#Đưa dữ liệu vào R
library(haven)
e1 <- read_sav("D:/Deskop/Endobronchial_biopsy/Xử lý số liệu/RStudio/Dữ liệu-FINAL.sav")
head(e1)
## # A tibble: 6 × 108
## sothutu hoten namsinh gioi ldvv V6 hutthuocla goinam tha suytim dtd
## <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl+lbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 LY VAN… 1956 1 5 "" 1 [Còn hú… 20 1 0 0
## 2 2 PHAM V… 1956 1 1 "" 0 [Không … NA 1 0 0
## 3 3 HO VAN… 1957 1 6 "HO … 0 [Không … NA 0 0 0
## 4 4 KIM CH… 1970 1 1 "" 1 [Còn hú… 37 0 0 0
## 5 5 TRAN T… 1969 1 6 "CO … 1 [Còn hú… 40 0 0 0
## 6 6 DANH T… 1976 0 1 "" 0 [Không … NA 0 0 0
## # ℹ 97 more variables: ckd <dbl>, xogan <dbl>, doiquy <dbl>, ungthu <dbl>,
## # loaiut <chr>, copd <dbl>, hen <dbl>, lao <dbl>, laomp <dbl>, cc <dbl>,
## # cn <dbl>, sot <dbl>, met <dbl>, sutcan <dbl>, chanan <dbl>, hokhan <dbl>,
## # hodam <dbl>, homau <dbl>, daunguc <dbl>, khotho <dbl>, khangiong <dbl>,
## # nuotnghen <dbl>, sohach <dbl>, hc3giam <dbl>, tn <dbl>, tt <dbl>, nt <dbl>,
## # khonggn <dbl>, kinhmo <dbl>, hang <dbl>, notmo <dbl>, dam <dbl>,
## # dongdac <dbl>, xep <dbl>, tdmp <dbl>, trenp <dbl>, giuap <dbl>, …
e1$gpb1 <- factor(e1$gpb,
levels = c(1, 0), # chú ý: 1 là UTP, 0 là không UTP
labels = c("UTP", "Không UTP"))
e1$nhomtuoi1 <- factor(e1$nhomtuoi, levels = c(1, 2, 3), labels = c("<50", "50-70", ">70"))
model_nhomtuoi1.1 <- glm(gpb ~ nhomtuoi1, data = e1, family = binomial)
summary(model_nhomtuoi1.1)
##
## Call:
## glm(formula = gpb ~ nhomtuoi1, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2528 0.8018 -1.562 0.118
## nhomtuoi150-70 0.6597 0.8466 0.779 0.436
## nhomtuoi1>70 1.4351 0.8570 1.674 0.094 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 146.99 on 109 degrees of freedom
## AIC: 152.99
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_nhomtuoi1.1), confint(model_nhomtuoi1.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.2857143 0.04257115 1.182077
## nhomtuoi150-70 1.9342105 0.42120919 13.778222
## nhomtuoi1>70 4.2000000 0.89583766 30.379652
round(exp(cbind(OR = coef(model_nhomtuoi1.1), confint(model_nhomtuoi1.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.29 0.04 1.18
## nhomtuoi150-70 1.93 0.42 13.78
## nhomtuoi1>70 4.20 0.90 30.38
model_gioi.1 <- glm(gpb ~ gioi, data = e1, family = binomial)
summary(model_gioi.1)
##
## Call:
## glm(formula = gpb ~ gioi, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5754 0.4167 -1.381 0.167
## gioi 0.3211 0.4694 0.684 0.494
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.88 on 110 degrees of freedom
## AIC: 155.88
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_gioi.1), confint(model_gioi.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.562500 0.2380299 1.247758
## gioi 1.378685 0.5580359 3.576569
round(exp(cbind(OR = coef(model_gioi.1), confint(model_gioi.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.56 0.24 1.25
## gioi 1.38 0.56 3.58
e1$hutthuocla2 <- factor(e1$hutthuocla, levels = c(0, 1, 2), labels = c("khong", "co", "Ngung"))
model_htl.2 <- glm(gpb ~ hutthuocla2, data = e1, family = binomial)
summary(model_htl.2)
##
## Call:
## glm(formula = gpb ~ hutthuocla2, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7828 0.3018 -2.594 0.00949 **
## hutthuocla2co 0.8661 0.4178 2.073 0.03816 *
## hutthuocla2Ngung 0.6286 0.6329 0.993 0.32062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 147.85 on 109 degrees of freedom
## AIC: 153.85
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_htl.2), confint(model_htl.2)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.4571429 0.2464302 0.8116553
## hutthuocla2co 2.3777174 1.0582036 5.4777717
## hutthuocla2Ngung 1.8750000 0.5276059 6.5605628
round(exp(cbind(OR = coef(model_htl.2), confint(model_htl.2))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.46 0.25 0.81
## hutthuocla2co 2.38 1.06 5.48
## hutthuocla2Ngung 1.87 0.53 6.56
model_copd.1 <- glm(gpb ~ copd, data = e1, family = binomial)
summary(model_copd.1)
##
## Call:
## glm(formula = gpb ~ copd, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.31634 0.20051 -1.578 0.115
## copd -0.08913 0.67592 -0.132 0.895
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.34 on 110 degrees of freedom
## AIC: 156.34
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_copd.1), confint(model_copd.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7288136 0.4893514 1.076678
