我按如下方式运行了逻辑回归，并且可以通过直接运行模型对象或通过摘要从模型对象中获取系数。但是，尝试对 tbl_regression() 函数执行相同操作会引发错误。我不知道如何解决这个问题，任何帮助将不胜感激。
library(gtsummary)
load("C:/Users/Public/Documents/mio_ivor1_imp.RDA")
logist_30dmort_adj <- glm(mortality_30day ~ approach_012 + age + female1 + asa_score + neoadjuvant_analys + cstage_analys + ope_yr_analys + hospital_vol_analys, mio_ivor1_imp, family = binomial)
summary(logist_30dmort_adj)
#>
#> Call:
#> glm(formula = mortality_30day ~ approach_012 + age + female1 +
#> asa_score + neoadjuvant_analys + cstage_analys + ope_yr_analys +
#> hospital_vol_analys, family = binomial, data = mio_ivor1_imp)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.15235 -0.19589 -0.11796 -0.06798 3.09348
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -11.70437 521.81937 -0.022 0.98211
#> approach_0121 -0.30851 0.68431 -0.451 0.65211
#> approach_0122 -0.67283 0.86303 -0.780 0.43562
#> age 0.08611 0.03185 2.703 0.00686 **
#> female11 -0.94071 0.75947 -1.239 0.21548
#> asa_score.L 1.99191 0.92262 2.159 0.03085 *
#> asa_score.Q -0.18094 0.71959 -0.251 0.80146
#> asa_score.C -0.42744 0.44134 -0.969 0.33279
#> neoadjuvant_analys1 0.40230 0.72689 0.553 0.57995
#> neoadjuvant_analys2 0.43587 0.58262 0.748 0.45438
#> cstage_analys.L 0.66557 0.65909 1.010 0.31258
#> cstage_analys.Q 0.18363 0.52716 0.348 0.72758
#> cstage_analys.C 0.33085 0.39319 0.841 0.40010
#> ope_yr_analys.L -5.23155 3119.87967 -0.002 0.99866
#> ope_yr_analys.Q -2.24941 3500.05087 -0.001 0.99949
#> ope_yr_analys.C -8.99084 3320.60681 -0.003 0.99784
#> ope_yr_analys^4 -10.49249 2820.35758 -0.004 0.99703
#> ope_yr_analys^5 -2.78503 2138.26572 -0.001 0.99896
#> ope_yr_analys^6 0.89335 1493.93098 0.001 0.99952
#> ope_yr_analys^7 -4.02858 926.49686 -0.004 0.99653
#> ope_yr_analys^8 -5.46683 570.70847 -0.010 0.99236
#> ope_yr_analys^9 3.70191 342.06586 0.011 0.99137
#> ope_yr_analys^10 3.12888 263.79515 0.012 0.99054
#> ope_yr_analys^11 -5.76733 322.81793 -0.018 0.98575
#> ope_yr_analys^12 -3.05926 201.09534 -0.015 0.98786
#> ope_yr_analys^13 7.58505 522.03343 0.015 0.98841
#> hospital_vol_analys.L -0.65262 0.39766 -1.641 0.10077
#> hospital_vol_analys.Q 0.25345 0.42534 0.596 0.55126
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 265.43 on 1367 degrees of freedom
#> Residual deviance: 213.11 on 1340 degrees of freedom
#> (36 observations deleted due to missingness)
#> AIC: 269.11
#>
#> Number of Fisher Scoring iterations: 18
tbl_regression(logist_30dmort_adj, exponentiate = TRUE)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: algorithm did not converge
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> x There was an error calling `tidy_fun()`. Most likely, this is because
#> the function supplied in `tidy_fun=` was misspelled, does not exist,
#> is not compatible with your object, or was missing necessary arguments (e.g.
#> `conf.level=` or `conf.int=`). See error message below.
#> Error: Error in profile.glm(object, which = parm, alpha = (1 - level)/4, trace = trace): profiling has found a better solution, so original fit had not converged
由reprex 包于 2021-02-12 创建(v1.0.0)