update (i.e., update.cmp) will update and (by-default) re-fit a model. It is
identical to update in the stats package.
# S3 method for cmp
update(object, formula., formula_nu., ..., evaluate = TRUE)an object class 'cmp', obtained from a call to glm.cmp.
changes to the existing formula in object -- see update.formula
changes to the existing formula_nu in object -- see update.formula for details. It also accepts NULL to not regressing on the dispersion.
other arguments passed to or from other methods (currently unused).
logical; if TRUE evaluate the new call otherwise simply return
the call
# To update the mean regression formula
data(takeoverbids)
## Fit full model
M.bids.full <- glm.cmp(numbids ~ leglrest + rearest + finrest + whtknght
+ bidprem + insthold + size + sizesq + regulatn, data = takeoverbids)
M.bids.full
#>
#> Call: glm.cmp(formula = numbids ~ leglrest + rearest + finrest + whtknght +
#> bidprem + insthold + size + sizesq + regulatn, data = takeoverbids)
#>
#> Linear Model Coefficients:
#> (Intercept) leglrest rearest finrest whtknght bidprem
#> 0.9896300 0.2678800 -0.1731800 0.0677440 0.4812800 -0.6848200
#> insthold size sizesq regulatn
#> -0.3678900 0.1793300 -0.0075823 -0.0375690
#>
#> Dispersion (nu): 1.75
#> Degrees of Freedom: 125 Total (i.e. Null); 116 Residual
#> Null Deviance: 182.3906
#> Residual Deviance:
#> AIC: 382.1753
#>
## Dropping whtknght
M.bids.null <- update(M.bids.full, . ~ . - whtknght)
M.bids.null
#>
#> Call: glm.cmp(formula = numbids ~ leglrest + rearest + finrest + bidprem +
#> insthold + size + sizesq + regulatn, data = takeoverbids)
#>
#> Linear Model Coefficients:
#> (Intercept) leglrest rearest finrest bidprem insthold
#> 1.3344000 0.3912400 -0.1464000 0.1342300 -0.7477300 -0.4406100
#> size sizesq regulatn
#> 0.1878000 -0.0079426 -0.0445510
#>
#> Dispersion (nu): 1.54
#> Degrees of Freedom: 125 Total (i.e. Null); 117 Residual
#> Null Deviance: 164.7208
#> Residual Deviance:
#> AIC: 393.8799
#>
## To update the dispersion regression formula
data(sitophilus)
## Fit full model
M.sit.full <- glm.cmp(formula = ninsect ~ extract, formula_nu = ~extract, data = sitophilus)
M.sit.full
#>
#> Call: glm.cmp(formula = ninsect ~ extract, formula_nu = ~extract, data = sitophilus)
#>
#> Mean Model Coefficients:
#> (Intercept) extractLeaf extractBranch extractSeed
#> 3.45000 -0.00637 -0.05210 -3.35000
#>
#> Dispersion Model Coefficients:
#> (Intercept) extractLeaf extractBranch extractSeed
#> -0.665 -0.383 -0.372 -0.118
#>
#> Degrees of Freedom: 39 Total (i.e. Null); 32 Residual
#> Null Deviance: 257.2109
#> Residual Deviance:
#> AIC: 260.8279
#>
## Dropping extract from the dispersion regression
M.sit.null1 <- update(M.sit.full, formula_nu = ~ . - extract)
M.sit.null1
#>
#> Call: glm.cmp(formula = ninsect ~ extract, formula_nu = ~1, data = sitophilus)
#>
#> Mean Model Coefficients:
#> (Intercept) extractLeaf extractBranch extractSeed
#> 3.45000 -0.00637 -0.05210 -3.35000
#>
#> Dispersion Model Coefficients:
#> (Intercept)
#> -0.927
#>
#> Degrees of Freedom: 39 Total (i.e. Null); 35 Residual
#> Null Deviance: 234.4692
#> Residual Deviance:
#> AIC: 255.2668
#>
## To not regress on the dispersion at all
M.sit.null2 <- update(M.sit.full, formula_nu = NULL)
M.sit.null2
#>
#> Call: glm.cmp(formula = ninsect ~ extract, data = sitophilus)
#>
#> Linear Model Coefficients:
#> (Intercept) extractLeaf extractBranch extractSeed
#> 3.4500000 -0.0063694 -0.0521290 -3.3547000
#>
#> Dispersion (nu): 0.396
#> Degrees of Freedom: 39 Total (i.e. Null); 36 Residual
#> Null Deviance: 234.4696
#> Residual Deviance:
#> AIC: 253.2668
#>