Second quarter 2015 Archives by thread

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Starting: Wed Apr 1 03:45:21 CEST 2015
Ending: Tue Jun 30 15:11:37 CEST 2015
Messages: 356

• [R-sig-ME] predict() with varying betas (newparams)   Hans Ekbrand
  • [R-sig-ME] predict() with varying betas (newparams)   Ben Bolker
    • [R-sig-ME] predict() with varying betas (newparams)   Hans Ekbrand
• [R-sig-ME] problem bootstraping a mixed-model (lme)   David Villegas Ríos
  • [R-sig-ME] problem bootstraping a mixed-model (lme)   Ken Beath
    • [R-sig-ME] problem bootstraping a mixed-model (lme)   David Villegas Ríos
      • [R-sig-ME] problem bootstraping a mixed-model (lme)   Ken Beath
      • [R-sig-ME] problem bootstraping a mixed-model (lme)   Ben Bolker
      • [R-sig-ME] problem bootstraping a mixed-model (lme)   Ken Beath
      • [R-sig-ME] problem bootstraping a mixed-model (lme)   David Villegas Ríos
      • [R-sig-ME] problem bootstraping a mixed-model (lme)   Ken Beath
      • [R-sig-ME] calculation max|grad value?   Ben Pelzer
      • [R-sig-ME] calculation max|grad value?   Daniel McCloy
      • [R-sig-ME] calculation max|grad value?   Ben Pelzer
      • [R-sig-ME] calculation max|grad value?   Douglas Bates
      • [R-sig-ME] calculation max|grad value?   Ben Pelzer
      • [R-sig-ME] calculation max|grad value?   Ben Bolker
• [R-sig-ME] Is it possible to account for temporal autocorrelation in MCMCglmm?   David Villegas Ríos
  • [R-sig-ME] Is it possible to account for temporal autocorrelation in MCMCglmm?   Jarrod Hadfield
• [R-sig-ME] glmer does not converge, how inaccurate is using nAGQ = 0?   Paolo Fraccaro
  • [R-sig-ME] glmer does not converge, how inaccurate is using nAGQ = 0?   Ken Beath
    • [R-sig-ME] glmer does not converge, how inaccurate is using nAGQ = 0?   Ben Bolker
    • [R-sig-ME] glmer does not converge, how inaccurate is using nAGQ = 0?   Paolo Fraccaro
    • [R-sig-ME] glmer does not converge, how inaccurate is using nAGQ = 0?   Ken Beath
      • [R-sig-ME] glmer does not converge, how inaccurate is using nAGQ = 0?   Ken Beath
• [R-sig-ME] 2 statistics courses in Perth   Highland Statistics Ltd
• [R-sig-ME] Problems with convergence   Ken Beath
  • [R-sig-ME] Problems with convergence   Ben Bolker
    • [R-sig-ME] Problems with convergence   Ken Beath
• [R-sig-ME] heritability from longitudinal zero-inflated count data   Paul Johnson
  • [R-sig-ME] heritability from longitudinal zero-inflated count data   Jarrod Hadfield
    • [R-sig-ME] heritability from longitudinal zero-inflated count data   Jarrod Hadfield
      • [R-sig-ME] heritability from longitudinal zero-inflated count data   Paul Johnson
• [R-sig-ME] design matrices in MCMCglmm   Jarrod Hadfield
  • [R-sig-ME] design matrices in MCMCglmm   Jarrod Hadfield
• [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
  • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Ken Beath
    • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Ben Bolker
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Ken Beath
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Ken Beath
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Ken Beath
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Ken Beath
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Ben Pelzer
      • [R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?   Hans Ekbrand
• [R-sig-ME] Removing p.d. constraint for random effects in lme   R User
  • [R-sig-ME] Removing p.d. constraint for random effects in lme   Ben Bolker
