r语言 - 使用or_glm()函数调整的优势比?

Question

我使用or_glm()来计算优势比，使用以下可重复的示例:

library(oddsratio)
or_glm(data = data_glm, 
model = glm(admit ~ gre + gpa + rank, 
data = data_glm, 
family = "binomial"), 
incr = list(gre = 1, gpa = 1, rank = 1))

我有两个问题:

1. 我如何也提取每个优势比的p值?
2. 我怎样才能得到"伟大"的比值比呢?调整为"gpa"one_answers"rank" ?

Answer 1

我会尝试如下:

library(oddsratio)
library(mfx)
model = glm(admit ~ gre + gpa + rank, 
data = data_glm, 
family = "binomial")
logitor(admit ~ gre + gpa + rank,data=data_glm)
Call:
logitor(formula = admit ~ gre + gpa + rank, data = data_glm)
Odds Ratio:
OddsRatio Std. Err.       z     P>|z|    
gre   1.0022670 0.0010965  2.0699 0.0384651 *  
gpa   2.2345448 0.7414651  2.4231 0.0153879 *  
rank2 0.5089310 0.1610714 -2.1342 0.0328288 *  
rank3 0.2617923 0.0903986 -3.8812 0.0001039 ***
rank4 0.2119375 0.0885542 -3.7131 0.0002047 ***
---
Signif. codes:  
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef(model))
(Intercept)         gre         gpa       rank2 
0.0185001   1.0022670   2.2345448   0.5089310 
rank3       rank4 
0.2617923   0.2119375 
exp(cbind(OR=coef(model), confint(model)))
Waiting for profiling to be done...
OR       2.5 %    97.5 %
(Intercept) 0.0185001 0.001889165 0.1665354
gre         1.0022670 1.000137602 1.0044457
gpa         2.2345448 1.173858216 4.3238349
rank2       0.5089310 0.272289674 0.9448343
rank3       0.2617923 0.131641717 0.5115181
rank4       0.2119375 0.090715546 0.4706961

Answer 2

逻辑回归(即二项glm)的系数只是优势比的自然对数，所以你通过做exp(coefficient)得到优势比。同样，95%置信区间是系数的指数+/- 1.96 *标准误差。比值比的p值已经在系数表中给出了，这些值根本不需要修改。

因此自己制作表格很容易。让我们从模型中获取系数表开始:

library(oddsratio)
mod <- glm(admit ~ gre + gpa + rank, 
data = data_glm, 
family = "binomial")
tab <- summary(mod)$coef
tab
#>                 Estimate  Std. Error   z value     Pr(>|z|)
#> (Intercept) -3.989979073 1.139950936 -3.500132 0.0004650273
#> gre          0.002264426 0.001093998  2.069864 0.0384651284
#> gpa          0.804037549 0.331819298  2.423119 0.0153878974
#> rank2       -0.675442928 0.316489661 -2.134171 0.0328288188
#> rank3       -1.340203916 0.345306418 -3.881202 0.0001039415
#> rank4       -1.551463677 0.417831633 -3.713131 0.0002047107

我们可以这样制作比值比表:

data.frame(variable = rownames(tab),
oddsratio = round(exp(tab[,1]), 3),
ci_low = round(exp(tab[,1] - 1.96 * tab[,2]), 3),
ci_high = round(exp(tab[,1] + 1.96 * tab[,2]), 3),
pval = scales::pvalue(tab[,4]),
row.names = NULL)[-1,]
#>   variable oddsratio ci_low ci_high   pval
#> 2      gre     1.002  1.000   1.004  0.038
#> 3      gpa     2.235  1.166   4.282  0.015
#> 4    rank2     0.509  0.274   0.946  0.033
#> 5    rank3     0.262  0.133   0.515 <0.001
#> 6    rank4     0.212  0.093   0.481 <0.001

比较or_glm函数的输出:

#>   predictor oddsratio ci_low (2.5) ci_high (97.5)          increment
#> 1       gre     1.002        1.000          1.004                  1
#> 2       gpa     2.235        1.174          4.324                  1
#> 3     rank2     0.509        0.272          0.945 Indicator variable
#> 4     rank3     0.262        0.132          0.512 Indicator variable
#> 5     rank4     0.212        0.091          0.471 Indicator variable

^{由reprex包(v2.0.1)创建于2022-06-05}