## copd 0.9147287 0.2223989 3.398374
round(exp(cbind(OR = coef(model_copd.1), confint(model_copd.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.73 0.49 1.08
## copd 0.91 0.22 3.40
model_lao.1 <- glm(gpb ~ lao, data = e1, family = binomial)
summary(model_lao.1)
##
## Call:
## glm(formula = gpb ~ lao, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4595 0.2129 -2.159 0.0309 *
## lao 0.7780 0.5111 1.522 0.1280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 150.01 on 110 degrees of freedom
## AIC: 154.01
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_lao.1), confint(model_lao.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6315789 0.4127457 0.9537812
## lao 2.1770833 0.8054137 6.1210460
round(exp(cbind(OR = coef(model_lao.1), confint(model_lao.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.63 0.41 0.95
## lao 2.18 0.81 6.12
model_laomp.1 <- glm(gpb ~ laomp, data = e1, family = binomial)
summary(model_laomp.1)
##
## Call:
## glm(formula = gpb ~ laomp, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3302 0.1933 -1.708 0.0876 .
## laomp 0.3302 1.4274 0.231 0.8170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.31 on 110 degrees of freedom
## AIC: 156.31
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_laomp.1), confint(model_laomp.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.718750 0.48959220 1.046897
## laomp 1.391304 0.05408245 35.794499
round(exp(cbind(OR = coef(model_laomp.1), confint(model_laomp.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.72 0.49 1.05
## laomp 1.39 0.05 35.79
model_sot.1 <- glm(gpb ~ sot, data = e1, family = binomial)
summary(model_sot.1)
##
## Call:
## glm(formula = gpb ~ sot, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.07796 0.22809 -0.342 0.7325
## sot -0.83833 0.43821 -1.913 0.0557 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 148.51 on 110 degrees of freedom
## AIC: 152.51
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_sot.1), confint(model_sot.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.9250000 0.5896666 1.4472458
## sot 0.4324324 0.1769368 0.9987641
round(exp(cbind(OR = coef(model_sot.1), confint(model_sot.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.93 0.59 1.45
## sot 0.43 0.18 1.00
model_met.1 <- glm(gpb ~ met, data = e1, family = binomial)
summary(model_met.1)
##
## Call:
## glm(formula = gpb ~ met, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2822 0.1953 -1.445 0.148
## met -1.1041 1.1350 -0.973 0.331
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.23 on 110 degrees of freedom
## AIC: 155.23
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_met.1), confint(model_met.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7540984 0.51191942 1.103230
## met 0.3315217 0.01664777 2.333705
round(exp(cbind(OR = coef(model_met.1), confint(model_met.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.75 0.51 1.10
## met 0.33 0.02 2.33
model_chanan.1 <- glm(gpb ~ chanan, data = e1, family = binomial)
summary(model_chanan.1)
##
## Call:
## glm(formula = gpb ~ chanan, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5341 0.2160 -2.473 0.0134 *
## chanan 1.1531 0.5162 2.234 0.0255 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 147.10 on 110 degrees of freedom
## AIC: 151.1
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_chanan.1), confint(model_chanan.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.5862069 0.3802664 0.8896239
## chanan 3.1680672 1.1800137 9.1625380
round(exp(cbind(OR = coef(model_chanan.1), confint(model_chanan.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.59 0.38 0.89
## chanan 3.17 1.18 9.16
model_sutcan.1 <- glm(gpb ~ sutcan, data = e1, family = binomial)
summary(model_sutcan.1)
##
## Call:
## glm(formula = gpb ~ sutcan, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5390 0.2746 -1.963 0.0497 *
## sutcan 0.4298 0.3852 1.116 0.2645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.11 on 110 degrees of freedom
## AIC: 155.11
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_sutcan.1), confint(model_sutcan.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.5833333 0.3351025 0.9897619
## sutcan 1.5369458 0.7244891 3.2948404
round(exp(cbind(OR = coef(model_sutcan.1), confint(model_sutcan.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.58 0.34 0.99
## sutcan 1.54 0.72 3.29
model_hokhan.1 <- glm(gpb ~ hokhan, data = e1, family = binomial)
summary(model_hokhan.1)
