作为创建多变量逻辑回归的准备工作,我正在进行单变量回归并希望选择 p < 0.20 的变量以包含在多变量模型中。我可以将所需的变量映射到glm
并获得模型的输出,但我正在努力按 p 值的等级对它们进行排序。
这是我到目前为止:
predictor1 <- c(0,1.1,2.4,3.1,4.0,5.9,4.2,3.3,2.2,1.1)
predictor2 <- as.factor(c("yes","no","no","yes","yes","no","no","yes","no","no"))
predictor3 <- as.factor(c("a", "b", "c", "c", "a", "c", "a", "a", "a", "c"))
outcome <- as.factor(c("alive","dead","alive","dead","alive","dead","alive","dead","alive","dead"))
df <- data.frame(pred1 = predictor1, pred2 = predictor2, pred3 = predictor3, outcome = outcome)
predictors <- c("pred1", "pred2", "pred3")
df %>%
select(predictors) %>%
map(~ glm(df$outcome ~ .x, data = df, family = "binomial")) %>%
#Extract odds ratio, confidence interval lower and upper bounds, and p value
map(function (x, y) data.frame(OR = exp(coef(x)),
lower=exp(confint(x)[,1]),
upper=exp(confint(x)[,2]),
Pval = coef(summary(x))[,4]))
此代码吐出每个模型的摘要
$pred1
OR lower upper Pval
(Intercept) 0.711082 0.04841674 8.521697 0.7818212
.x 1.133085 0.52179227 2.653040 0.7465663
$pred2
OR lower upper Pval
(Intercept) 1 0.18507173 5.40331 1
.xyes 1 0.07220425 13.84960 1
$pred3
OR lower upper Pval
(Intercept) 0.25 0.0127798 1.689944 0.2149978
.xb 170179249.43 0.0000000 NA 0.9961777
.xc 12.00 0.6908931 542.678010 0.1220957
但是对于我的真实数据集,有几十个预测变量,所以我需要一种对输出进行排序的方法。最好通过每个模型中的最小(非截距)p 值。也许我为每个模型的摘要选择的数据结构不是最好的,所以任何关于如何在更灵活的数据结构中获取相同信息的建议也会很好。
使用map_dfr
而不是map
,用截距过滤行然后做arrange
。使用tidy
frombroom
而不是您的自定义函数。
library(broom)
df %>%
select(predictors) %>%
map(~ glm(df$outcome ~ .x, data = df, family = "binomial")) %>%
map_dfr(tidy, .id='Model') %>%
filter(term!="(Intercept)") %>% arrange(p.value)
# A tibble: 4 x 6
Model term estimate std.error statistic p.value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 pred3 .xc 2.48e+ 0 1.61 1.55e+ 0 0.122
2 pred1 .x 1.25e- 1 0.387 3.23e- 1 0.747
3 pred3 .xb 1.90e+ 1 3956. 4.79e- 3 0.996
4 pred2 .xyes -5.73e-16 1.29 -4.44e-16 1.000
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