    • [R-sig-ME] Removing p.d. constraint for random effects in lme   Douglas Bates
      • [R-sig-ME] Removing p.d. constraint for random effects in lme   Douglas Bates
• [R-sig-ME] bootstraping a mixed-model (lme)   Roslyn Dakin
  • [R-sig-ME] bootstrapping a mixed-model (lme)   David Duffy
  • [R-sig-ME] bootstraping a mixed-model (lme)   Ken Beath
• [R-sig-ME] Statistics course: Darwin, Australia   Highland Statistics Ltd
• [R-sig-ME] contrast of predicted slopes from MCMCglmm model   Daniel Fulop
  • [R-sig-ME] contrast of predicted slopes from MCMCglmm model   Lenth, Russell V
    • [R-sig-ME] contrast of predicted slopes from MCMCglmm model   Daniel Fulop
      • [R-sig-ME] contrast of predicted slopes from MCMCglmm model   Lenth, Russell V
      • [R-sig-ME] contrast of predicted slopes from MCMCglmm model   Daniel Fulop
      • [R-sig-ME] contrast of predicted slopes from MCMCglmm model   Daniel Fulop
• [R-sig-ME] Post-hoc test and Cook's distance vs. leverageplot for glmer from package lme4   Mario Garrido
  • [R-sig-ME] Post-hoc test and Cook's distance vs. leverageplot for glmer from package lme4   Ben Bolker
    • [R-sig-ME] Post-hoc test and Cook's distance vs. leverageplot for glmer from package lme4   Mario Garrido
• [R-sig-ME] "mixed" MANOVAs   Mike
• [R-sig-ME] Wheigts glmmADMB   bbonit at tin.it
• [R-sig-ME] Weights argument glmmADMB   bbonit at tin.it
  • [R-sig-ME] Weights argument glmmADMB   Ben Bolker
  • [R-sig-ME] Weights argument glmmADMB   bbonit at tin.it
    • [R-sig-ME] Weights argument glmmADMB   Joseph Bulbulia
• [R-sig-ME] Zero inflation parameter in glmmadmb   Zahwa Al Ayyash (Student)
• [R-sig-ME] mixed effect models where time ordering is important   Gosse, Michelle
  • [R-sig-ME] mixed effect models where time ordering is important   John Maindonald
    • [R-sig-ME] mixed effect models where time ordering is important   Gosse, Michelle
  • [R-sig-ME] mixed effect models where time ordering is important   Cole, Tim
  • [R-sig-ME] mixed effect models where time ordering is important   Steve Candy
    • [R-sig-ME] mixed effect models where time ordering is important   Emmanuel Curis
      • [R-sig-ME] mixed effect models where time ordering is important   Steve Candy
      • [R-sig-ME] mixed effect models where time ordering is important   Emmanuel Curis
      • [R-sig-ME] mixed effect models where time ordering is important   Steve Candy
  • [R-sig-ME] mixed effect models where time ordering is important   Steven J. Pierce
    • [R-sig-ME] mixed effect models where time ordering is important   Gosse, Michelle
    • [R-sig-ME] mixed effect models where time ordering is important   Ken Beath
      • [R-sig-ME] mixed effect models where time ordering is important   Gosse, Michelle
• [R-sig-ME] GLMM Power Analysis   Johanna Bradie Simpkins
• [R-sig-ME] nested mixed effects logistic regression binomial glm) results differ by function.   Lize van der Merwe
  • [R-sig-ME] nested mixed effects logistic regression binomial glm)results differ by function.   David Duffy
    • [R-sig-ME] nested mixed effects logistic regression binomial glm)results differ by function.   Lize van der Merwe
  • [R-sig-ME] nested mixed effects logistic regression binomial glm) results differ by function.   Thierry Onkelinx
    • [R-sig-ME] nested mixed effects logistic regression binomial glm) results differ by function.   Linus Holtermann
  • [R-sig-ME] nested mixed effects logistic regression binomial glm) results differ by function.   Malcolm Fairbrother