##
## Call:
## glm(formula = gpb ~ hokhan, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3697 0.2109 -1.753 0.0796 .
## hokhan 0.2644 0.5056 0.523 0.6010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.09 on 110 degrees of freedom
## AIC: 156.09
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_hokhan.1), confint(model_hokhan.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6909091 0.4538788 1.040765
## hokhan 1.3026316 0.4754311 3.532063
round(exp(cbind(OR = coef(model_hokhan.1), confint(model_hokhan.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.69 0.45 1.04
## hokhan 1.30 0.48 3.53
model_hodam.1 <- glm(gpb ~ hodam, data = e1, family = binomial)
summary(model_hodam.1)
##
## Call:
## glm(formula = gpb ~ hodam, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2231 0.3000 -0.744 0.457
## hodam -0.1699 0.3899 -0.436 0.663
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.17 on 110 degrees of freedom
## AIC: 156.17
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_hodam.1), confint(model_hodam.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.80000 0.4396049 1.437147
## hodam 0.84375 0.3921357 1.818037
round(exp(cbind(OR = coef(model_hodam.1), confint(model_hodam.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.80 0.44 1.44
## hodam 0.84 0.39 1.82
model_homau.1 <- glm(gpb ~ homau, data = e1, family = binomial)
summary(model_homau.1)
##
## Call:
## glm(formula = gpb ~ homau, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4238 0.2144 -1.977 0.0481 *
## homau 0.5191 0.4867 1.067 0.2861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.22 on 110 degrees of freedom
## AIC: 155.22
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_homau.1), confint(model_homau.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6545455 0.4266673 0.9917968
## homau 1.6805556 0.6449437 4.4315823
round(exp(cbind(OR = coef(model_homau.1), confint(model_homau.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.65 0.43 0.99
## homau 1.68 0.64 4.43
model_daunguc.1 <- glm(gpb ~ daunguc, data = e1, family = binomial)
summary(model_daunguc.1)
##
## Call:
## glm(formula = gpb ~ daunguc, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7191 0.2798 -2.570 0.0102 *
## daunguc 0.7932 0.3904 2.032 0.0422 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 148.15 on 110 degrees of freedom
## AIC: 152.15
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_daunguc.1), confint(model_daunguc.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.4871795 0.2756922 0.8314211
## daunguc 2.2105263 1.0358224 4.8107166
round(exp(cbind(OR = coef(model_daunguc.1), confint(model_daunguc.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.49 0.28 0.83
## daunguc 2.21 1.04 4.81
model_khotho.1 <- glm(gpb ~ khotho, data = e1, family = binomial)
summary(model_khotho.1)
##
## Call:
## glm(formula = gpb ~ khotho, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6931 0.3162 -2.192 0.0284 *
## khotho 0.6035 0.3998 1.510 0.1311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 150.03 on 110 degrees of freedom
## AIC: 154.03
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_khotho.1), confint(model_khotho.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.500000 0.2619361 0.914162
## khotho 1.828571 0.8432246 4.069102
round(exp(cbind(OR = coef(model_khotho.1), confint(model_khotho.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.50 0.26 0.91
## khotho 1.83 0.84 4.07
model_khangiong.1 <- glm(gpb ~ khangiong, data = e1, family = binomial)
summary(model_khangiong.1)
##
## Call:
## glm(formula = gpb ~ khangiong, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4212 0.1986 -2.121 0.0339 *
## khangiong 2.0307 1.1133 1.824 0.0682 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 147.75 on 110 degrees of freedom
## AIC: 151.75
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_khangiong.1), confint(model_khangiong.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.656250 0.441779 0.9646243
