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glmer with/without intercept gave different results
6 messages · Chang, Yu-Mei, ONKELINX, Thierry, Jarrod Hadfield
Dear Ruby, The hypotheses of those models are different. Hence the diference in p-values. Fit1: H0: Capsule 1 = 0 H0: Capsule 2 - Capsule 1 = 0 H0: Control - Capsule 1 = 0 Fit2: H0: Capsule 1 = 0 H0: Capsule 2 = 0 H0: Control = 0 However, the predictions of both model should be the same. Best regards, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey
-----Oorspronkelijk bericht----- Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Chang, Yu-Mei Verzonden: donderdag 18 november 2010 15:22 Aan: r-sig-mixed-models at r-project.org Onderwerp: [R-sig-ME] glmer with/without intercept gave different results Dear all, I have fitted two glmer models (with or without intercept term). I thought the results should be similar if not identical, but they are quite different. I suspect it's related to the random effects. Any suggestions on how to proceed is greatly appreciated. Kind regards, Ruby Chang fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) + (1|Cell),family=binomial(link = "logit")) fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) + (1|Cell),family=binomial(link = "logit"))
table(Cyptoplasmic.vacuolation, Group)
Group
Cyptoplasmic.vacuolation Capsule 1 Capsule 2 Control
0 560 340 1230
1 190 160 20
fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit1)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group + (1 | Villus) + (1 | Cell)
AIC BIC logLik deviance
138.9 168.1 -64.47 128.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 1983.5708 44.5373
Villus (Intercept) 5.9475 2.4387
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.945 9.123 -1.309 0.190
GroupCapsule 2 -1.409 14.343 -0.098 0.922
GroupControl -17.792 33.251 -0.535 0.593
Correlation of Fixed Effects:
(Intr) GrpCp2
GroupCapsl2 -0.636
GroupContrl -0.274 0.175
fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit2)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group - 1 + (1 | Villus) + (1 |
Cell)
AIC BIC logLik deviance
132.9 162.0 -61.43 122.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 5.9933e+03 7.7417e+01
Villus (Intercept) 1.2025e-07 3.4677e-04
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
GroupCapsule 1 -15.08 17.70 -0.852 0.394
GroupCapsule 2 -14.72 19.29 -0.763 0.445
GroupControl -18.23 54.54 -0.334 0.738
Correlation of Fixed Effects:
GrpCp1 GrpCp2
GroupCapsl2 0.000
GroupContrl 0.000 0.000
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Dear Thierry, I understood the hypotheses were different between the two models. What surprise me were the different estimated variances for the random effects and also the estimated differences between fixed effects levels. Ruby -----Original Message----- From: ONKELINX, Thierry [mailto:Thierry.ONKELINX at inbo.be] Sent: 18 November 2010 15:43 To: Chang, Yu-Mei; r-sig-mixed-models at r-project.org Subject: RE: [R-sig-ME] glmer with/without intercept gave different results Dear Ruby, The hypotheses of those models are different. Hence the diference in p-values. Fit1: H0: Capsule 1 = 0 H0: Capsule 2 - Capsule 1 = 0 H0: Control - Capsule 1 = 0 Fit2: H0: Capsule 1 = 0 H0: Capsule 2 = 0 H0: Control = 0 However, the predictions of both model should be the same. Best regards, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey
-----Oorspronkelijk bericht----- Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Chang, Yu-Mei Verzonden: donderdag 18 november 2010 15:22 Aan: r-sig-mixed-models at r-project.org Onderwerp: [R-sig-ME] glmer with/without intercept gave different results Dear all, I have fitted two glmer models (with or without intercept term). I thought the results should be similar if not identical, but they are quite different. I suspect it's related to the random effects. Any suggestions on how to proceed is greatly appreciated. Kind regards, Ruby Chang fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) + (1|Cell),family=binomial(link = "logit")) fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) + (1|Cell),family=binomial(link = "logit"))
table(Cyptoplasmic.vacuolation, Group)
Group
Cyptoplasmic.vacuolation Capsule 1 Capsule 2 Control
0 560 340 1230
1 190 160 20
fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit1)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group + (1 | Villus) + (1 | Cell)
AIC BIC logLik deviance
138.9 168.1 -64.47 128.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 1983.5708 44.5373
Villus (Intercept) 5.9475 2.4387
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.945 9.123 -1.309 0.190
GroupCapsule 2 -1.409 14.343 -0.098 0.922
GroupControl -17.792 33.251 -0.535 0.593
Correlation of Fixed Effects:
(Intr) GrpCp2
GroupCapsl2 -0.636
GroupContrl -0.274 0.175
fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit2)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group - 1 + (1 | Villus) + (1 |
Cell)
AIC BIC logLik deviance
132.9 162.0 -61.43 122.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 5.9933e+03 7.7417e+01
Villus (Intercept) 1.2025e-07 3.4677e-04
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
GroupCapsule 1 -15.08 17.70 -0.852 0.394
GroupCapsule 2 -14.72 19.29 -0.763 0.445
GroupControl -18.23 54.54 -0.334 0.738
Correlation of Fixed Effects:
GrpCp1 GrpCp2
GroupCapsl2 0.000
GroupContrl 0.000 0.000
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Dear Ruby, I notice that the fixed effect estimates are very small and the Cell variance very large which may indicate numerical issues. What does: table(table(Cyptoplasmic.vacuolation, Cell)[1,]) look like? Cheers, Jarrod
On 18 Nov 2010, at 15:51, Chang, Yu-Mei wrote:
Dear Thierry, I understood the hypotheses were different between the two models. What surprise me were the different estimated variances for the random effects and also the estimated differences between fixed effects levels. Ruby -----Original Message----- From: ONKELINX, Thierry [mailto:Thierry.ONKELINX at inbo.be] Sent: 18 November 2010 15:43 To: Chang, Yu-Mei; r-sig-mixed-models at r-project.org Subject: RE: [R-sig-ME] glmer with/without intercept gave different results Dear Ruby, The hypotheses of those models are different. Hence the diference in p-values. Fit1: H0: Capsule 1 = 0 H0: Capsule 2 - Capsule 1 = 0 H0: Control - Capsule 1 = 0 Fit2: H0: Capsule 1 = 0 H0: Capsule 2 = 0 H0: Control = 0 However, the predictions of both model should be the same. Best regards, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey
-----Oorspronkelijk bericht----- Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Chang, Yu- Mei Verzonden: donderdag 18 november 2010 15:22 Aan: r-sig-mixed-models at r-project.org Onderwerp: [R-sig-ME] glmer with/without intercept gave different results Dear all, I have fitted two glmer models (with or without intercept term). I thought the results should be similar if not identical, but they are quite different. I suspect it's related to the random effects. Any suggestions on how to proceed is greatly appreciated. Kind regards, Ruby Chang fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) + (1|Cell),family=binomial(link = "logit")) fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) + (1|Cell),family=binomial(link = "logit"))
table(Cyptoplasmic.vacuolation, Group)
Group
Cyptoplasmic.vacuolation Capsule 1 Capsule 2 Control
0 560 340 1230
1 190 160 20
fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit1)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group + (1 | Villus) + (1 | Cell)
AIC BIC logLik deviance
138.9 168.1 -64.47 128.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 1983.5708 44.5373
Villus (Intercept) 5.9475 2.4387
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.945 9.123 -1.309 0.190
GroupCapsule 2 -1.409 14.343 -0.098 0.922
GroupControl -17.792 33.251 -0.535 0.593
Correlation of Fixed Effects:
(Intr) GrpCp2
GroupCapsl2 -0.636
GroupContrl -0.274 0.175
fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit2)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group - 1 + (1 | Villus) + (1 |
Cell)
AIC BIC logLik deviance
132.9 162.0 -61.43 122.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 5.9933e+03 7.7417e+01
Villus (Intercept) 1.2025e-07 3.4677e-04
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
GroupCapsule 1 -15.08 17.70 -0.852 0.394
GroupCapsule 2 -14.72 19.29 -0.763 0.445
GroupControl -18.23 54.54 -0.334 0.738
Correlation of Fixed Effects:
GrpCp1 GrpCp2
GroupCapsl2 0.000
GroupContrl 0.000 0.000
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.