    • [R-sig-ME] nested mixed effects logistic regression binomial glm) results differ by function.   Lize van der Merwe
    • [R-sig-ME] nested mixed effects logistic regression binomial glm) results differ by function.   Ben Bolker
• [R-sig-ME] GLM course in Palm Cove   Highland Statistics Ltd
• [R-sig-ME] nested mixed effects logistic regression binomial, glm) results differ by function.   Highland Statistics Ltd
• [R-sig-ME] Resulat from GLMM, error bars for predictions   Quentin Schorpp
  • [R-sig-ME] Resulat from GLMM, error bars for predictions   Thierry Onkelinx
    • [R-sig-ME] Results from GLMM, error bars for predictions   Thierry Onkelinx
      • [R-sig-ME] Results from GLMM, error bars for predictions   Quentin Schorpp
      • [R-sig-ME] Results from GLMM, error bars for predictions   Thierry Onkelinx
      • [R-sig-ME] Results from GLMM, error bars for predictions   Quentin Schorpp
      • [R-sig-ME] Results from GLMM, error bars for predictions   Ben Bolker
• [R-sig-ME] mixed-effects model with crossed random effects   Magdalena Wiedermann
  • [R-sig-ME] mixed-effects model with crossed random effects   Ben Bolker
    • [R-sig-ME] mixed-effects model with crossed random effects   Magdalena Wiedermann
• [R-sig-ME] predict.merMod with random slopes - revisiting an older issue?   Robert Zimbardo
• [R-sig-ME] glmer.nb(): Residual term ??   Christine Adrion
  • [R-sig-ME] glmer.nb(): Residual term ??   Daniel Fulop
• [R-sig-ME] Using lme4 with very limited number of observations makes sense?   Massimiliano Mario Iraci
  • [R-sig-ME] Using lme4 with very limited number of observations makes sense?   Jonathan Baron
  • [R-sig-ME] Using lme4 with very limited number of observations makes sense?   Ken Beath
• [R-sig-ME] GlmmADMB: random slopes and fixed effects   Genevieve Perkins
  • [R-sig-ME] GlmmADMB: random slopes and fixed effects   Thierry Onkelinx
    • [R-sig-ME] GlmmADMB: random slopes and fixed effects   Genevieve Perkins
      • [R-sig-ME] GlmmADMB: random slopes and fixed effects   Ken Beath
      • [R-sig-ME] GlmmADMB: random slopes and fixed effects   Ben Bolker
      • [R-sig-ME] GlmmADMB: random slopes and fixed effects   Genevieve Perkins
• [R-sig-ME] Adding additional factor to previously working MCMCglmm model kills it. Problem with priors? Help?   Tricia Markle
  • [R-sig-ME] Adding additional factor to previously working MCMCglmm model kills it. Problem with priors? Help?   Jarrod Hadfield
    • [R-sig-ME] Adding additional factor to previously working MCMCglmm model kills it. Problem with priors? Help?   Tricia Markle
• [R-sig-ME] glmmADMB   Silvia Rodríguez Fernández
  • [R-sig-ME] glmmADMB   Thierry Onkelinx
    • [R-sig-ME] glmmADMB   Silvia Rodríguez Fernández
      • [R-sig-ME] glmmADMB   Thierry Onkelinx
• [R-sig-ME] Random effect in ancova model   li li
• [R-sig-ME] REML vs ML in lmerTest   Bradley Carlson
  • [R-sig-ME] REML vs ML in lmerTest   Daniel Wright
    • [R-sig-ME] REML vs ML in lmerTest   Bradley Carlson
      • [R-sig-ME] REML vs ML in lmerTest   Daniel Wright
      • [R-sig-ME] REML vs ML in lmerTest   Bradley Carlson
• [R-sig-ME] gamm4 "no terms component nor attribute"   Steve Bellan
  • [R-sig-ME] gamm4 "no terms component nor attribute"   Ken Beath
• [R-sig-ME] Including an autocorrelation term dramatically reduces random variance (lme)   David Villegas Ríos
  • [R-sig-ME] Including an autocorrelation term dramatically reduces random variance (lme)   Thierry Onkelinx
• [R-sig-ME] estimates of low variance in mcmcglmm (cont.)   Robert Griffin
• [R-sig-ME] lmer: constraining sigma to 0   Markus Brauer