## khangiong 7.619048 1.174862 148.7335249
round(exp(cbind(OR = coef(model_khangiong.1), confint(model_khangiong.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.66 0.44 0.96
## khangiong 7.62 1.17 148.73
model_nuotnghen.1 <- glm(gpb ~ nuotnghen, data = e1, family = binomial)
summary(model_nuotnghen.1)
##
## Call:
## glm(formula = gpb ~ nuotnghen, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4367 0.1980 -2.206 0.0274 *
## nuotnghen 17.0028 1073.1090 0.016 0.9874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 143.35 on 110 degrees of freedom
## AIC: 147.35
##
## Number of Fisher Scoring iterations: 15
model_sohach.1 <- glm(gpb ~ sohach, data = e1, family = binomial)
summary(model_sohach.1)
##
## Call:
## glm(formula = gpb ~ sohach, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4383 0.2028 -2.161 0.0307 *
## sohach 1.2856 0.7192 1.787 0.0739 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 148.84 on 110 degrees of freedom
## AIC: 152.84
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_sohach.1), confint(model_sohach.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6451613 0.430509 0.9558605
## sohach 3.6166667 0.945904 17.5416455
round(exp(cbind(OR = coef(model_sohach.1), confint(model_sohach.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.65 0.43 0.96
## sohach 3.62 0.95 17.54
model_hc3giam.1<- glm(gpb ~ hc3giam, data = e1, family = binomial)
summary(model_hc3giam.1)
##
## Call:
## glm(formula = gpb ~ hc3giam, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.34033 0.21508 -1.582 0.114
## hc3giam 0.07796 0.47242 0.165 0.869
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.33 on 110 degrees of freedom
## AIC: 156.33
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_hc3giam.1), confint(model_hc3giam.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7115385 0.4637209 1.080884
## hc3giam 1.0810811 0.4199047 2.722497
round(exp(cbind(OR = coef(model_hc3giam.1), confint(model_hc3giam.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.71 0.46 1.08
## hc3giam 1.08 0.42 2.72
model_tn.1<- glm(gpb ~ tn, data = e1, family = binomial)
summary(model_tn.1)
##
## Call:
## glm(formula = gpb ~ tn, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6745 0.2364 -2.853 0.00434 **
## tn 1.1853 0.4350 2.725 0.00644 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 144.64 on 110 degrees of freedom
## AIC: 148.64
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_tn.1), confint(model_tn.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.509434 0.3160877 0.8020456
## tn 3.271605 1.4138525 7.8590962
round(exp(cbind(OR = coef(model_tn.1), confint(model_tn.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.51 0.32 0.80
## tn 3.27 1.41 7.86
model_nt.1<- glm(gpb ~ nt, data = e1, family = binomial)
summary(model_nt.1)
##
## Call:
## glm(formula = gpb ~ nt, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.05264 0.22950 -0.229 0.8186
## nt -0.90287 0.43718 -2.065 0.0389 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 147.85 on 110 degrees of freedom
## AIC: 151.85
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_nt.1), confint(model_nt.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.9487179 0.6033565 1.4891018
## nt 0.4054054 0.1661177 0.9337894
round(exp(cbind(OR = coef(model_nt.1), confint(model_nt.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.95 0.60 1.49
## nt 0.41 0.17 0.93
model_tt.1<- glm(gpb ~ tt, data = e1, family = binomial)
summary(model_tt.1)
##
## Call:
## glm(formula = gpb ~ tt, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3589 0.1965 -1.827 0.0677 .
## tt 0.7644 0.9338 0.819 0.4130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.67 on 110 degrees of freedom
## AIC: 155.67
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_tt.1), confint(model_tt.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6984127 0.4725376 1.02319
## tt 2.1477273 0.3425033 16.81516
round(exp(cbind(OR = coef(model_tt.1), confint(model_tt.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.70 0.47 1.02
## tt 2.15 0.34 16.82
model_khonggn.1<- glm(gpb ~ khonggn, data = e1, family = binomial)
summary(model_khonggn.1)
