tests/testthat/_snaps/windows/report.survreg.md

In easystats/report: Automated Reporting of Results and Statistical Models

report-survreg

Code
  report(mod_survreg)
Output
  Can't calculate log-loss.
Message
  Can't calculate proper scoring rules for models without integer response
    values.
Output
  `performance_pcp()` only works for models with binary response values.
  Can't calculate log-loss.
Message
  Can't calculate proper scoring rules for models without integer response
    values.
Output
  `performance_pcp()` only works for models with binary response values.
  We fitted a logistic model to predict survival::Surv(futime, fustat) with
  ecog.ps and rx (formula: survival::Surv(futime, fustat) ~ ecog.ps + rx). The
  model's explanatory power is weak (Nagelkerke's R2 = 0.07). The model's
  intercept, corresponding to ecog.ps = 0 and rx = 0, is at 667.43 (95% CI
  [-415.59, 1750.45], p = 0.227). Within this model:

    - The effect of ecog ps is statistically non-significant and negative (beta =
  -210.59, 95% CI [-726.18, 305.01], p = 0.423; Std. beta = -107.06, 95% CI
  [-369.19, 155.07])
    - The effect of rx is statistically non-significant and positive (beta =
  320.10, 95% CI [-194.11, 834.32], p = 0.222; Std. beta = 163.22, 95% CI
  [-98.98, 425.42])
    - The effect of Log(scale) is statistically significant and positive (beta =
  5.82, 95% CI [5.35, 6.29], p < .001; Std. beta = 5.82, 95% CI [5.35, 6.29])

  Standardized parameters were obtained by fitting the model on a standardized
  version of the dataset. 95% Confidence Intervals (CIs) and p-values were
  computed using a Wald z-distribution approximation.

easystats/report documentation built on Feb. 10, 2025, 10:38 a.m.