Dear Jarrod, Yes, that's the culprit. The 10 repeated cells are either all 0's or 1's!
table(table(Cyptoplasmic.vacuolation, Cell)[1,])
0 10 37 213 Many thanks! Ruby -----Original Message----- From: Jarrod Hadfield [mailto:j.hadfield at ed.ac.uk] Sent: 18 November 2010 16:09 To: Chang, Yu-Mei Cc: ONKELINX, Thierry; r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] glmer with/without intercept gave different results Dear Ruby, I notice that the fixed effect estimates are very small and the Cell variance very large which may indicate numerical issues. What does: table(table(Cyptoplasmic.vacuolation, Cell)[1,]) look like? Cheers, Jarrod
On 18 Nov 2010, at 15:51, Chang, Yu-Mei wrote:
Dear Thierry, I understood the hypotheses were different between the two models. What surprise me were the different estimated variances for the random effects and also the estimated differences between fixed effects levels. Ruby -----Original Message----- From: ONKELINX, Thierry [mailto:Thierry.ONKELINX at inbo.be] Sent: 18 November 2010 15:43 To: Chang, Yu-Mei; r-sig-mixed-models at r-project.org Subject: RE: [R-sig-ME] glmer with/without intercept gave different results Dear Ruby, The hypotheses of those models are different. Hence the diference in p-values. Fit1: H0: Capsule 1 = 0 H0: Capsule 2 - Capsule 1 = 0 H0: Control - Capsule 1 = 0 Fit2: H0: Capsule 1 = 0 H0: Capsule 2 = 0 H0: Control = 0 However, the predictions of both model should be the same. Best regards, Thierry
------------------------------------------------------------------------
---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey
-----Oorspronkelijk bericht----- Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Chang, Yu- Mei Verzonden: donderdag 18 november 2010 15:22 Aan: r-sig-mixed-models at r-project.org Onderwerp: [R-sig-ME] glmer with/without intercept gave different results Dear all, I have fitted two glmer models (with or without intercept term). I thought the results should be similar if not identical, but they are quite different. I suspect it's related to the random effects. Any suggestions on how to proceed is greatly appreciated. Kind regards, Ruby Chang fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) + (1|Cell),family=binomial(link = "logit")) fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) + (1|Cell),family=binomial(link = "logit"))
table(Cyptoplasmic.vacuolation, Group)
Group
Cyptoplasmic.vacuolation Capsule 1 Capsule 2 Control
0 560 340 1230
1 190 160 20
fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit1)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group + (1 | Villus) + (1 | Cell)
AIC BIC logLik deviance
138.9 168.1 -64.47 128.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 1983.5708 44.5373
Villus (Intercept) 5.9475 2.4387
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.945 9.123 -1.309 0.190
GroupCapsule 2 -1.409 14.343 -0.098 0.922
GroupControl -17.792 33.251 -0.535 0.593
Correlation of Fixed Effects:
(Intr) GrpCp2
GroupCapsl2 -0.636
GroupContrl -0.274 0.175
fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit2)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group - 1 + (1 | Villus) + (1 |
Cell)
AIC BIC logLik deviance
132.9 162.0 -61.43 122.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 5.9933e+03 7.7417e+01
Villus (Intercept) 1.2025e-07 3.4677e-04
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
GroupCapsule 1 -15.08 17.70 -0.852 0.394
GroupCapsule 2 -14.72 19.29 -0.763 0.445
GroupControl -18.23 54.54 -0.334 0.738
Correlation of Fixed Effects:
GrpCp1 GrpCp2
GroupCapsl2 0.000
GroupContrl 0.000 0.000
[[alternative HTML version deleted]]
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_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.