##
## Call:
## glm(formula = gpb ~ khonggn, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4372 0.2304 -1.898 0.0578 .
## khonggn 0.3766 0.4176 0.902 0.3672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.55 on 110 degrees of freedom
## AIC: 155.55
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_khonggn.1), confint(model_khonggn.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6458333 0.4073263 1.009030
## khonggn 1.4573055 0.6403680 3.319837
round(exp(cbind(OR = coef(model_khonggn.1), confint(model_khonggn.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.65 0.41 1.01
## khonggn 1.46 0.64 3.32
model_kinhmo.1<- glm(gpb ~ kinhmo, data = e1, family = binomial)
summary(model_kinhmo.1)
##
## Call:
## glm(formula = gpb ~ kinhmo, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4055 0.2152 -1.884 0.0595 .
## kinhmo 0.4055 0.4776 0.849 0.3959
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.64 on 110 degrees of freedom
## AIC: 155.64
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_kinhmo.1), confint(model_kinhmo.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6666667 0.4339887 1.011951
## kinhmo 1.5000000 0.5836481 3.863890
round(exp(cbind(OR = coef(model_kinhmo.1), confint(model_kinhmo.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.67 0.43 1.01
## kinhmo 1.50 0.58 3.86
model_hang.1<- glm(gpb ~ hang, data = e1, family = binomial)
summary(model_hang.1)
##
## Call:
## glm(formula = gpb ~ hang, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1787 0.1998 -0.894 0.3711
## hang -2.1239 1.0675 -1.990 0.0466 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 145.91 on 110 degrees of freedom
## AIC: 149.91
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_hang.1), confint(model_hang.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.8363636 0.563353289 1.2360059
## hang 0.1195652 0.006395857 0.6589616
round(exp(cbind(OR = coef(model_hang.1), confint(model_hang.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.84 0.56 1.24
## hang 0.12 0.01 0.66
model_not.1<- glm(gpb ~ notmo, data = e1, family = binomial)
summary(model_not.1)
##
## Call:
## glm(formula = gpb ~ notmo, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.07411 0.22237 -0.333 0.7389
## notmo -0.98194 0.46683 -2.103 0.0354 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 147.58 on 110 degrees of freedom
## AIC: 151.58
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_not.1), confint(model_not.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.9285714 0.5987744 1.4366219
## notmo 0.3745819 0.1426495 0.9059756
round(exp(cbind(OR = coef(model_not.1), confint(model_not.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.93 0.60 1.44
## notmo 0.37 0.14 0.91
model_dam.1<- glm(gpb ~ dam, data = e1, family = binomial)
summary(model_dam.1)
##
## Call:
## glm(formula = gpb ~ dam, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17.57 1097.25 -0.016 0.987
## dam 17.46 1097.25 0.016 0.987
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 136.99 on 110 degrees of freedom
## AIC: 140.99
##
## Number of Fisher Scoring iterations: 16
exp(cbind(OR = coef(model_dam.1), confint(model_dam.1)))
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## OR 2.5 % 97.5 %
## (Intercept) 2.350463e-08 NA 2.149555e+25
## dam 3.845396e+07 5.337581e-26 NA
round(exp(cbind(OR = coef(model_dam.1), confint(model_dam.1))), 2)
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## OR 2.5 % 97.5 %
## (Intercept) 0 NA 2.149555e+25
## dam 38453965 0 NA
model_dongdac.1<- glm(gpb ~ dongdac, data = e1, family = binomial)
summary(model_dongdac.1)
##
## Call:
## glm(formula = gpb ~ dongdac, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2624 0.2428 -1.080 0.280
## dongdac -0.1625 0.3953 -0.411 0.681
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.19 on 110 degrees of freedom
## AIC: 156.19
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_dongdac.1), confint(model_dongdac.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7692308 0.4744653 1.234812
## dongdac 0.8500000 0.3879816 1.838557
round(exp(cbind(OR = coef(model_dongdac.1), confint(model_dongdac.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.77 0.47 1.23
## dongdac 0.85 0.39 1.84
model_xep.1<- glm(gpb ~ xep, data = e1, family = binomial)
summary(model_xep.1)
