我试图在实践中看到这里解释的内容what happens to the coefficients once labels are switched，但我没有得到预期的结果。这是我的尝试：
我正在使用以和＃34;实践数据科学为例，以R＆＃34;为例给出的自然公共使用数据的例子。输出是一个逻辑变量，如果新生婴儿在风险级别为FALSE且为TRUE，则对新生婴儿进行分类
load(url("https://github.com/WinVector/zmPDSwR/tree/master/CDC/NatalRiskData.rData"))
train <- sdata[sdata$ORIGRANDGROUP<=5,]
test <- sdata[sdata$ORIGRANDGROUP>5,]
complications <- c("ULD_MECO","ULD_PRECIP","ULD_BREECH")
riskfactors <- c("URF_DIAB", "URF_CHYPER", "URF_PHYPER",
"URF_ECLAM")
y <- "atRisk"
x <- c("PWGT", "UPREVIS", "CIG_REC", "GESTREC3", "DPLURAL", complications, riskfactors)
fmla <- paste(y, paste(x, collapse="+"), sep="~")
model <- glm(fmla, data=train, family=binomial(link="logit"))
summary(model)
这导致以下系数：
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.412189 0.289352 -15.249 < 2e-16 ***
PWGT 0.003762 0.001487 2.530 0.011417 *
UPREVIS -0.063289 0.015252 -4.150 3.33e-05 ***
CIG_RECTRUE 0.313169 0.187230 1.673 0.094398 .
GESTREC3< 37 weeks 1.545183 0.140795 10.975 < 2e-16 ***
DPLURALtriplet or higher 1.394193 0.498866 2.795 0.005194 **
DPLURALtwin 0.312319 0.241088 1.295 0.195163
ULD_MECOTRUE 0.818426 0.235798 3.471 0.000519 ***
ULD_PRECIPTRUE 0.191720 0.357680 0.536 0.591951
ULD_BREECHTRUE 0.749237 0.178129 4.206 2.60e-05 ***
URF_DIABTRUE -0.346467 0.287514 -1.205 0.228187
URF_CHYPERTRUE 0.560025 0.389678 1.437 0.150676
URF_PHYPERTRUE 0.161599 0.250003 0.646 0.518029
URF_ECLAMTRUE 0.498064 0.776948 0.641 0.521489
好的，现在让我们切换atRisk变量中的标签：
esdata$atRisk <- factor(sdata$atRisk)
levels(sdata$atRisk) <- c("TRUE", "FALSE")
并重新运行上述分析，我希望看到上述报告系数的符号发生变化，但是，我得到完全相同的系数：
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.412189 0.289352 -15.249 < 2e-16 ***
PWGT 0.003762 0.001487 2.530 0.011417 *
UPREVIS -0.063289 0.015252 -4.150 3.33e-05 ***
CIG_RECTRUE 0.313169 0.187230 1.673 0.094398 .
GESTREC3< 37 weeks 1.545183 0.140795 10.975 < 2e-16 ***
DPLURALtriplet or higher 1.394193 0.498866 2.795 0.005194 **
DPLURALtwin 0.312319 0.241088 1.295 0.195163
ULD_MECOTRUE 0.818426 0.235798 3.471 0.000519 ***
ULD_PRECIPTRUE 0.191720 0.357680 0.536 0.591951
ULD_BREECHTRUE 0.749237 0.178129 4.206 2.60e-05 ***
URF_DIABTRUE -0.346467 0.287514 -1.205 0.228187
URF_CHYPERTRUE 0.560025 0.389678 1.437 0.150676
URF_PHYPERTRUE 0.161599 0.250003 0.646 0.518029
URF_ECLAMTRUE 0.498064 0.776948 0.641 0.521489
我在这做错了什么？你能帮忙吗
答案 0 :(得分：0)
因为您设置了train <- sdata[sdata$ORIGRANDGROUP<=5,]然后更改了sdata$atRisk <- factor(sdata$atRisk)，但您的模型正在使用train数据集，其级别不会被更改。
相反，你可以做
y <- "!atRisk"
x <- c("PWGT", "UPREVIS", "CIG_REC", "GESTREC3", "DPLURAL", complications, riskfactors)
fmla <- paste(y, paste(x, collapse="+"), sep="~")
model <- glm(fmla, data=train, family=binomial(link="logit"))
Call:
glm(formula = fmla, family = binomial(link = "logit"), data = train)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.2641 0.1358 0.1511 0.1818 0.9732
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.412189 0.289352 15.249 < 2e-16 ***
PWGT -0.003762 0.001487 -2.530 0.011417 *
UPREVIS 0.063289 0.015252 4.150 3.33e-05 ***
CIG_RECTRUE -0.313169 0.187230 -1.673 0.094398 .
GESTREC3< 37 weeks -1.545183 0.140795 -10.975 < 2e-16 ***
DPLURALtriplet or higher -1.394193 0.498866 -2.795 0.005194 **
DPLURALtwin -0.312319 0.241088 -1.295 0.195163
ULD_MECOTRUE -0.818426 0.235798 -3.471 0.000519 ***
ULD_PRECIPTRUE -0.191720 0.357680 -0.536 0.591951
ULD_BREECHTRUE -0.749237 0.178129 -4.206 2.60e-05 ***
URF_DIABTRUE 0.346467 0.287514 1.205 0.228187
URF_CHYPERTRUE -0.560025 0.389678 -1.437 0.150676
URF_PHYPERTRUE -0.161599 0.250003 -0.646 0.518029
URF_ECLAMTRUE -0.498064 0.776948 -0.641 0.521489
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2698.7 on 14211 degrees of freedom
Residual deviance: 2463.0 on 14198 degrees of freedom
AIC: 2491
Number of Fisher Scoring iterations: 7