  • [R-sig-ME] lmer: constraining sigma to 0   Ben Bolker
• [R-sig-ME] question about gls in nlme   Mark Leeds
  • [R-sig-ME] question about gls in nlme   Thierry Onkelinx
• [R-sig-ME] Interpreting the ACME in mediation   Elizabeth Pasipanodya
• [R-sig-ME] Number of Iterations lme4   Jakob Gährken
  • [R-sig-ME] Number of Iterations lme4   Thierry Onkelinx
• [R-sig-ME] Including a correlation matrix with the metafor package   Eike-Lena Neuschulz
  • [R-sig-ME] Including a correlation matrix with the metafor package   Viechtbauer Wolfgang (STAT)
• [R-sig-ME] GAMM big data (70K rand effects) guidance   Steve Bellan
  • [R-sig-ME] GAMM big data (70K rand effects) guidance   Thierry Onkelinx
  • [R-sig-ME] GAMM big data (70K rand effects) guidance   Ken Beath
• [R-sig-ME] Logistic modelling with guessing parameter   Peter Harrison
  • [R-sig-ME] Logistic modelling with guessing parameter   Ben Bolker
  • [R-sig-ME] Logistic modelling with guessing parameter   Ben Bolker
  • [R-sig-ME] Logistic modelling with guessing parameter   Ken Knoblauch
  • [R-sig-ME] Logistic modelling with guessing parameter   Peter Harrison
    • [R-sig-ME] Logistic modelling with guessing parameter   ken knoblauch
      • [R-sig-ME] Logistic modelling with guessing parameter   Peter Harrison
• [R-sig-ME] another gls question   Mark Leeds
  • [R-sig-ME] another gls question   Thierry Onkelinx
  • [R-sig-ME] another gls question   Hufthammer, Karl Ove
    • [R-sig-ME] another gls question   Mark Leeds
• [R-sig-ME] Multiple threshold/ordinal traits in MCMCglmm   Napoleon Vargas
• [R-sig-ME] heterogeneous variance models   Ben Bolker
  • [R-sig-ME] heterogeneous variance models   Aaron Mackey
  • [R-sig-ME] heterogeneous variance models   Hufthammer, Karl Ove
    • [R-sig-ME] heterogeneous variance models   Ben Bolker
• [R-sig-ME] toxicology dietary subchronic feed/weight analysis   Gosse, Michelle
  • [R-sig-ME] toxicology dietary subchronic feed/weight analysis   Thierry Onkelinx
    • [R-sig-ME] toxicology dietary subchronic feed/weight analysis   Steve Denham
      • [R-sig-ME] toxicology dietary subchronic feed/weight analysis   Gosse, Michelle
  • [R-sig-ME] toxicology dietary subchronic feed/weight analysis   Steve Denham
• [R-sig-ME] Course: Introduction to zero inflated models with R   Highland Statistics Ltd
• [R-sig-ME] Fwd: random effects heteroscedasticity glmm   Lauren Catherine Ponisio
  • [R-sig-ME] Fwd: random effects heteroscedasticity glmm   Thierry Onkelinx
• [R-sig-ME] genetic effects and multiple membership in MCMCglmm   Alexandre Martin
• [R-sig-ME] glmm question   Joaquín Aldabe
  • [R-sig-ME] glmm question   Joaquín Aldabe
  • [R-sig-ME] glmm question   Ken Beath
    • [R-sig-ME] glmm question   Joaquín Aldabe
      • [R-sig-ME] glmm question   Ken Beath
• [R-sig-ME] direct-maternal correlation   emre karaman
• [R-sig-ME] genetic effects and multiple membership in MCMCglmm (Alexandre Martin)   Szymek Drobniak
  • [R-sig-ME] genetic effects and multiple membership in MCMCglmm (Alexandre Martin)   Alexandre Martin
• [R-sig-ME] lmer(): Higher weight in case of more measurements   Susanne Susanne
  • [R-sig-ME] lmer(): Higher weight in case of more measurements   romunov
    • [R-sig-ME] lmer(): Higher weight in case of more measurements   Ben Bolker
      • [R-sig-ME] lmer(): Higher weight in case of more measurements   Susanne Susanne
  • [R-sig-ME] lmer(): Higher weight in case of more measurements   Bradley Carlson
  • [R-sig-ME] lmer(): Higher weight in case of more measurements   Susanne Susanne
