I am using R to build the random structure of my model but I am ending up with a very complex model. Currently looks like this:

Model <- lmer(x ~ y * z * d * k + (1 + y * z + d | subject), data = Data, REML = FALSE, control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000))) 

I would like to know if I am simply overfitting. How can I get a predictive R-squared for linear mixed effects models? Is there a way to calculate these values?

I am aware of the package MuMIn for getting Rsquared values but I am concerned with overfitting, so I wanted to see if the degrees of freedom are biasing too much the AIC and p-values when comparing the models using anova.

I am using R to build the random structure of my model but I am ending up with a very complex model. Currently looks like this:

Model <- lmer(x ~ y * z * d * k + (1 + y * z + d | subject),  data = Data, REML = FALSE, control = lmerControl(optimizer = "bobyqa",  optCtrl = list(maxfun = 100000))) 

I would like to know if I am simply overfitting. How can I get a predictive R-squared for linear mixed effects models? Is there a way to calculate these values?

I am aware of the package MuMIn for getting Rsquared values but I am concerned with overfitting, so I wanted to see if the degrees of freedom are biasing too much the AIC and p-values when comparing the models using anova.

I am using R to build the random structure of my model but I am ending up with a very complex model. Currently looks like this:

Model <- lmer(x ~ y * z * d * k + (1 + y * z + d | subject), data = Data, REML = FALSE, control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000))) 

I would like to know if I am simply overfitting. How can I get a predicted R-squared for linear mixed effects models? Is there a way to calculate these values?

I am aware of the package MuMIn for getting Rsquared values but I am concerned with overfitting, so I wanted to see if the degrees of freedom are biasing too much the AIC and p-values when comparing the models using anova.

I am using R to build the random structure of my model but I am ending up with a very complex model. Currently looks like this:

Model <- lmer(x ~ y * z * d * k + (1 + y * z + d | subject), data = Data, REML = FALSE, control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000))) 

I would like to know if I am simply overfitting. How can I get a predictive R-squared for linear mixed effects models? Is there a way to calculate these values?

I am aware of the package MuMIn for getting Rsquared values but I am concerned with overfitting, so I wanted to see if the degrees of freedom are biasing too much the AIC and p-values when comparing the models using anova.

I am using R to build the random structure of my model but I am ending up with a very complex model. Currently looks like this:

Model <- lmer(x ~ y*z*d*k + (1 + y*z + d| subject),data= Data, REML=FALSE, control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000))) 

I would like to know if I am simply overfitting. How can I get a predicted R-squared for linear mixed effects models? Is there a way to calculate these values?

I am aware of the package MuMIn for getting Rsquared values but I am concerned with overfitting, so I wanted to see if the degrees of freedom are biasing too much the AIC and p-values when comparing the models using anova.

I am using R to build the random structure of my model but I am ending up with a very complex model. Currently looks like this:

Model <- lmer(x ~ y * z * d * k + (1 + y * z + d | subject), data = Data,  REML = FALSE,  control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000))) 

I would like to know if I am simply overfitting. How can I get a predicted R-squared for linear mixed effects models? Is there a way to calculate these values?

I am aware of the package MuMIn for getting Rsquared values but I am concerned with overfitting, so I wanted to see if the degrees of freedom are biasing too much the AIC and p-values when comparing the models using anova.