Hi everybody, I am trying to fit a model with the lmer function for mixed effects. I have an experimental design consisting of 5 field plots. Each plot is divided in 12 subplots where the influence of three factors on the growing of tree seedlings is tested: (1) seed (1 = presence; 0 = absence); (2) seedling species (oak holm vs. pine); (3) treatment (three different treatments). In each of these subplots we planted 13 seedlings. Therefore I would have a model with three fixed factors and one random factor (a block?). If I´m not wrong the model would be as follows: model2 <- lmer(Growing ~ Seed + Species + Treatment +(Seed + Species + Treatment|Block), data) My first question is: if the three fixed factors occur within-subjects (considering the plot as a subject), is the model correctly defined (assuming no interactions)? Should I specify the model in some other way? I second problem I had is that the factors are not crossed because some of the seedling died during the experiment. This means that some factors are nested. Specifically Species is nested within Seed and Block would be nested within Treatment. I have tried to use the %in% specification for nested designs but it does not work. model2 <- lmer(Growing ~ Seed + Species%in%Seed + Treatment +(Seed + Species + Treatment|Block%in%Treatment), data) I get the following error: Error en lmer(Growing ~ Seed + Species %in% Seed + Treatment + : .. Leading minor of order 5 in downdated X'X is not positive definite I would appreciate some help to fit this model. Thanks to everybody, Luis Luis Cayuela Departamento de Ecología Universidad de Alcalá Crta. de Barcelona km. 33,600 E-28871 Alcalá de Henares Madrid España Tlf: (+0034) 918856407 Fax: (+0034) 918854929 [[alternative HTML version deleted]]
On Thu, May 15, 2008 at 9:22 AM, Luis Cayuela <luis.cayuela at uah.es> wrote:> Hi everybody,> I am trying to fit a model with the lmer function for mixed effects. I have an experimental design consisting of 5 field plots. Each plot is divided in 12 subplots where the influence of three factors on the growing of tree seedlings is tested: (1) seed (1 = presence; 0 = absence); (2) seedling species (oak holm vs. pine); (3) treatment (three different treatments). In each of these subplots we planted 13 seedlings. Therefore I would have a model with three fixed factors and one random factor (a block?). If I?m not wrong the model would be as follows:> model2 <- lmer(Growing ~ Seed + Species + Treatment +(Seed + Species + Treatment|Block), data)That's unlikely. This specification would fit 5 fixed effects parameters and 5, possibly correlated, random effects for each level of the Block factor. This would require estimating a total of 15 variance-covariance parameters for the random effects from the 5 blocks. Can you indicate how many random effects you expect to obtain and how many variance-covariance parameters would be involved? For example, a model with a simple random effect would be expressed as lmer(Growing ~ Seed + Species + Treatment + (1|Block), data) and would involve estimating the 5 fixed effects and one variance for the random effects.> My first question is: if the three fixed factors occur within-subjects (considering the plot as a subject), is the model correctly defined (assuming no interactions)? Should I specify the model in some other way? > > > > I second problem I had is that the factors are not crossed because some of the seedling died during the experiment. This means that some factors are nested. Specifically Species is nested within Seed and Block would be nested within Treatment. I have tried to use the %in% specification for nested designs but it does not work. > > > > model2 <- lmer(Growing ~ Seed + Species%in%Seed + Treatment +(Seed + Species + Treatment|Block%in%Treatment), data) > > > > I get the following error: > > > > Error en lmer(Growing ~ Seed + Species %in% Seed + Treatment + : .. > > Leading minor of order 5 in downdated X'X is not positive definite > > > > I would appreciate some help to fit this model. > > > Thanks to everybody, > > Luis > > Luis Cayuela > Departamento de Ecolog?a > Universidad de Alcal? > Crta. de Barcelona km. 33,600 > E-28871 Alcal? de Henares > Madrid > Espa?a > Tlf: (+0034) 918856407 > Fax: (+0034) 918854929 > [[alternative HTML version deleted]] > > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > >
Thanks for the help provided to fit the model. I still have two questions: 1) What is the syntax for nested fixed and random factors. I have tried using the %in% operator but it does not work. The model I want to fit would be as follow: lmer 1 <- lmer(Growing ~ Seed + Species%in%Seed + Treatment + (1|Block%in%Treatment), data) 2) Seed, Species and Treatment are intra-subject factors. This means that an ANOVA of repeated measure should apply here. How should this be specified in the model? Or should I assume that this is incorporated by specifying the Block as a random factor? All the best, Luis Luis Cayuela Departamento de Ecolog?a Universidad de Alcal? Crta. de Barcelona km. 33,600 E-28871 Alcal? de Henares Madrid Espa?a Tlf: (+0034) 918856407 Fax: (+0034) 918854929 ----- Original Message ----- From: "Douglas Bates" <bates at stat.wisc.edu> To: "Luis Cayuela" <luis.cayuela at uah.es> Cc: <r-help at r-project.org> Sent: Thursday, May 15, 2008 5:22 PM Subject: Re: [R] mixed effects models with nested factors On Thu, May 15, 2008 at 9:22 AM, Luis Cayuela <luis.cayuela at uah.es> wrote:> Hi everybody,> I am trying to fit a model with the lmer function for mixed effects. I > have an experimental design consisting of 5 field plots. Each plot is > divided in 12 subplots where the influence of three factors on the growing > of tree seedlings is tested: (1) seed (1 = presence; 0 = absence); (2) > seedling species (oak holm vs. pine); (3) treatment (three different > treatments). In each of these subplots we planted 13 seedlings. Therefore > I would have a model with three fixed factors and one random factor (a > block?). If I?m not wrong the model would be as follows:> model2 <- lmer(Growing ~ Seed + Species + Treatment +(Seed + Species + > Treatment|Block), data)That's unlikely. This specification would fit 5 fixed effects parameters and 5, possibly correlated, random effects for each level of the Block factor. This would require estimating a total of 15 variance-covariance parameters for the random effects from the 5 blocks. Can you indicate how many random effects you expect to obtain and how many variance-covariance parameters would be involved? For example, a model with a simple random effect would be expressed as lmer(Growing ~ Seed + Species + Treatment + (1|Block), data) and would involve estimating the 5 fixed effects and one variance for the random effects.> My first question is: if the three fixed factors occur within-subjects > (considering the plot as a subject), is the model correctly defined > (assuming no interactions)? Should I specify the model in some other way? > > > > I second problem I had is that the factors are not crossed because some of > the seedling died during the experiment. This means that some factors are > nested. Specifically Species is nested within Seed and Block would be > nested within Treatment. I have tried to use the %in% specification for > nested designs but it does not work. > > > > model2 <- lmer(Growing ~ Seed + Species%in%Seed + Treatment +(Seed + > Species + Treatment|Block%in%Treatment), data) > > > > I get the following error: > > > > Error en lmer(Growing ~ Seed + Species %in% Seed + Treatment + : .. > > Leading minor of order 5 in downdated X'X is not positive definite > > > > I would appreciate some help to fit this model. > > > Thanks to everybody, > > Luis