  • [R-sig-ME] lmer(): Higher weight in case of more measurements   Susanne Susanne
    • [R-sig-ME] lmer(): Higher weight in case of more measurements   Ken Beath
      • [R-sig-ME] lmer(): Higher weight in case of more measurements   Ken Beath
  • [R-sig-ME] lmer(): Higher weight in case of more measurements   Susanne Susanne
• [R-sig-ME] estimating AR1 parameters of level one error using lme   Asher Strauss
  • [R-sig-ME] estimating AR1 parameters of level one error using lme   Daniel Wright
    • [R-sig-ME] estimating AR1 parameters of level one error using lme   Asher Strauss
      • [R-sig-ME] estimating AR1 parameters of level one error using lme   Thierry Onkelinx
      • [R-sig-ME] estimating AR1 parameters of level one error using lme   Viechtbauer Wolfgang (STAT)
• [R-sig-ME] MCMCglmm phylogenetic model with polymorphic binary outcome   Alberto Gallano
• [R-sig-ME] : estimating AR1 parameters of level one error using lme   Steve Candy
  • [R-sig-ME] : estimating AR1 parameters of level one error using lme   Viechtbauer Wolfgang (STAT)
• [R-sig-ME] lme4 and deviance   Nadège Jacot
  • [R-sig-ME] lme4 and deviance   Douglas Bates
• [R-sig-ME] error with parametric bootstrap for negative binomial model, lme4, pbkrtest   Lauren Catherine Ponisio
  • [R-sig-ME] error with parametric bootstrap for negative binomial model, lme4, pbkrtest   Leithen
• [R-sig-ME] estimating AR1 parameters of level one error   Steve Candy
  • [R-sig-ME] estimating AR1 parameters of level one error   Viechtbauer Wolfgang (STAT)
• [R-sig-ME] metafor - Appropriate heterogeneity estimator when heterogeneity is extreme   Theodore Lytras
  • [R-sig-ME] metafor - Appropriate heterogeneity estimator when heterogeneity is extreme   Viechtbauer Wolfgang (STAT)
• [R-sig-ME] lme function to obtain pvalue for fixed effect   li li
  • [R-sig-ME] lme function to obtain pvalue for fixed effect   byron vinueza
    • [R-sig-ME] lme function to obtain pvalue for fixed effect   li li
      • [R-sig-ME] lme function to obtain pvalue for fixed effect   Thierry Onkelinx
      • [R-sig-ME] lme function to obtain pvalue for fixed effect   li li
      • [R-sig-ME] lme function to obtain pvalue for fixed effect   Ben Bolker
• [R-sig-ME] different results from lme and lmer function   li li
  • [R-sig-ME] different results from lme and lmer function   Ben Bolker
    • [R-sig-ME] [R] different results from lme and lmer function   John Sorkin
      • [R-sig-ME] [R] different results from lme and lmer function   Ben Bolker
      • [R-sig-ME] [R] different results from lme and lmer function   Pantelis Z. Hadjipantelis
      • [R-sig-ME] [R] different results from lme and lmer function   Ben Bolker
      • [R-sig-ME] [R] different results from lme and lmer function   Pantelis Z. Hadjipantelis
      • [R-sig-ME] [R] different results from lme and lmer function   Douglas Bates
• [R-sig-ME] DHGLM Double hierarchical GLMs   Patrick, Samantha
  • [R-sig-ME] DHGLM Double hierarchical GLMs   Ken Beath
  • [R-sig-ME] Fw: DHGLM Double hierarchical GLMs   Patrick, Samantha
• [R-sig-ME] Zero cells in contrast matrix problem   Francesco Romano
  • [R-sig-ME] Zero cells in contrast matrix problem   Viechtbauer Wolfgang (STAT)
    • [R-sig-ME] Zero cells in contrast matrix problem   Ben Bolker
      • [R-sig-ME] Zero cells in contrast matrix problem   Francesco Romano
      • [R-sig-ME] Zero cells in contrast matrix problem   Ben Bolker
      • [R-sig-ME] Zero cells in contrast matrix problem   Ben Bolker
