R-SIG-mixed-models May 2015

MCMCglmm predictions with new data, marginalized with respect to random effects

1 msg May 31

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Malcolm Fairbrother MCMCglmm predictions with new data, marginalized with respect to random effects May 31, 2015

MCMCglmm predictions with new data, marginalized with respect to random effects

1 msg May 30

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Lenth, Russell V MCMCglmm predictions with new data, marginalized with respect to random effects May 30, 2015

Zero cells in contrast matrix problem

1 msg May 29

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Ben Bolker Zero cells in contrast matrix problem May 29, 2015

Zero cells in contrast matrix problem

1 msg May 28

B

Ben Bolker Zero cells in contrast matrix problem May 28, 2015

Zero cells in contrast matrix problem

5 msgs May 28

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Francesco Romano Zero cells in contrast matrix problem May 27, 2015

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Viechtbauer Wolfgang (STAT) Zero cells in contrast matrix problem May 27, 2015

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Ben Bolker Zero cells in contrast matrix problem May 27, 2015

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Francesco Romano Zero cells in contrast matrix problem May 28, 2015

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Ben Bolker Zero cells in contrast matrix problem May 28, 2015

MCMCglmm priors for multi-response multi-level model

1 msg May 28

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rafter sass ferguson MCMCglmm priors for multi-response multi-level model May 28, 2015

DHGLM Double hierarchical GLMs

3 msgs May 28

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Patrick, Samantha DHGLM Double hierarchical GLMs May 27, 2015

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Ken Beath DHGLM Double hierarchical GLMs May 28, 2015

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Patrick, Samantha DHGLM Double hierarchical GLMs May 28, 2015

multilevel analysis with sample weighted data

1 msg May 28

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Rodrigo Travitzki multilevel analysis with sample weighted data May 28, 2015

Release of the R package brms to fit Bayesian mixed effects models

1 msg May 28

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Paul Buerkner Release of the R package brms to fit Bayesian mixed effects models May 28, 2015

MCMCglmm predictions with new data, marginalized with respect to random effects

1 msg May 27

A

Alberto Gallano MCMCglmm predictions with new data, marginalized with respect to random effects May 27, 2015

MCMCglmm prediction without marginalising

1 msg May 27

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Manabu Sakamoto MCMCglmm prediction without marginalising May 27, 2015

lme4 and deviance

2 msgs May 27

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Nadège Jacot lme4 and deviance May 20, 2015

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Douglas Bates lme4 and deviance May 27, 2015

different results from lme and lmer function

8 msgs May 27

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Li Li different results from lme and lmer function May 26, 2015

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Ben Bolker different results from lme and lmer function May 26, 2015

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John Sorkin different results from lme and lmer function May 26, 2015

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Ben Bolker different results from lme and lmer function May 26, 2015

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Pantelis Z. Hadjipantelis different results from lme and lmer function May 26, 2015

B

Ben Bolker different results from lme and lmer function May 26, 2015

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Pantelis Z. Hadjipantelis different results from lme and lmer function May 26, 2015

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Douglas Bates different results from lme and lmer function May 27, 2015

lme function to obtain pvalue for fixed effect

2 msgs May 26

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Li Li lme function to obtain pvalue for fixed effect May 26, 2015

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Ben Bolker lme function to obtain pvalue for fixed effect May 26, 2015

lme function to obtain pvalue for fixed effect

4 msgs May 26

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Li Li lme function to obtain pvalue for fixed effect May 26, 2015

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byron vinueza lme function to obtain pvalue for fixed effect May 26, 2015

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Li Li lme function to obtain pvalue for fixed effect May 26, 2015

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Thierry Onkelinx lme function to obtain pvalue for fixed effect May 26, 2015

metafor - Appropriate heterogeneity estimator when heterogeneity is extreme

1 msg May 25

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Viechtbauer Wolfgang (STAT) metafor - Appropriate heterogeneity estimator when heterogeneity is extreme May 25, 2015

metafor - Appropriate heterogeneity estimator when heterogeneity is extreme

1 msg May 25

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Theodore Lytras metafor - Appropriate heterogeneity estimator when heterogeneity is extreme May 25, 2015

estimating AR1 parameters of level one error

2 msgs May 22

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Steve Candy estimating AR1 parameters of level one error May 21, 2015

