> 53 2 6 3 2 2010
> 54 2 6 3 3 2011
> 55 2 7 1 1 1984
> 56 2 7 1 2 1993
> 57 2 7 1 3 1993
> 58 2 7 2 1 1992
> 59 2 7 2 2 1992
> 60 2 7 2 3 1990
> 61 2 7 3 1 1996
> 62 2 7 3 2 1993
> 63 2 7 3 3 1987
> 64 2 8 1 1 1996
> 65 2 8 1 2 1989
> 66 2 8 1 3 1996
> 67 2 8 2 1 1997
> 68 2 8 2 2 1993
> 69 2 8 2 3 1996
> 70 2 8 3 1 1990
> 71 2 8 3 2 1989
> 72 2 8 3 3 1992
>
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