      • [R-sig-ME] Zero cells in contrast matrix problem   Ben Bolker
• [R-sig-ME] MCMCglmm prediction without marginalising   Manabu Sakamoto
• [R-sig-ME] MCMCglmm predictions with new data, marginalized with respect to random effects   Alberto Gallano
  • [R-sig-ME] MCMCglmm predictions with new data, marginalized with respect to random effects   Lenth, Russell V
  • [R-sig-ME] MCMCglmm predictions with new data, marginalized with respect to random effects   Malcolm Fairbrother
• [R-sig-ME] Release of the R package brms to fit Bayesian mixed effects models   Paul Buerkner
• [R-sig-ME] multilevel analysis with sample weighted data   Rodrigo Travitzki
• [R-sig-ME] MCMCglmm priors for multi-response multi-level model   rafter sass ferguson
• [R-sig-ME] interactions lmer continuous and categorical fixed factor   Lotte Schoot
  • [R-sig-ME] interactions lmer continuous and categorical fixed factor   Thierry Onkelinx
    • [R-sig-ME] interactions lmer continuous and categorical fixed factor   Ben Bolker
• [R-sig-ME] proportion data based on finite population size   Thomas M
• [R-sig-ME] R-sig-mixed-models Digest, Vol 102, Issue 3   Gregoire, Timothy
• [R-sig-ME] error when including correlation=corAR1 in a gamm model   David Villegas Ríos
• [R-sig-ME] Binomial Temporal GAMM does not converge (R::mgcv)   Benjamin Kellman
  • [R-sig-ME] Binomial Temporal GAMM does not converge (R::mgcv)   Ken Beath
• [R-sig-ME] Compute a repeated measures model in lme4   Mario Garrido
  • [R-sig-ME] Compute a repeated measures model in lme4   Philippi, Tom
    • [R-sig-ME] Compute a repeated measures model in lme4   Mario Garrido
      • [R-sig-ME] Compute a repeated measures model in lme4   Mario Garrido
    • [R-sig-ME] Compute a repeated measures model in lme4   Ryan Hope
    • [R-sig-ME] Compute a repeated measures model in lme4   Philippi, Tom
      • [R-sig-ME] Compute a repeated measures model in lme4   Mario Garrido
      • [R-sig-ME] Compute a repeated measures model in lme4   Mario Garrido
• [R-sig-ME] help with the logistic formula using nlme/nlmer   Hans Ekbrand
  • [R-sig-ME] help with the logistic formula using nlme/nlmer   Thierry Onkelinx
    • [R-sig-ME] help with the logistic formula using nlme/nlmer   Phillip Alday
  • [R-sig-ME] help with the logistic formula using nlme/nlmer   David Duffy
• [R-sig-ME] Fwd: glme output   Daniel Frese
  • [R-sig-ME] Fwd: glme output   Ben Bolker
• [R-sig-ME] Warning message when using lmer function   li li
  • [R-sig-ME] Warning message when using lmer function   Ben Bolker
• [R-sig-ME] Different random intercepts but same random slope for groups   li li
  • [R-sig-ME] [R] Different random intercepts but same random slope for groups   Thierry Onkelinx
    • [R-sig-ME] [R] Different random intercepts but same random slope for groups   Bert Gunter
      • [R-sig-ME] [R] Different random intercepts but same random slope for groups   li li
      • [R-sig-ME] [R] Different random intercepts but same random slope for groups   Ben Bolker
      • [R-sig-ME] [R] Different random intercepts but same random slope for groups   li li
    • [R-sig-ME] [R] Different random intercepts but same random slope for groups   Ben Bolker
      • [R-sig-ME] [R] Different random intercepts but same random slope for groups   Ken Beath
• [R-sig-ME] Fwd: Clarifications on implementation of lmer (lme4) in R -weights   Christian Blanco
  • [R-sig-ME] Fwd: Clarifications on implementation of lmer (lme4) in R -weights   Rodrigo Travitzki
• [R-sig-ME] Cross-over Data with Kenward-Roger correction   knouri