##
## Call:
## glm(formula = gpb ~ xep, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3697 0.2109 -1.753 0.0796 .
## xep 0.2644 0.5056 0.523 0.6010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.09 on 110 degrees of freedom
## AIC: 156.09
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_xep.1), confint(model_xep.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6909091 0.4538788 1.040765
## xep 1.3026316 0.4754311 3.532063
round(exp(cbind(OR = coef(model_xep.1), confint(model_xep.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.69 0.45 1.04
## xep 1.30 0.48 3.53
model_tdmp.1<- glm(gpb ~ tdmp, data = e1, family = binomial)
summary(model_tdmp.1)
##
## Call:
## glm(formula = gpb ~ tdmp, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.28768 0.25459 -1.130 0.258
## tdmp -0.08388 0.38639 -0.217 0.828
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.31 on 110 degrees of freedom
## AIC: 156.31
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_tdmp.1), confint(model_tdmp.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7500000 0.4514642 1.231149
## tdmp 0.9195402 0.4287466 1.959928
round(exp(cbind(OR = coef(model_tdmp.1), confint(model_tdmp.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.75 0.45 1.23
## tdmp 0.92 0.43 1.96
model_trenp.1<- glm(gpb ~ trenp, data = e1, family = binomial)
summary(model_trenp.1)
##
## Call:
## glm(formula = gpb ~ trenp, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1643 0.2569 0.639 0.52252
## trenp -1.1362 0.4056 -2.802 0.00509 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 144.10 on 110 degrees of freedom
## AIC: 148.1
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_trenp.1), confint(model_trenp.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 1.1785714 0.7128543 1.9622026
## trenp 0.3210483 0.1418272 0.7001621
round(exp(cbind(OR = coef(model_trenp.1), confint(model_trenp.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 1.18 0.71 1.96
## trenp 0.32 0.14 0.70
model_giuap.1<- glm(gpb ~ giuap, data = e1, family = binomial)
summary(model_giuap.1)
##
## Call:
## glm(formula = gpb ~ giuap, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2877 0.2205 -1.305 0.192
## giuap -0.1476 0.4454 -0.331 0.740
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.25 on 110 degrees of freedom
## AIC: 156.25
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_giuap.1), confint(model_giuap.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7500000 0.4838458 1.152206
## giuap 0.8627451 0.3527618 2.048359
round(exp(cbind(OR = coef(model_giuap.1), confint(model_giuap.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.75 0.48 1.15
## giuap 0.86 0.35 2.05
model_duoip.1<- glm(gpb ~ duoip, data = e1, family = binomial)
summary(model_duoip.1)
##
## Call:
## glm(formula = gpb ~ duoip, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3295 0.2048 -1.609 0.108
## duoip 0.0418 0.5776 0.072 0.942
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.35 on 110 degrees of freedom
## AIC: 156.35
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_duoip.1), confint(model_duoip.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7192982 0.4787336 1.071262
## duoip 1.0426829 0.3214429 3.225456
round(exp(cbind(OR = coef(model_duoip.1), confint(model_duoip.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.72 0.48 1.07
## duoip 1.04 0.32 3.23
model_trent.1<- glm(gpb ~ trent, data = e1, family = binomial)
summary(model_trent.1)
##
## Call:
## glm(formula = gpb ~ trent, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6131 0.2435 -2.518 0.0118 *
## trent 0.8244 0.4071 2.025 0.0429 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 148.20 on 110 degrees of freedom
## AIC: 152.2
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_trent.1), confint(model_trent.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.5416667 0.3315188 0.8651394
## trent 2.2805430 1.0323278 5.1261785
round(exp(cbind(OR = coef(model_trent.1), confint(model_trent.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.54 0.33 0.87
## trent 2.28 1.03 5.13
model_duoit.1<- glm(gpb ~ duoit, data = e1, family = binomial)
summary(model_duoit.1)
##
## Call:
## glm(formula = gpb ~ duoit, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2429 0.2112 -1.150 0.250
## duoit -0.4502 0.5088 -0.885 0.376
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.55 on 110 degrees of freedom
