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Moe
  • 141
  • 7

You may be able to use this function attributed to D. Bates to get the ODscale parameter:

dispersion_glmer<- function(modelglmer)
{   
# computing  estimated scale  ( binomial model)
#following  D. Bates :
#That quantity is the square root of the penalized residual sum of
#squares divided by n, the number of observations, evaluated as:
    
    n <- length(modelglmer@resid)
    
    return(  sqrt( sum(c(modelglmer@resid, modelglmer@u) ^2) / n ) )
}

This is a link to more information on the scale parameter.

You may be able to use this function attributed to D. Bates to get the OD parameter:

dispersion_glmer<- function(modelglmer)
{   
# computing  estimated scale  ( binomial model)
#following  D. Bates :
#That quantity is the square root of the penalized residual sum of
#squares divided by n, the number of observations, evaluated as:
    
    n <- length(modelglmer@resid)
    
    return(  sqrt( sum(c(modelglmer@resid, modelglmer@u) ^2) / n ) )
}

You may be able to use this function attributed to D. Bates to get the scale parameter:

dispersion_glmer<- function(modelglmer)
{   
# computing  estimated scale  ( binomial model)
#following  D. Bates :
#That quantity is the square root of the penalized residual sum of
#squares divided by n, the number of observations, evaluated as:
    
    n <- length(modelglmer@resid)
    
    return(  sqrt( sum(c(modelglmer@resid, modelglmer@u) ^2) / n ) )
}

This is a link to more information on the scale parameter.

Source Link
Moe
  • 141
  • 7

You may be able to use this function attributed to D. Bates to get the OD parameter:

dispersion_glmer<- function(modelglmer)
{   
# computing  estimated scale  ( binomial model)
#following  D. Bates :
#That quantity is the square root of the penalized residual sum of
#squares divided by n, the number of observations, evaluated as:
    
    n <- length(modelglmer@resid)
    
    return(  sqrt( sum(c(modelglmer@resid, modelglmer@u) ^2) / n ) )
}