如何将列中的值转换为“是”和“不” R 中多元回归的值

Question

我正在使用以下可重现的数据集进行多元线性回归（这是我的数据的一小部分样本）：

structure(list(age = c(62.84998, 60.33899, 52.74698, 42.38498, 
 79.88495, 93.01599, 62.37097, 86.83899, 85.65594, 42.25897), 
     death = c(0, 1, 1, 1, 0, 1, 1, 1, 1, 1), sex = c("male", 
     "female", "female", "female", "female", "male", "male", "male", 
     "male", "female"), hospdead = c(0, 1, 0, 0, 0, 1, 0, 0, 0, 
     0), slos = c(5, 4, 17, 3, 16, 4, 9, 7, 12, 8), d.time = c(2029, 
     4, 47, 133, 2029, 4, 659, 142, 63, 370), dzgroup = c("Lung Cancer", 
     "Cirrhosis", "Cirrhosis", "Lung Cancer", "ARF/MOSF w/Sepsis", 
     "Coma", "CHF", "CHF", "Lung Cancer", "Colon Cancer"), dzclass = c("Cancer", 
     "COPD/CHF/Cirrhosis", "COPD/CHF/Cirrhosis", "Cancer", "ARF/MOSF", 
     "Coma", "COPD/CHF/Cirrhosis", "COPD/CHF/Cirrhosis", "Cancer", 
     "Cancer"), num.co = c(0, 2, 2, 2, 1, 1, 1, 3, 2, 0), edu = c(11, 
     12, 12, 11, NA, 14, 14, NA, 12, 11), income = c("$11-$25k", 
     "$11-$25k", "under $11k", "under $11k", NA, NA, "$25-$50k", 
     NA, NA, "$25-$50k"), scoma = c(0, 44, 0, 0, 26, 55, 0, 26, 
     26, 0), charges = c(9715, 34496, 41094, 3075, 50127, 6884, 
     30460, 30460, NA, 9914), totcst = c(NA_real_, NA_real_, NA_real_, 
     NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
     NA_real_), totmcst = c(NA_real_, NA_real_, NA_real_, NA_real_, 
     NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_
     ), avtisst = c(7, 29, 13, 7, 18.666656, 5, 8, 6.5, 8.5, 8
     ), race = c("other", "white", "white", "white", "white", 
     "white", "white", "white", "black", "hispanic"), sps = c(33.8984375, 
     52.6953125, 20.5, 20.0976562, 23.5, 19.3984375, 17.296875, 
     21.5976562, 15.8984375, 2.2998047), aps = c(20, 74, 45, 19, 
     30, 27, 46, 53, 17, 9), surv2m = c(0.262939453, 0.0009999275, 
     0.790893555, 0.698974609, 0.634887695, 0.284973145, 0.892944336, 
     0.670898438, 0.570922852, 0.952880859), surv6m = c(0.0369949341, 
     0, 0.664916992, 0.411987305, 0.532958984, 0.214996338, 0.820922852, 
     0.498962402, 0.24899292, 0.887939453), hday = c(1, 3, 4, 
     1, 3, 1, 1, 1, 1, 1), diabetes = c(0, 0, 0, 0, 0, 0, 0, 1, 
     0, 0), dementia = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0), ca = c("metastatic", 
     "no", "no", "metastatic", "no", "no", "no", "no", "metastatic", 
     "metastatic"), prg2m = c(0.5, 0, 0.75, 0.899999619, 0.899999619, 
     0, NA, 0.799999714, 0.049999982, NA), prg6m = c(0.25, 0, 
     0.5, 0.5, 0.8999996, 0, 0.6999998, 0.3999999, 0.0001249999, 
     NA), dnr = c("no dnr", NA, "no dnr", "no dnr", "no dnr", 
     "no dnr", "no dnr", "no dnr", "dnr after sadm", "no dnr"), 
     dnrday = c(5, NA, 17, 3, 16, 4, 9, 7, 2, 8), meanbp = c(97, 
     43, 70, 75, 59, 110, 78, 72, 97, 84), wblc = c(6, 17.0976562, 
     8.5, 9.09960938, 13.5, 10.3984375, 11.6992188, 13.5996094, 
     9.69921875, 11.2988281), hrt = c(69, 112, 88, 88, 112, 101, 
     120, 100, 56, 94), resp = c(22, 34, 28, 32, 20, 44, 28, 26, 
     20, 20), temp = c(36, 34.59375, 37.39844, 35, 37.89844, 38.39844, 
     37.39844, 37.59375, 36.59375, 38.19531), pafi = c(388, 98, 
     231.65625, NA, 173.3125, 266.625, 309.5, 404.75, 357.125, 
     NA), alb = c(1.7998047, NA, NA, NA, NA, NA, 4.7998047, NA, 
     NA, 4.6992188), bili = c(0.19998169, NA, 2.19970703, NA, 
     NA, NA, 0.39996338, NA, 0.39996338, 0.19998169), crea = c(1.19995117, 
     5.5, 2, 0.79992676, 0.79992676, 0.69995117, 1.59985352, 2, 
     1, 0.79992676), sod = c(141, 132, 134, 139, 143, 140, 132, 
     139, 143, 139), ph = c(7.459961, 7.25, 7.459961, NA, 7.509766, 
     7.65918, 7.479492, 7.509766, 7.449219, NA), glucose = c(NA_real_, 
     NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
     NA_real_, NA_real_, NA_real_), bun = c(NA_real_, NA_real_, 
     NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
     NA_real_, NA_real_), urine = c(NA_real_, NA_real_, NA_real_, 
     NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
     NA_real_), adlp = c(7, NA, 1, 0, NA, NA, 0, NA, NA, 0), adls = c(7, 
     1, 0, 0, 2, 1, 1, 0, 7, NA), sfdm2 = c(NA, "<2 mo. follow-up", 
     "<2 mo. follow-up", "no(M2 and SIP pres)", "no(M2 and SIP pres)", 
     "<2 mo. follow-up", "no(M2 and SIP pres)", NA, NA, NA), adlsc = c(7, 
     1, 0, 0, 2, 1, 1, 0, 7, 0.4947999)), row.names = c(NA, 10L
 ), class = "data.frame")

