R broom tidy.ergm 整理 a(n) ergm 对象

# S3 method for ergm
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

tidy() , ergm::ergm() , ergm::control.ergm() , ergm::summary()

其他 ergm 整理器：glance.ergm()

# load libraries for models and data
library(ergm)
#> 
#> ‘ergm’ 4.5.0 (2023-05-27), part of the Statnet Project
#> * ‘news(package="ergm")’ for changes since last version
#> * ‘citation("ergm")’ for citation information
#> * ‘https://statnet.org’ for help, support, and other information
#> ‘ergm’ 4 is a major update that introduces some
#> backwards-incompatible changes. Please type
#> ‘news(package="ergm")’ for a list of major changes.
#> 
#> Attaching package: ‘ergm’
#> The following object is masked from ‘package:btergm’:
#> 
#>     gof

# load the Florentine marriage network data
data(florentine)

# fit a model where the propensity to form ties between
# families depends on the absolute difference in wealth
gest <- ergm(flomarriage ~ edges + absdiff("wealth"))
#> Starting maximum pseudolikelihood estimation (MPLE):
#> Obtaining the responsible dyads.
#> Evaluating the predictor and response matrix.
#> Maximizing the pseudolikelihood.
#> Finished MPLE.
#> Evaluating log-likelihood at the estimate. 
#> 

# show terms, coefficient estimates and errors
tidy(gest)
#> # A tibble: 2 × 6
#>   term           estimate std.error mcmc.error statistic      p.value
#>   <chr>             <dbl>     <dbl>      <dbl>     <dbl>        <dbl>
#> 1 edges           -2.30     0.402            0     -5.73 0.0000000102
#> 2 absdiff.wealth   0.0155   0.00616          0      2.52 0.0117      

# show coefficients as odds ratios with a 99% CI
tidy(gest, exponentiate = TRUE, conf.int = TRUE, conf.level = 0.99)
#> Warning: Exponentiating but model didn't use log or logit link.
#> # A tibble: 2 × 8
#>   term  estimate std.error mcmc.error statistic p.value conf.low conf.high
#>   <chr>    <dbl>     <dbl>      <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 edges    0.100   0.402            0     -5.73 1.02e-8   0.0355     0.282
#> 2 absd…    1.02    0.00616          0      2.52 1.17e-2   1.00       1.03 

# take a look at likelihood measures and other
# control parameters used during MCMC estimation
glance(gest)
#> # A tibble: 1 × 5
#>   independence iterations logLik   AIC   BIC
#>   <lgl>             <int>  <dbl> <dbl> <dbl>
#> 1 TRUE                  4  -51.0  106.  112.
glance(gest, deviance = TRUE)
#> # A tibble: 1 × 9
#>   independence iterations logLik null.deviance df.null residual.deviance
#>   <lgl>             <int>  <dbl> <logLik>        <int>             <dbl>
#> 1 TRUE                  4  -51.0 166.3553          120              102.
#> # ℹ 3 more variables: df.residual <int>, AIC <dbl>, BIC <dbl>
glance(gest, mcmc = TRUE)
#> Though `glance` was supplied `mcmc = TRUE`, the model was not fittedusing MCMC, so the corresponding columns will be omitted.
#> # A tibble: 1 × 5
#>   independence iterations logLik   AIC   BIC
#>   <lgl>             <int>  <dbl> <dbl> <dbl>
#> 1 TRUE                  4  -51.0  106.  112.