  • [R-sig-ME] Cross-over Data with Kenward-Roger correction   Lenth, Russell V
• [R-sig-ME] PBmodcomp: pwrssUpdate does not converge with glmer   lorenz.gygax at agroscope.admin.ch
  • [R-sig-ME] PBmodcomp: pwrssUpdate does not converge with glmer   Ben Bolker
• [R-sig-ME] MCMCglmm priors and random effects for phylogenetic mixed model   Alberto Gallano
• [R-sig-ME] lme4 - equal estimates of regression coefficients across levels of a random effect   Nicolas Deguines
  • [R-sig-ME] lme4 - equal estimates of regression coefficients across levels of a random effect   Thierry Onkelinx
    • [R-sig-ME] lme4 - equal estimates of regression coefficients across levels of a random effect   Nicolas Deguines
      • [R-sig-ME] lme4 - equal estimates of regression coefficients across levels of a random effect   Ben Bolker
• [R-sig-ME] factor specific residual variance for random coefficient model with lmer   li li
  • [R-sig-ME] factor specific residual variance for random coefficient model with lmer   li li
    • [R-sig-ME] factor specific residual variance for random coefficient model with lmer   Thierry Onkelinx
• [R-sig-ME] Follow-up: PBmodcomp: pwrssUpdate does not converge with glmer   lorenz.gygax at agroscope.admin.ch
• [R-sig-ME] Doubts with the syntax in lme4   Mario Garrido
• [R-sig-ME] Error message in lme function and question about the residual plot   li li
  • [R-sig-ME] Error message in lme function and question about the residual plot   Thierry Onkelinx
    • [R-sig-ME] Error message in lme function and question about the residual plot   li li
• [R-sig-ME] MCMCglmm Bivariate model and prior   Greg - Univ/Pro
• [R-sig-ME] Set and interpret interaction terms in MCMCglmm   Patricia Soto
• [R-sig-ME] clustered data with glmer() and glmmPQL()   Marsela Alvanopoulou
  • [R-sig-ME] clustered data with glmer() and glmmPQL()   Thierry Onkelinx
    • [R-sig-ME] clustered data with glmer() and glmmPQL()   Marsela Alvanopoulou
      • [R-sig-ME] clustered data with glmer() and glmmPQL()   Thierry Onkelinx
• [R-sig-ME] sample with two weights in different levels   Rodrigo Travitzki
• [R-sig-ME] Spatial analysis   BURGUEÑO FERREIRA, Juan Andres (CIMMYT)
  • [R-sig-ME] Spatial analysis   Kevin Wright
• [R-sig-ME] mixed effect modeling with imputed data set   ali
  • [R-sig-ME] mixed effect modeling with imputed data set   Daniel Fulop
    • [R-sig-ME] mixed effect modeling with imputed data set   Ben Bolker
      • [R-sig-ME] mixed effect modeling with imputed data set   Ken Beath
• [R-sig-ME] (no subject)   Akanksha Singh
• [R-sig-ME] New paper by Bates et al: Parsimonious mixed models   Shravan Vasishth
  • [R-sig-ME] New paper by Bates et al: Parsimonious mixed models   marKo
  • [R-sig-ME] New paper by Bates et al: Parsimonious mixed models   Martin Maechler
  • [R-sig-ME] New paper by Bates et al: Parsimonious mixed models   Henrik Singmann
    • [R-sig-ME] New paper by Bates et al: Parsimonious mixed models   Jake Westfall
• [R-sig-ME] discrepancies in lme4   Julia Hoffmann
  • [R-sig-ME] discrepancies in lme4   Ben Bolker
• [R-sig-ME] Performing a single animal model that estimates additive genetic variation for many populations?   Jackie Wood
  • [R-sig-ME] Performing a single animal model that estimates additive genetic variation for many populations?   David Duffy
    • [R-sig-ME] Performing a single animal model that estimates additive genetic variation for many populations?   Pierre de Villemereuil
• [R-sig-ME] [R] Proc Mixed variance of random effects in R   Thierry Onkelinx
