非癌癥也能做逐步，logistic逐步回歸
爾云間? 一個專門做科研的團隊
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小果最近在做非疾病的分析，其中就涉及到了逐步回顧的分析，之前小果做過癌癥的逐步回歸，但沒做過非癌癥的，于是小果去找了一下逐步的代碼，下面就讓我們一起來看看吧。
代碼如下：
setwd("C:/Users/Administrator/Desktop")
data=read.table("逐步.txt",header = T,row.names = 1,sep = "\t")
library(gtsummary)
fitMul<-glm(group~.,data=data,family=binomial())
fitMul<- step(fitMul,direction = "both") #下面為逐步分析結(jié)果
Start:? AIC=92.88
group ~ AURKA + EHD1 + HTRA2 + LIAS + MRPS14 + MYC + NFU1 + NNMT +
??? SMYD2 + TACO1 + UBE2T
???????? Df Deviance???? AIC
- LIAS??? 1?? 68.881? 90.881
- TACO1?? 1?? 68.897? 90.897
- MRPS14? 1?? 68.975? 90.975
- SMYD2?? 1?? 69.005? 91.005
- EHD1??? 1?? 70.130? 92.130
<none>??????? 68.879? 92.879
- NFU1??? 1?? 72.151? 94.151
- HTRA2?? 1?? 72.170? 94.170
- NNMT??? 1?? 72.207? 94.207
- UBE2T?? 1?? 79.043 101.043
- AURKA?? 1?? 84.408 106.408
- MYC???? 1?? 99.722 121.722
Step:? AIC=90.88
group ~ AURKA + EHD1 + HTRA2 + MRPS14 + MYC + NFU1 + NNMT + SMYD2 +
??? TACO1 + UBE2T
???????? Df Deviance???? AIC
- TACO1?? 1?? 68.910? 88.910
- MRPS14? 1?? 68.981? 88.981
- SMYD2?? 1?? 69.010? 89.010
- EHD1??? 1?? 70.138? 90.138
<none>??????? 68.881? 90.881
- NFU1??? 1?? 72.167? 92.167
- NNMT??? 1?? 72.231? 92.231
- HTRA2?? 1?? 72.371? 92.371
+ LIAS??? 1?? 68.879? 92.879
- UBE2T?? 1?? 79.443? 99.443
- AURKA?? 1?? 84.435 104.435
- MYC???? 1?? 99.735 119.735
Step:? AIC=88.91
group ~ AURKA + EHD1 + HTRA2 + MRPS14 + MYC + NFU1 + NNMT + SMYD2 +
??? UBE2T
???????? Df Deviance???? AIC
- SMYD2?? 1?? 69.085? 87.085
- MRPS14? 1?? 69.119? 87.119
- EHD1??? 1?? 70.144? 88.144
<none>??????? 68.910? 88.910
- NFU1??? 1?? 72.231? 90.231
- NNMT??? 1?? 72.566? 90.566
- HTRA2?? 1?? 72.595? 90.595
+ TACO1?? 1?? 68.881? 90.881
+ LIAS??? 1?? 68.897? 90.897
- UBE2T?? 1?? 79.614? 97.614
- AURKA?? 1?? 85.261 103.261
- MYC???? 1?? 99.994 117.994
Step:? AIC=87.08
group ~ AURKA + EHD1 + HTRA2 + MRPS14 + MYC + NFU1 + NNMT + UBE2T
???????? Df Deviance???? AIC
- MRPS14? 1?? 69.276? 85.276
- EHD1??? 1?? 70.180? 86.180
<none>??????? 69.085? 87.085
- NFU1??? 1?? 72.705? 88.705
- NNMT??? 1?? 72.782? 88.782
+ SMYD2?? 1?? 68.910? 88.910
+ TACO1?? 1?? 69.010? 89.010
+ LIAS??? 1?? 69.054? 89.054
- HTRA2?? 1?? 74.058? 90.058
- UBE2T?? 1?? 79.624? 95.624
- AURKA?? 1?? 88.230 104.230
- MYC???? 1? 101.523 117.523
Step:? AIC=85.28
group ~ AURKA + EHD1 + HTRA2 + MYC + NFU1 + NNMT + UBE2T
???????? Df Deviance???? AIC
- EHD1??? 1?? 70.369? 84.369
<none>??????? 69.276? 85.276
- NNMT??? 1?? 72.970? 86.970
+ TACO1?? 1?? 69.076? 87.076
+ MRPS14? 1?? 69.085? 87.085
+ SMYD2?? 1?? 69.119? 87.119
+ LIAS??? 1?? 69.183? 87.183
- NFU1??? 1?? 74.035? 88.035
- HTRA2?? 1?? 77.191? 91.191
- UBE2T?? 1?? 81.170? 95.170
- AURKA?? 1?? 88.866 102.866
- MYC???? 1? 103.781 117.781
Step:? AIC=84.37
group ~ AURKA + HTRA2 + MYC + NFU1 + NNMT + UBE2T
???????? Df Deviance???? AIC
<none>??????? 70.369? 84.369
+ EHD1??? 1?? 69.276? 85.276
+ MRPS14? 1?? 70.180? 86.180
- NFU1??? 1?? 74.240? 86.240
+ TACO1?? 1?? 70.339? 86.339
+ SMYD2?? 1?? 70.342? 86.342
+ LIAS??? 1?? 70.365? 86.365
- NNMT??? 1?? 76.817? 88.817
- HTRA2?? 1?? 78.932? 90.932
- UBE2T?? 1?? 82.157? 94.157
- AURKA?? 1?? 89.783 101.783
- MYC???? 1? 106.493 118.493
#下面繼續(xù)分析代碼
fitSum<-summary(fitMul)
ResultMul<-c()#準備空向量，用來儲存結(jié)果
ResultMul<-rbind(ResultMul,fitSum$coef)
OR<-exp(fitSum$coef[,'Estimate'])
ResultMul<-cbind(ResultMul,cbind(OR,exp(confint(fitMul))))
#Waiting for profiling to be done...
ResultMul? #查看分析結(jié)果
????????????? Estimate Std. Error?? z value???? Pr(>|z|)?????????? OR??????? 2.5 %?????? 97.5 %
(Intercept) -51.047887 12.5263722 -4.075233 4.596828e-05 6.763703e-23 6.811140e-35 3.443089e-13
AURKA???????? 5.086038? 1.3770334? 3.693475 2.212104e-04 1.617478e+02 1.354446e+01 3.198491e+03
HTRA2???????? 3.541670? 1.3155403? 2.692179 7.098684e-03 3.452452e+01 3.069880e+00 5.844779e+02
MYC????????? -2.410618? 0.5051995 -4.771617 1.827532e-06 8.975980e-02 2.951109e-02 2.198625e-01
NFU1????????? 2.560629? 1.3508181? 1.895614 5.801116e-02 1.294396e+01 1.009701e+00 2.159337e+02
NNMT???????? -1.345624? 0.5717925 -2.353344 1.860544e-02 2.603771e-01 7.681164e-02 7.461131e-01
UBE2T???????? 2.581739? 0.8298538? 3.111077 1.864062e-03 1.322011e+01 2.880822e+00 7.776186e+01
這個分析和單因素的分析差不多，小伙伴們?nèi)绻绬我蛩氐姆治龇椒ǖ脑?，那這個肯定一看就懂。
好了，這就是今天的主要內(nèi)容了，小伙伴們有什么問題歡迎來和小果分享討論啊。
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