【原创】R语言逻辑回归NRI、IDI 指标比较案例 附代码数据
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Null deviance: 131.85 on 98 degrees of freedom
Residual deviance: 106.78 on 96 degrees of freedom
AIC: 112.78
Number of Fisher Scoring iterations: 4
725.226
Outcome: present
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 14 9 39
[0.5,1] 1 14 7
Combined Data
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 56 2 3
[0.5,1] 1 2 33
Outcome: present
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 12 11 48
[0.5,1] 2 13 13
Combined Data
Updated Model
AIC: 94.712
Number of Fisher Scoring iterations: 5
与725.226模型比较的NRI、IDI
_________________________________________
Reclassification table
_________________________________________
NRI(Continuous) [95% CI]: 0.4107 [ 0.0161 - 0.8053 ] ; p-value: 0.04135
IDI [95% CI]: 0.1459 [ 0.0713 - 0.2206 ] ; p-value: 0.00013
1328.55的NRI、IDI
_________________________________________
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.1845 1.4842 -4.841 1.29e-06 ***
术前PSA -0.2366 0.4501 -0.526 0.599055
`CAPRA-S评分` 1.9122 0.5452 3.507 0.000453 ***
`CAPRA-S评分` 1.6073 0.5108 3.147 0.001650 **
`1014.16` -1.4363 0.4010 -3.582 0.000341 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 68 13 16
[0.5,1] 3 15 17
_________________________________________
NRI(Categorical) [95% CI]: 0.2204 [ 0.0416 - 0.3993 ] ; p-value: 0.01571
术前PSA 0.1220 0.4851 0.251 0.801475
`CAPRA-S评分` 1.5707 0.5431 2.892 0.003824 **
`1328.55` 1.7919 0.4662 3.844 0.000121 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
`725.226` 1.6861 0.4451 3.788 0.000152 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
-1.7453 -0.5387 -0.4765 0.7288 2.6769
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.9433 1.2427 -0.759 0.447792
术前PSA 0.0903 0.4575 0.197 0.843530
AIC: 98.939
Number of Fisher Scoring iterations: 5
1328.55
#logitMod3<-glm(是否复发~术前PSA+`CAPRA-S评分`+`1328.55`,data=data,
family=binomial(link="logit"))
summary(logitMod3)
summary(logitMod)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8076 -1.0169 -0.4592 0.7041 2.1457
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.0032 0.9489 -4.219 2.45e-05 ***
术前PSA 0.1481 0.3991 0.371 0.710537
`CAPRA-S评分` 1.6585 0.4641 3.574 0.000352 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 131.851 on 98 degrees of freedom
Residual deviance: 90.939 on 95 degrees of freedom
Reclassification table
_________________________________________
Outcome: absent
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 57 1 2
[0.5,1] 1 2 33
NRI(Categorical) [95% CI]: 0.1678 [ -0.0115 - 0.3472 ] ; p-value: 0.06667
NRI(Continuous) [95% CI]: 0.9422 [ 0.5822 - 1.3022 ] ; p-value: 0
IDI [95% CI]: 0.1584 [ 0.0812 - 0.2356 ] ; p-value: 6e-05
[0,0.5) 13 10 43
[0.5,1] 3 12 20
Combined Data
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 70 11 14
[0.5,1] 3 15 17
_________________________________________
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 131.851 on 98 degrees of freedom
Residual deviance: 86.712 on 95 degrees of freedom
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7580 -0.7114 -0.4417 0.6742 2.7821
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.4556 1.4946 -4.988 6.09e-07 ***
# 1014.16
logitMod2<-glm(是否复发~术前PSA+`CAPRA-S评分`+`1014.16`,data=data,
family=binomial(link="logit"))summary(logitMod2)
Deviance Residuals:
Min 1Q Median 3Q Max
逻辑回归NRI、IDI指标比较案例
head(data)
##是否复发术前PSA CAPRA-S评分725.226 1014.16 1328.55
## 1 0 3 2 2 2 1
## 2 0 3 2 2 Fra bibliotek 1## 3 0 1 1 1 3 1
## 4 0 2 2 2 1 2
## 5 0 2 2 1 1 2
Outcome: absent
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 57 1 2
[0.5,1] 0 3 0
Outcome: present
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 71 10 12
[0.5,1] 2 16 11
_________________________________________
NRI(Categorical) [95% CI]: 0.2105 [ 0.055 - 0.3661 ] ; p-value: 0.00797
NRI(Continuous) [95% CI]: 0.679 [ 0.3019 - 1.0562 ] ; p-value: 0.00042
IDI [95% CI]: 0.1753 [ 0.0969 - 0.2538 ] ; p-value: 1e-05
logitMod1<-glm(是否复发~术前PSA+`CAPRA-S评分`+`725.226`,data=data,
family=binomial(link="logit"))summary(logitMod1)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7219 -0.8988 -0.4221 0.7178 2.8554
## 6 0 2 2 1 2 1
三个logistic回归模型与只有PSA+CAPRA score基础模型
加入了725,1014和1328，分别建立了三个logistic回归模型，分别比较NRI、IDI指标。
原始模型
logitMod<-glm(是否复发~术前PSA+`CAPRA-S评分`,data=data,family=binomial(link="logit"))
Null deviance: 131.851 on 98 degrees of freedom
Residual deviance: 88.256 on 95 degrees of freedom
AIC: 96.256
Number of Fisher Scoring iterations: 5
1014.16
1014.16的NRI、IDI
_________________________________________
Reclassification table
_________________________________________
Outcome: absent