## AIC: 155.55
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_duoit.1), confint(model_duoit.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7843137 0.5158871 1.184156
## duoit 0.6375000 0.2233456 1.684950
round(exp(cbind(OR = coef(model_duoit.1), confint(model_duoit.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.78 0.52 1.18
## duoit 0.64 0.22 1.68
model_nho3.1<- glm(gpb ~ nho3, data = e1, family = binomial)
summary(model_nho3.1)
##
## Call:
## glm(formula = gpb ~ nho3, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1011 0.2013 -0.502 0.615
## nho3 -17.4650 1097.2470 -0.016 0.987
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 136.99 on 110 degrees of freedom
## AIC: 140.99
##
## Number of Fisher Scoring iterations: 16
exp(cbind(OR = coef(model_nho3.1), confint(model_nho3.1)))
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## OR 2.5 % 97.5 %
## (Intercept) 9.038462e-01 0.6076408 1.340905e+00
## nho3 2.600512e-08 NA 1.873509e+25
round(exp(cbind(OR = coef(model_nho3.1), confint(model_nho3.1))), 2)
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## OR 2.5 % 97.5 %
## (Intercept) 0.9 0.61 1.340000e+00
## nho3 0.0 NA 1.873509e+25
model_tu35.1<- glm(gpb ~ tu35, data = e1, family = binomial)
summary(model_tu35.1)
##
## Call:
## glm(formula = gpb ~ tu35, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3830 0.2368 -1.618 0.106
## tu35 0.1717 0.4031 0.426 0.670
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.18 on 110 degrees of freedom
## AIC: 156.18
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_tu35.1), confint(model_tu35.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6818182 0.4248539 1.079343
## tu35 1.1873016 0.5356103 2.618985
round(exp(cbind(OR = coef(model_tu35.1), confint(model_tu35.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.68 0.42 1.08
## tu35 1.19 0.54 2.62
model_tu57.1<- glm(gpb ~ tu57, data = e1, family = binomial)
summary(model_tu57.1)
##
## Call:
## glm(formula = gpb ~ tu57, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4738 0.2217 -2.137 0.0326 *
## tu57 0.6279 0.4516 1.391 0.1644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 150.42 on 110 degrees of freedom
## AIC: 154.42
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_tu57.1), confint(model_tu57.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6226415 0.3995115 0.9562005
## tu57 1.8737374 0.7740147 4.6057053
round(exp(cbind(OR = coef(model_tu57.1), confint(model_tu57.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.62 0.40 0.96
## tu57 1.87 0.77 4.61
model_lon7.1<- glm(gpb ~ lon7, data = e1, family = binomial)
summary(model_lon7.1)
##
## Call:
## glm(formula = gpb ~ lon7, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3947 0.2324 -1.698 0.0894 .
## lon7 0.2228 0.4113 0.542 0.5880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.07 on 110 degrees of freedom
## AIC: 156.07
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_lon7.1), confint(model_lon7.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.673913 0.4236365 1.057612
## lon7 1.249576 0.5546321 2.802534
round(exp(cbind(OR = coef(model_lon7.1), confint(model_lon7.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.67 0.42 1.06
## lon7 1.25 0.55 2.80
e1$bo3 <- factor(e1$bo, levels = c(1, 2, 3), labels = c("tron_deu", "da_cung", "tua_gai"))
# Tạo biến mới bo4 từ biến bo
e1$bo4 <- factor(e1$bo, levels = c(1, 2, 3), labels = c(0, 1, 2))
model_bo4.1 <- glm(gpb ~ bo4, data = e1, family = binomial)
summary(model_bo4.1)
##
## Call:
## glm(formula = gpb ~ bo4, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5041 0.7817 -1.924 0.0544 .
## bo41 0.8109 0.8333 0.973 0.3305
## bo42 1.8042 0.8356 2.159 0.0308 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 143.28 on 109 degrees of freedom
## AIC: 149.28
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_bo4.1), confint(model_bo4.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.2222222 0.03387787 0.8622328
## bo41 2.2500000 0.51142764 15.7837538
## bo42 6.0750000 1.37999207 42.8078397
round(exp(cbind(OR = coef(model_bo4.1), confint(model_bo4.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.22 0.03 0.86
## bo41 2.25 0.51 15.78
## bo42 6.07 1.38 42.81
model_hach.1 <- glm(gpb ~ hach, data = e1, family = binomial)
summary(model_hach.1)