  • [R-sig-ME] [R] Proc Mixed variance of random effects in R   Ken Beath
• [R-sig-ME] MCMCgmml univariate model - binary response.   Patricia Soto
• [R-sig-ME] MCMCglmm: mix of good and bad eff.samp   rafter sass ferguson
  • [R-sig-ME] MCMCglmm: mix of good and bad eff.samp   rafter sass ferguson
• [R-sig-ME] correct specification and interpretation of multivariate mixed-model in lme   David Villegas Ríos
  • [R-sig-ME] correct specification and interpretation of multivariate mixed-model in lme   Thierry Onkelinx
    • [R-sig-ME] correct specification and interpretation of multivariate mixed-model in lme   David Villegas Ríos
• [R-sig-ME] recover simulated individual-level random effects with glmer?   Ian Carroll
  • [R-sig-ME] recover simulated individual-level random effects with glmer?   Ben Bolker
    • [R-sig-ME] recover simulated individual-level random effects with glmer?   Thierry Onkelinx
• [R-sig-ME] MCMCglmm ignores custom contrasts in multi-response models   rafter sass ferguson
• [R-sig-ME] Specification of binomial mixed model with custom intercept   Tom Wenseleers
  • [R-sig-ME] Specification of binomial mixed model with custom intercept   Jake Westfall
    • [R-sig-ME] Specification of binomial mixed model with custom intercept   Tom Wenseleers
  • [R-sig-ME] Specification of binomial mixed model with custom intercept   Tom Wenseleers
    • [R-sig-ME] Specification of binomial mixed model with custom intercept   Dale Barr
      • [R-sig-ME] Specification of binomial mixed model with custom intercept   Dale Barr
• [R-sig-ME] glmer and lme4 - Quick question   Joseph Maina
  • [R-sig-ME] glmer and lme4 - Quick question   Thierry Onkelinx
  • [R-sig-ME] glmer and lme4 - Quick question   Ken Beath
• [R-sig-ME] lme4 and random effects modelling.   Tom Wilding
  • [R-sig-ME] lme4 and random effects modelling.   Thierry Onkelinx
• [R-sig-ME] convergence issues in multivariate mixed-model in lme   David Villegas Ríos
  • [R-sig-ME] convergence issues in multivariate mixed-model in lme   David Duffy
• [R-sig-ME] comparing random effect variation between data sets   John Morrongiello
  • [R-sig-ME] comparing random effect variation between data sets   Ben Bolker
    • [R-sig-ME] comparing random effect variation between data sets   John Morrongiello
• [R-sig-ME] Dependence of Effect Sizes in meta analysis metafor (some statistical theory questions included)   Drwecki, Brian B
  • [R-sig-ME] Dependence of Effect Sizes in meta analysis metafor (some statistical theory questions included)   Theodore Lytras
    • [R-sig-ME] Dependence of Effect Sizes in meta analysis metafor (some statistical theory questions included)   Mike Cheung
      • [R-sig-ME] Dependence of Effect Sizes in meta analysis metafor (some statistical theory questions included)   Viechtbauer Wolfgang (STAT)
    • [R-sig-ME] Dependence of Effect Sizes in meta analysis metafor (some statistical theory questions included)   Drwecki, Brian B
• [R-sig-ME] trouble specifying cumulative-link mixed model   Dan McCloy
  • [R-sig-ME] trouble specifying cumulative-link mixed model   Paul Johnson
• [R-sig-ME] [R] mixed model ANCOVA   Ian Danilevicz
  • [R-sig-ME] [R] mixed model ANCOVA   Ben Bolker
    • [R-sig-ME] [R] mixed model ANCOVA   Ben Bolker
• [R-sig-ME] constructed level 2 predictors/random effects   Paul Johnson
  • [R-sig-ME] constructed level 2 predictors/random effects   Steven J. Pierce

Last message date: Tue Jun 30 15:11:37 CEST 2015
Archived on: Tue Jun 30 15:11:49 CEST 2015

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