我在这里有回归公式。

SB_xlsx13 = SB_xlsx13[!is.na(SB_xlsx13$dnrday), ]
SB_xlsx13 = SB_xlsx13[!is.na(SB_xlsx13$sps), ]
MLR_2 = lm(SB_xlsx13$hospdead ~ SB_xlsx13$dzclass_f + SB_xlsx13$age + SB_xlsx13$sex + SB_xlsx13$num.co + SB_xlsx13$sps)
summary(MLR_2)
## 
## Call:
## lm(formula = SB_xlsx13$hospdead ~ SB_xlsx13$dzclass_f + SB_xlsx13$age + 
##     SB_xlsx13$sex + SB_xlsx13$num.co + SB_xlsx13$sps)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.26132 -0.25758 -0.08914  0.15412  1.14048 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)                           -0.3519553  0.0224017 -15.711  < 2e-16
## SB_xlsx13$dzclass_fCancer             -0.0870012  0.0123327  -7.055 1.86e-12
## SB_xlsx13$dzclass_fComa                0.2907825  0.0164644  17.661  < 2e-16
## SB_xlsx13$dzclass_fCOPD/CHF/Cirrhosis -0.1378731  0.0104787 -13.157  < 2e-16
## SB_xlsx13$age                          0.0027082  0.0002555  10.598  < 2e-16
## SB_xlsx13$sexmale                      0.0022789  0.0079126   0.288    0.773
## SB_xlsx13$num.co                       0.0028155  0.0032577   0.864    0.387
## SB_xlsx13$sps                          0.0184986  0.0004393  42.105  < 2e-16
##                                          
## (Intercept)                           ***
## SB_xlsx13$dzclass_fCancer             ***
## SB_xlsx13$dzclass_fComa               ***
## SB_xlsx13$dzclass_fCOPD/CHF/Cirrhosis ***
## SB_xlsx13$age                         ***
## SB_xlsx13$sexmale                        
## SB_xlsx13$num.co                         
## SB_xlsx13$sps                         ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3724 on 9067 degrees of freedom
## Multiple R-squared:  0.2772, Adjusted R-squared:  0.2767 
## F-statistic: 496.8 on 7 and 9067 DF,  p-value: < 2.2e-16

回归结果很好；但是，我想再添加一个变量，即第三天的 dnr 状态。如果该值小于或等于 3，则存在 DNR；如果该值大于 3，则不存在 DNR。我之前使用此代码对先前任务的这些值进行了子集化：

YesDNR <- subset(SB_xlsx12, dnrday <= 3, na.rm=TRUE)
NoDNR <- subset(SB_xlsx12, dnrday > 3, na.rm=TRUE)

这工作正常，但我无法真正在回归模型中使用这些子集。我假设要使模型正常工作，我需要将“dnrday”列中每个 3 或更少 (<=) 的值转换为“yes”，并将每个超过 3 (>) 的值转换为“no”。我的这种想法是否正确？如果是，我将如何完成改变这些价值观。

Answer 1

我将创建一个新专栏 - 请参阅下面的两个选项。

（注意，在 lm() 中，如果将协变量列为 data =，则每次添加协变量时都不必指定 SB_xlsx13$参数一次！这将使您的输出更易于阅读。）

Tidyverse 方法：mutate 和 case_when：

library(dplyr)
SB_xlsx13 <- SB_xlsx13 %>%
  mutate(dnr_d3 = case_when(dnrday <= 3 ~ "yes",
                            dnrday > 3 ~ "no",
                            TRUE ~ NA_character_))

MLR_3 <- lm(hospdead ~ dzclass + age + sex + num.co + sps + dnr_d3,
            data = SB_xlsx13)

Base R 方法：

SB_xlsx13$dnr_d3[SB_xlsx13$dnrday <= 3] <- "yes"
SB_xlsx13$dnr_d3[SB_xlsx13$dnrday > 3] <- "no"
MLR_4 <- lm(hospdead ~ dzclass + age + sex + num.co + sps + dnr_d3,
            data = SB_xlsx13)

Answer 2

您只需在公式中使用 AsIs 函数 I() 即可。还可以使用 factor 轻松分解变量。

lm(hospdead ~ factor(dzclass) + I(dnrday <= 3) + age + sex + num.co + sps, 
   SB_xlsx13)

这可能看起来有点不干净，但对于处理数据来说很不错。一旦您对某些内容感到满意，您就可以使用 transform 轻松地在数据中更改它。例如，只需使用 dnrday <= 3 即可创建布尔变量 DNR。

SB_xlsx13 <- transform(SB_xlsx13,
                       dzclass_f=as.factor(dzclass),
                       dnrday_3=dnrday <= 3)