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
lmer random effect model matrix question
2 messages · Mark Lyman, Douglas Bates
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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