Summary Method for mixpoissonreg Objects.

Source: R/2_diagnostic_functions.R

Function providing a summary of results related to mixed Poisson regression models.

# S3 method for mixpoissonreg
summary(object, ...)

Arguments

object

an object of class "mixpoissonreg" containing results from the fitted model.
...

further arguments passed to or from other methods.

Value

An object of class summary_mixpoissonreg containing several informations of a mixpoissonreg object.

See also

plot.mixpoissonreg, autoplot.mixpoissonreg, local_influence_plot.mixpoissonreg, local_influence_autoplot.mixpoissonreg

Examples

# \donttest{
data("Attendance", package = "mixpoissonreg")

daysabs_fit <- mixpoissonreg(daysabs ~ gender + math +
prog | gender + math + prog, data = Attendance)
summary(daysabs_fit)

#> 
#> Negative Binomial Regression - Expectation-Maximization Algorithm
#> 
#> Call:
#> mixpoissonreg(formula = daysabs ~ gender + math + prog | gender + 
#>     math + prog, data = Attendance)
#> 
#> 
#> Pearson residuals:
#>      RSS      Min       1Q   Median       3Q      Max 
#> 322.0142  -1.1751  -0.6992  -0.3600   0.3014   4.7178 
#> 
#> Coefficients modeling the mean (with  link):
#>                 Estimate Std.error z-value Pr(>|z|)    
#> (Intercept)     2.746123  0.147412  18.629  < 2e-16 ***
#> gendermale     -0.245113  0.117967  -2.078  0.03773 *  
#> math           -0.006617  0.002317  -2.856  0.00429 ** 
#> progAcademic   -0.425983  0.132144  -3.224  0.00127 ** 
#> progVocational -1.269755  0.174444  -7.279 3.37e-13 ***
#> 
#> Coefficients modeling the precision (with  link):
#>                 Estimate Std.error z-value Pr(>|z|)    
#> (Intercept)     1.414227  0.343243   4.120 3.79e-05 ***
#> gendermale     -0.208397  0.203692  -1.023 0.306262    
#> math           -0.005123  0.004181  -1.225 0.220458    
#> progAcademic   -1.084418  0.305479  -3.550 0.000385 ***
#> progVocational -1.422051  0.343812  -4.136 3.53e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
#> 
#> Efron's pseudo R-squared:  0.1860886 
#> Number of iterations of the EM algorithm = 11

daysabs_fit_ml <- mixpoissonregML(daysabs ~ gender + math +
prog | gender + math + prog, data = Attendance)
summary(daysabs_fit_ml)

#> 
#> Negative Binomial Regression - Maximum-Likelihood Estimation
#> 
#> Call:
#> mixpoissonregML(formula = daysabs ~ gender + math + prog | gender + 
#>     math + prog, data = Attendance)
#> 
#> 
#> Pearson residuals:
#>      RSS      Min       1Q   Median       3Q      Max 
#> 322.0172  -1.1751  -0.6992  -0.3600   0.3014   4.7178 
#> 
#> Coefficients modeling the mean (with  link):
#>                 Estimate Std.error z-value Pr(>|z|)    
#> (Intercept)     2.746123  0.147412  18.629  < 2e-16 ***
#> gendermale     -0.245113  0.117967  -2.078  0.03773 *  
#> math           -0.006617  0.002317  -2.856  0.00429 ** 
#> progAcademic   -0.425983  0.132144  -3.224  0.00127 ** 
#> progVocational -1.269755  0.174444  -7.279 3.37e-13 ***
#> 
#> Coefficients modeling the precision (with  link):
#>                 Estimate Std.error z-value Pr(>|z|)    
#> (Intercept)     1.414227  0.343243   4.120 3.79e-05 ***
#> gendermale     -0.208397  0.203692  -1.023 0.306262    
#> math           -0.005123  0.004181  -1.225 0.220457    
#> progAcademic   -1.084418  0.305479  -3.550 0.000385 ***
#> progVocational -1.422051  0.343811  -4.136 3.53e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
#> 
#> Efron's pseudo R-squared:  0.1860887 
#> Number of function calls by 'optim' = 4
# }

daysabs_prog <- mixpoissonreg(daysabs ~ prog, data = Attendance)
summary(daysabs_prog)

#> 
#> Negative Binomial Regression - Expectation-Maximization Algorithm
#> 
#> Call:
#> mixpoissonreg(formula = daysabs ~ prog, data = Attendance)
#> 
#> 
#> Pearson residuals:
#>      RSS      Min       1Q   Median       3Q      Max 
#> 340.4808  -0.9371  -0.6885  -0.2614   0.4086   5.2215 
#> 
#> Coefficients modeling the mean (with  link):
#>                Estimate Std.error z-value Pr(>|z|)    
#> (Intercept)      2.3656    0.1650  14.341  < 2e-16 ***
#> progAcademic    -0.4291    0.1845  -2.326     0.02 *  
#> progVocational  -1.3824    0.2000  -6.912 4.78e-12 ***
#> 
#> Coefficients modeling the precision (with  link):
#>             Estimate Std.error z-value Pr(>|z|)    
#> (Intercept)   1.0056    0.1023   9.832   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
#> 
#> Efron's pseudo R-squared:  0.1415831 
#> Number of iterations of the EM algorithm = 1