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Viechtbauer Wolfgang (STAT) estimating AR1 parameters of level one error May 22, 2015

error with parametric bootstrap for negative binomial model, lme4, pbkrtest

2 msgs May 21

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Lauren Catherine Ponisio error with parametric bootstrap for negative binomial model, lme4, pbkrtest May 21, 2015

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Leithen error with parametric bootstrap for negative binomial model, lme4, pbkrtest May 21, 2015

: estimating AR1 parameters of level one error using lme

1 msg May 20

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Viechtbauer Wolfgang (STAT) : estimating AR1 parameters of level one error using lme May 20, 2015

: estimating AR1 parameters of level one error using lme

1 msg May 20

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Steve Candy : estimating AR1 parameters of level one error using lme May 20, 2015

genetic effects and multiple membership in MCMCglmm (Alexandre Martin)

2 msgs May 19

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Szymek Drobniak genetic effects and multiple membership in MCMCglmm (Alexandre Martin) May 14, 2015

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Alexandre Martin genetic effects and multiple membership in MCMCglmm (Alexandre Martin) May 19, 2015

estimating AR1 parameters of level one error using lme

5 msgs May 19

A

Asher Strauss estimating AR1 parameters of level one error using lme May 15, 2015

D

Daniel Wright estimating AR1 parameters of level one error using lme May 18, 2015

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Asher Strauss estimating AR1 parameters of level one error using lme May 19, 2015

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Thierry Onkelinx estimating AR1 parameters of level one error using lme May 19, 2015

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Viechtbauer Wolfgang (STAT) estimating AR1 parameters of level one error using lme May 19, 2015

MCMCglmm phylogenetic model with polymorphic binary outcome

1 msg May 17

A

Alberto Gallano MCMCglmm phylogenetic model with polymorphic binary outcome May 17, 2015

toxicology dietary subchronic feed/weight analysis

5 msgs May 17

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Gosse, Michelle toxicology dietary subchronic feed/weight analysis May 10, 2015

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Thierry Onkelinx toxicology dietary subchronic feed/weight analysis May 11, 2015

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Steve Denham toxicology dietary subchronic feed/weight analysis May 15, 2015

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Steve Denham toxicology dietary subchronic feed/weight analysis May 15, 2015

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Gosse, Michelle toxicology dietary subchronic feed/weight analysis May 17, 2015

lmer(): Higher weight in case of more measurements

1 msg May 15

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Susanne Susanne lmer(): Higher weight in case of more measurements May 15, 2015

lmer(): Higher weight in case of more measurements

5 msgs May 15

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Susanne Susanne lmer(): Higher weight in case of more measurements May 15, 2015

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Roman Luštrik lmer(): Higher weight in case of more measurements May 15, 2015

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Bradley Carlson lmer(): Higher weight in case of more measurements May 15, 2015

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Ben Bolker lmer(): Higher weight in case of more measurements May 15, 2015

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Susanne Susanne lmer(): Higher weight in case of more measurements May 15, 2015

glmm question

4 msgs May 14

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Joaquín Aldabe glmm question May 13, 2015

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Ken Beath glmm question May 13, 2015

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Joaquín Aldabe glmm question May 14, 2015

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Ken Beath glmm question May 14, 2015

direct-maternal correlation

1 msg May 14

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emre karaman direct-maternal correlation May 14, 2015

glmm question

1 msg May 13

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Joaquín Aldabe glmm question May 13, 2015

genetic effects and multiple membership in MCMCglmm

1 msg May 13

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Alexandre Martin genetic effects and multiple membership in MCMCglmm May 13, 2015

random effects heteroscedasticity glmm

2 msgs May 13

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Lauren Catherine Ponisio random effects heteroscedasticity glmm May 12, 2015

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Thierry Onkelinx random effects heteroscedasticity glmm May 13, 2015

Course: Introduction to zero inflated models with R

1 msg May 11

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Highland Statistics Ltd Course: Introduction to zero inflated models with R May 11, 2015

heterogeneous variance models

2 msgs May 11

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Hufthammer, Karl Ove heterogeneous variance models May 11, 2015

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Ben Bolker heterogeneous variance models May 11, 2015

heterogeneous variance models

2 msgs May 11

B

Ben Bolker heterogeneous variance models May 10, 2015

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Aaron Mackey heterogeneous variance models May 11, 2015

Multiple threshold/ordinal traits in MCMCglmm

1 msg May 9

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Napoleon Vargas Multiple threshold/ordinal traits in MCMCglmm May 9, 2015

