Summary of Objects of Class "summary.glimML"

Summary of a model of formal class “glimML” fitted by betabin or negbin.

Objects from the Class

Objects can be created by calls of the form new("summary.glimML", ...) or, more commonly, via the summary or show method for objects of formal class “glimML”.

Slots

object

An object of formal class “glimML”.

Coef

A data frame containing the estimates, standard error, z and P values for the fixed-effect coefficients which were estimated by the fitting function.

FixedCoef

A data frame containing the values of the fixed-effect coefficients which were set to a fixed value.

Phi

A data frame containing the estimates, standard error, z and P values for the overdispersion coefficients which were estimated by the fitting function. Because the overdispersion coefficients are \(> 0\), P values correspond to unilateral tests.

FixedPhi

A data frame containing the values of the overdispersion coefficients which were set to a fixed value.

Methods

show

signature(object = "summary.glimML")

show

signature(object = "glimML")

summary

signature(object = "glimML")

Examples

  data(orob2)
  fm1 <- betabin(cbind(y, n - y) ~ seed, ~ 1, data = orob2)
  # show objects of class "glimML"
  fm1
#> Beta-binomial model
#> -------------------
#> betabin(formula = cbind(y, n - y) ~ seed, random = ~1, data = orob2)
#> 
#> Convergence was obtained after 80 iterations.
#> 
#> Fixed-effect coefficients:
#>               Estimate Std. Error   z value Pr(> |z|)
#> (Intercept) -2.602e-01  2.262e-01 -1.15e+00 2.500e-01
#> seedO75      4.130e-01  2.993e-01  1.38e+00 1.676e-01
#> 
#> Overdispersion coefficients:
#>                  Estimate Std. Error   z value  Pr(> z)
#> phi.(Intercept) 7.805e-02  3.015e-02 2.589e+00 4.82e-03
#> 
#> Log-likelihood statistics
#>    Log-lik      nbpar    df res.   Deviance        AIC       AICc 
#> -6.355e+01          3         18  5.051e+01  1.331e+02  1.345e+02 
  # summary for objects of class "glimML"
  res <- summary(fm1)
  res@Coef
#>               Estimate Std. Error   z value Pr(> |z|)
#> (Intercept) -0.2601803  0.2261948 -1.150248 0.2500415
#> seedO75      0.4130146  0.2993271  1.379810 0.1676452
  # show objects of class "summary.glimML"
  res
#> Beta-binomial model
#> -------------------
#> betabin(formula = cbind(y, n - y) ~ seed, random = ~1, data = orob2)
#> 
#> Convergence was obtained after 80 iterations.
#> 
#> Fixed-effect coefficients:
#>               Estimate Std. Error   z value Pr(> |z|)
#> (Intercept) -2.602e-01  2.262e-01 -1.15e+00 2.500e-01
#> seedO75      4.130e-01  2.993e-01  1.38e+00 1.676e-01
#> 
#> Overdispersion coefficients:
#>                  Estimate Std. Error   z value  Pr(> z)
#> phi.(Intercept) 7.805e-02  3.015e-02 2.589e+00 4.82e-03
#> 
#> Log-likelihood statistics
#>    Log-lik      nbpar    df res.   Deviance        AIC       AICc 
#> -6.355e+01          3         18  5.051e+01  1.331e+02  1.345e+02