Summarize relevant results from a selmodel object.
Usage
# S3 method for class 'selmodel'
summary(object, transf_gamma = TRUE, transf_zeta = TRUE, digits = 3, ...)Arguments
- object
Fitted model of class
"selmodel".- transf_gamma
logical with
TRUE(the default) indicating that the heterogeneity parameter estimates (called gamma) should be transformed by exponentiating.- transf_zeta
logical with
TRUE(the default) indicating that the selection parameter estimates (called zeta) should be transformed by exponentiating.- digits
Minimum number of significant digits to be used, with a default of 3.
- ...
further arguments passed to
print.data.frame().
Examples
res_ML <- selection_model(
data = self_control,
yi = g,
sei = se_g,
cluster = studyid,
steps = 0.025,
estimator = "CML",
bootstrap = "none"
)
summary(res_ML)
#> Step Function Model
#>
#> Call:
#> selection_model(data = self_control, yi = g, sei = se_g, cluster = studyid,
#> steps = 0.025, estimator = "CML", bootstrap = "none")
#>
#> Number of clusters = 33; Number of effects = 158
#>
#> Steps: 0.025
#> Estimator: composite marginal likelihood
#> Variance estimator: robust
#>
#> Log composite likelihood of selection model: -54.97404
#> Inverse selection weighted partial log likelihood: 87.11211
#>
#> Mean effect estimates:
#> Large Sample
#> Coef. Estimate Std. Error p-value Lower Upper
#> beta 0.22 0.0525 2.85e-05 0.117 0.322
#>
#> Heterogeneity estimates:
#> Large Sample
#> Coef. Estimate Std. Error p-value Lower Upper
#> tau2 0.0394 0.0286 --- 0.00951 0.163
#>
#> Selection process estimates:
#> Step: 0 < p <= 0.025; Studies: 18; Effects: 32
#> Large Sample
#> Coef. Estimate Std. Error p-value Lower Upper
#> lambda0 1 --- --- --- ---
#>
#> Step: 0.025 < p <= 1; Studies: 24; Effects: 126
#> Large Sample
#> Coef. Estimate Std. Error p-value Lower Upper
#> lambda1 1.03 0.506 0.944 0.397 2.7
summary(res_ML, transf_gamma = FALSE, transf_zeta = FALSE)
#> Step Function Model
#>
#> Call:
#> selection_model(data = self_control, yi = g, sei = se_g, cluster = studyid,
#> steps = 0.025, estimator = "CML", bootstrap = "none")
#>
#> Number of clusters = 33; Number of effects = 158
#>
#> Steps: 0.025
#> Estimator: composite marginal likelihood
#> Variance estimator: robust
#>
#> Log composite likelihood of selection model: -54.97404
#> Inverse selection weighted partial log likelihood: 87.11211
#>
#> Mean effect estimates:
#> Large Sample
#> Coef. Estimate Std. Error p-value Lower Upper
#> beta 0.22 0.0525 2.85e-05 0.117 0.322
#>
#> Heterogeneity estimates:
#> Large Sample
#> Coef. Estimate Std. Error p-value Lower Upper
#> gamma -3.23 0.725 --- -4.66 -1.81
#>
#> Selection process estimates:
#> Step: 0 < p <= 0.025; Studies: 18; Effects: 32
#> Large Sample
#> Coef. Estimate Std. Error p-value Lower Upper
#> zeta0 0 --- --- --- ---
#>
#> Step: 0.025 < p <= 1; Studies: 24; Effects: 126
#> Large Sample
#> Coef. Estimate Std. Error p-value Lower Upper
#> zeta1 0.0344 0.489 0.944 -0.924 0.992