GAMM big data (70K rand effects) guidance

3 msgs May 9

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Steve Bellan GAMM big data (70K rand effects) guidance May 7, 2015

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Thierry Onkelinx GAMM big data (70K rand effects) guidance May 8, 2015

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Ken Beath GAMM big data (70K rand effects) guidance May 9, 2015

Logistic modelling with guessing parameter

3 msgs May 8

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Peter Harrison Logistic modelling with guessing parameter May 8, 2015

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Ken Knoblauch Logistic modelling with guessing parameter May 8, 2015

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Peter Harrison Logistic modelling with guessing parameter May 8, 2015

another gls question

2 msgs May 8

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Hufthammer, Karl Ove another gls question May 8, 2015

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Mark Leeds another gls question May 8, 2015

Including a correlation matrix with the metafor package

2 msgs May 8

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Eike-Lena Neuschulz Including a correlation matrix with the metafor package May 7, 2015

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Viechtbauer Wolfgang (STAT) Including a correlation matrix with the metafor package May 8, 2015

another gls question

2 msgs May 8

M

Mark Leeds another gls question May 7, 2015

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Thierry Onkelinx another gls question May 8, 2015

Logistic modelling with guessing parameter

4 msgs May 7

P

Peter Harrison Logistic modelling with guessing parameter May 7, 2015

B

Ben Bolker Logistic modelling with guessing parameter May 7, 2015

B

Ben Bolker Logistic modelling with guessing parameter May 7, 2015

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Ken Knoblauch Logistic modelling with guessing parameter May 7, 2015

REML vs ML in lmerTest

5 msgs May 7

B

Bradley Carlson REML vs ML in lmerTest May 5, 2015

D

Daniel Wright REML vs ML in lmerTest May 5, 2015

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Bradley Carlson REML vs ML in lmerTest May 5, 2015

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Daniel Wright REML vs ML in lmerTest May 5, 2015

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Bradley Carlson REML vs ML in lmerTest May 7, 2015

Number of Iterations lme4

2 msgs May 7

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Jakob Gährken Number of Iterations lme4 May 7, 2015

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Thierry Onkelinx Number of Iterations lme4 May 7, 2015

question about gls in nlme

2 msgs May 7

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Mark Leeds question about gls in nlme May 6, 2015

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Thierry Onkelinx question about gls in nlme May 7, 2015

Including an autocorrelation term dramatically reduces random variance (lme)

1 msg May 7

T

Thierry Onkelinx Including an autocorrelation term dramatically reduces random variance (lme) May 7, 2015

Interpreting the ACME in mediation

1 msg May 6

E

Elizabeth Pasipanodya Interpreting the ACME in mediation May 6, 2015

gamm4 "no terms component nor attribute"

2 msgs May 6

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Steve Bellan gamm4 "no terms component nor attribute" May 5, 2015

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Ken Beath gamm4 "no terms component nor attribute" May 6, 2015

lmer: constraining sigma to 0

2 msgs May 6

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Markus Brauer lmer: constraining sigma to 0 May 6, 2015

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Ben Bolker lmer: constraining sigma to 0 May 6, 2015

estimates of low variance in mcmcglmm (cont.)

1 msg May 6

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Robert Griffin estimates of low variance in mcmcglmm (cont.) May 6, 2015

Including an autocorrelation term dramatically reduces random variance (lme)

1 msg May 6

D

David Villegas Ríos Including an autocorrelation term dramatically reduces random variance (lme) May 6, 2015

mixed-effects model with crossed random effects

1 msg May 4

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Magdalena Wiedermann mixed-effects model with crossed random effects May 4, 2015

glmmADMB

1 msg May 3

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Thierry Onkelinx glmmADMB May 3, 2015

Using lme4 with very limited number of observations makes sense?

1 msg May 3

K

Ken Beath Using lme4 with very limited number of observations makes sense? May 3, 2015

Using lme4 with very limited number of observations makes sense?

1 msg May 3

J

Jonathan Baron Using lme4 with very limited number of observations makes sense? May 3, 2015

Random effect in ancova model

1 msg May 1

L

Li Li Random effect in ancova model May 1, 2015

GlmmADMB: random slopes and fixed effects

1 msg May 1

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Genevieve Perkins GlmmADMB: random slopes and fixed effects May 1, 2015