Dear Ruby, I do not think a REML solution exists for the Cell variance in this instance (it's infinity). I presume the data for each Cell all have the same Villus and Group? If so you would be better off reducing your data to 250 binary data (i.e. One datum for each Cell) and removing the (1|Cell) term from the model. Cheers, Jarrod
On 18 Nov 2010, at 16:18, Chang, Yu-Mei wrote:
Dear Jarrod, Yes, that's the culprit. The 10 repeated cells are either all 0's or 1's!
table(table(Cyptoplasmic.vacuolation, Cell)[1,])
0 10 37 213 Many thanks! Ruby -----Original Message----- From: Jarrod Hadfield [mailto:j.hadfield at ed.ac.uk] Sent: 18 November 2010 16:09 To: Chang, Yu-Mei Cc: ONKELINX, Thierry; r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] glmer with/without intercept gave different results Dear Ruby, I notice that the fixed effect estimates are very small and the Cell variance very large which may indicate numerical issues. What does: table(table(Cyptoplasmic.vacuolation, Cell)[1,]) look like? Cheers, Jarrod On 18 Nov 2010, at 15:51, Chang, Yu-Mei wrote:
Dear Thierry, I understood the hypotheses were different between the two models. What surprise me were the different estimated variances for the random effects and also the estimated differences between fixed effects levels. Ruby -----Original Message----- From: ONKELINX, Thierry [mailto:Thierry.ONKELINX at inbo.be] Sent: 18 November 2010 15:43 To: Chang, Yu-Mei; r-sig-mixed-models at r-project.org Subject: RE: [R-sig-ME] glmer with/without intercept gave different results Dear Ruby, The hypotheses of those models are different. Hence the diference in p-values. Fit1: H0: Capsule 1 = 0 H0: Capsule 2 - Capsule 1 = 0 H0: Control - Capsule 1 = 0 Fit2: H0: Capsule 1 = 0 H0: Capsule 2 = 0 H0: Control = 0 However, the predictions of both model should be the same. Best regards, Thierry
------------------------------------------------------------------------
---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey
-----Oorspronkelijk bericht----- Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Chang, Yu- Mei Verzonden: donderdag 18 november 2010 15:22 Aan: r-sig-mixed-models at r-project.org Onderwerp: [R-sig-ME] glmer with/without intercept gave different results Dear all, I have fitted two glmer models (with or without intercept term). I thought the results should be similar if not identical, but they are quite different. I suspect it's related to the random effects. Any suggestions on how to proceed is greatly appreciated. Kind regards, Ruby Chang fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) + (1|Cell),family=binomial(link = "logit")) fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) + (1|Cell),family=binomial(link = "logit"))
table(Cyptoplasmic.vacuolation, Group)
Group
Cyptoplasmic.vacuolation Capsule 1 Capsule 2 Control
0 560 340 1230
1 190 160 20
fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit1)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group + (1 | Villus) + (1 |
Cell)
AIC BIC logLik deviance
138.9 168.1 -64.47 128.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 1983.5708 44.5373
Villus (Intercept) 5.9475 2.4387
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.945 9.123 -1.309 0.190
GroupCapsule 2 -1.409 14.343 -0.098 0.922
GroupControl -17.792 33.251 -0.535 0.593
Correlation of Fixed Effects:
(Intr) GrpCp2
GroupCapsl2 -0.636
GroupContrl -0.274 0.175
fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) +
(1|Cell),family=binomial(link = "logit"))
summary(fit2)
Generalized linear mixed model fit by the Laplace approximation
Formula: Cyptoplasmic.vacuolation ~ Group - 1 + (1 | Villus) + (1 |
Cell)
AIC BIC logLik deviance
132.9 162.0 -61.43 122.9
Random effects:
Groups Name Variance Std.Dev.
Cell (Intercept) 5.9933e+03 7.7417e+01
Villus (Intercept) 1.2025e-07 3.4677e-04
Number of obs: 2500, groups: Cell, 250; Villus, 50
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
GroupCapsule 1 -15.08 17.70 -0.852 0.394
GroupCapsule 2 -14.72 19.29 -0.763 0.445
GroupControl -18.23 54.54 -0.334 0.738
Correlation of Fixed Effects:
GrpCp1 GrpCp2
GroupCapsl2 0.000
GroupContrl 0.000 0.000
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
-- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.
The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.