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
Residual deviance: 106.78 on 96 degrees of freedom
AIC: 112.78
Number of Fisher Scoring iterations: 4
725.226
Outcome: present
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 14 9 39
[0.5,1] 1 14 7
Combined Data
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 56 2 3
[0.5,1] 1 2 33
Outcome: present
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 12 11 48
[0.5,1] 2 13 13
Combined Data
Updated Model
AIC: 94.712
Number of Fisher Scoring iterations: 5
与725.226模型比较的NRI、IDI
_________________________________________
Reclassification table
_________________________________________
NRI(Continuous) [95% CI]: 0.4107 [ 0.0161 - 0.8053 ] ; p-value: 0.04135
IDI [95% CI]: 0.1459 [ 0.0713 - 0.2206 ] ; p-value: 0.00013
1328.55的NRI、IDI
_________________________________________
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.1845 1.4842 -4.841 1.29e-06 ***
术前PSA -0.2366 0.4501 -0.526 0.599055
`CAPRA-S评分` 1.9122 0.5452 3.507 0.000453 ***
`CAPRA-S评分` 1.6073 0.5108 3.147 0.001650 **
`1014.16` -1.4363 0.4010 -3.582 0.000341 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 68 13 16
[0.5,1] 3 15 17
_________________________________________
NRI(Categorical) [95% CI]: 0.2204 [ 0.0416 - 0.3993 ] ; p-value: 0.01571
术前PSA 0.1220 0.4851 0.251 0.801475
`CAPRA-S评分` 1.5707 0.5431 2.892 0.003824 **
`1328.55` 1.7919 0.4662 3.844 0.000121 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
`725.226` 1.6861 0.4451 3.788 0.000152 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
-1.7453 -0.5387 -0.4765 0.7288 2.6769
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.9433 1.2427 -0.759 0.447792
术前PSA 0.0903 0.4575 0.197 0.843530
AIC: 98.939
Number of Fisher Scoring iterations: 5
1328.55
#logitMod3<-glm(是否复发~术前PSA+`CAPRA-S评分`+`1328.55`,data=data,
family=binomial(link="logit"))
summary(logitMod3)
summary(logitMod)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8076 -1.0169 -0.4592 0.7041 2.1457
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.0032 0.9489 -4.219 2.45e-05 ***
术前PSA 0.1481 0.3991 0.371 0.710537
`CAPRA-S评分` 1.6585 0.4641 3.574 0.000352 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 131.851 on 98 degrees of freedom
Residual deviance: 90.939 on 95 degrees of freedom
Reclassification table
_________________________________________
Outcome: absent
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 57 1 2
[0.5,1] 1 2 33
NRI(Categorical) [95% CI]: 0.1678 [ -0.0115 - 0.3472 ] ; p-value: 0.06667
NRI(Continuous) [95% CI]: 0.9422 [ 0.5822 - 1.3022 ] ; p-value: 0
IDI [95% CI]: 0.1584 [ 0.0812 - 0.2356 ] ; p-value: 6e-05
[0,0.5) 13 10 43
[0.5,1] 3 12 20
Combined Data
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 70 11 14
[0.5,1] 3 15 17
_________________________________________
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 131.851 on 98 degrees of freedom
Residual deviance: 86.712 on 95 degrees of freedom
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7580 -0.7114 -0.4417 0.6742 2.7821
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.4556 1.4946 -4.988 6.09e-07 ***
# 1014.16
logitMod2<-glm(是否复发~术前PSA+`CAPRA-S评分`+`1014.16`,data=data,
family=binomial(link="logit"))summary(logitMod2)
Deviance Residuals:
Min 1Q Median 3Q Max
逻辑回归NRI、IDI指标比较案例
head(data)
##是否复发术前PSA CAPRA-S评分725.226 1014.16 1328.55
## 1 0 3 2 2 2 1
## 2 0 3 2 2 Fra bibliotek 1## 3 0 1 1 1 3 1
## 4 0 2 2 2 1 2
## 5 0 2 2 1 1 2
Outcome: absent
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 57 1 2
[0.5,1] 0 3 0
Outcome: present
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified
[0,0.5) 71 10 12
[0.5,1] 2 16 11
_________________________________________
NRI(Categorical) [95% CI]: 0.2105 [ 0.055 - 0.3661 ] ; p-value: 0.00797
NRI(Continuous) [95% CI]: 0.679 [ 0.3019 - 1.0562 ] ; p-value: 0.00042
IDI [95% CI]: 0.1753 [ 0.0969 - 0.2538 ] ; p-value: 1e-05
logitMod1<-glm(是否复发~术前PSA+`CAPRA-S评分`+`725.226`,data=data,
family=binomial(link="logit"))summary(logitMod1)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7219 -0.8988 -0.4221 0.7178 2.8554
## 6 0 2 2 1 2 1
三个logistic回归模型与只有PSA+CAPRA score基础模型
加入了725,1014和1328，分别建立了三个logistic回归模型，分别比较NRI、IDI指标。
原始模型
logitMod<-glm(是否复发~术前PSA+`CAPRA-S评分`,data=data,family=binomial(link="logit"))
Null deviance: 131.851 on 98 degrees of freedom
Residual deviance: 88.256 on 95 degrees of freedom
AIC: 96.256
Number of Fisher Scoring iterations: 5
1014.16
1014.16的NRI、IDI
_________________________________________
Reclassification table
_________________________________________
Outcome: absent
Updated Model
Initial Model [0,0.5) [0.5,1] % reclassified