##
## Call:
## glm(formula = gpb ~ hach, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5021 0.2642 -1.901 0.0574 .
## hach 0.3843 0.3854 0.997 0.3186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.36 on 110 degrees of freedom
## AIC: 155.36
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_hach.1), confint(model_hach.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6052632 0.3556082 1.007622
## hach 1.4685990 0.6908658 3.144383
round(exp(cbind(OR = coef(model_hach.1), confint(model_hach.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.61 0.36 1.01
## hach 1.47 0.69 3.14
model_khongxl.1 <- glm(gpb ~ khongxl, data = e1, family = binomial)
summary(model_khongxl.1)
##
## Call:
## glm(formula = gpb ~ khongxl, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2048 0.2872 -0.713 0.476
## khongxl -0.2139 0.3858 -0.555 0.579
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.05 on 110 degrees of freedom
## AIC: 156.05
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_khongxl.1), confint(model_khongxl.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.8148148 0.4598224 1.428246
## khongxl 0.8074163 0.3778081 1.722468
round(exp(cbind(OR = coef(model_khongxl.1), confint(model_khongxl.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.81 0.46 1.43
## khongxl 0.81 0.38 1.72
model_xlmp.1 <- glm(gpb ~ xlmp, data = e1, family = binomial)
summary(model_xlmp.1)
##
## Call:
## glm(formula = gpb ~ xlmp, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4520 0.2162 -2.090 0.0366 *
## xlmp 0.6343 0.4797 1.322 0.1860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 150.60 on 110 degrees of freedom
## AIC: 154.6
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_xlmp.1), confint(model_xlmp.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6363636 0.4130902 0.9672841
## xlmp 1.8857143 0.7372032 4.9212646
round(exp(cbind(OR = coef(model_xlmp.1), confint(model_xlmp.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.64 0.41 0.97
## xlmp 1.89 0.74 4.92
model_xltn.1 <- glm(gpb ~ xltn, data = e1, family = binomial)
summary(model_xltn.1)
##
## Call:
## glm(formula = gpb ~ xltn, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3640 0.2033 -1.790 0.0734 .
## xltn 0.3640 0.6121 0.595 0.5521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.01 on 110 degrees of freedom
## AIC: 156.01
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_xltn.1), confint(model_xltn.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6949153 0.4636632 1.031574
## xltn 1.4390244 0.4226504 4.904081
round(exp(cbind(OR = coef(model_xltn.1), confint(model_xltn.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.69 0.46 1.03
## xltn 1.44 0.42 4.90
model_xltt.1 <- glm(gpb ~ xltt, data = e1, family = binomial)
summary(model_xltt.1)
##
## Call:
## glm(formula = gpb ~ xltt, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5500 0.2293 -2.399 0.0164 *
## xltt 0.8183 0.4339 1.886 0.0593 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 148.75 on 110 degrees of freedom
## AIC: 152.75
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_xltt.1), confint(model_xltt.1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.5769231 0.3640605 0.8977806
## xltt 2.2666667 0.9739394 5.3925165
round(exp(cbind(OR = coef(model_xltt.1), confint(model_xltt.1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.58 0.36 0.90
## xltt 2.27 0.97 5.39
model_ca1 <- glm(gpb ~ carina, data = e1, family = binomial)
summary(model_ca1)
##
## Call:
## glm(formula = gpb ~ carina, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3522 0.1945 -1.810 0.0702 .
## carina 1.0454 1.2401 0.843 0.3992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.60 on 110 degrees of freedom
## AIC: 155.6
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_ca1), confint(model_ca1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.703125 0.4776211 1.026346
## carina 2.844444 0.2648030 62.299279
round(exp(cbind(OR = coef(model_ca1), confint(model_ca1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.70 0.48 1.03
## carina 2.84 0.26 62.30
model_gocp1 <- glm(gpb ~ gocp, data = e1, family = binomial)
summary(model_gocp1)
##
## Call:
## glm(formula = gpb ~ gocp, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2992 0.2012 -1.487 0.137
## gocp -0.2604 0.6583 -0.396 0.692
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.20 on 110 degrees of freedom
## AIC: 156.2
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_gocp1), confint(model_gocp1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7413793 0.4971663 1.097025
## gocp 0.7707641 0.1918181 2.719771
round(exp(cbind(OR = coef(model_gocp1), confint(model_gocp1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.74 0.50 1.10
## gocp 0.77 0.19 2.72
model_goct1 <- glm(gpb ~ goct, data = e1, family = binomial)
summary(model_goct1)
##
## Call:
## glm(formula = gpb ~ goct, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4473 0.2050 -2.182 0.0291 *
## goct 1.1405 0.6458 1.766 0.0774 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 149.03 on 110 degrees of freedom
## AIC: 153.03
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_goct1), confint(model_goct1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6393443 0.4246526 0.9511848
## goct 3.1282051 0.9206413 12.3729094
round(exp(cbind(OR = coef(model_goct1), confint(model_goct1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.64 0.42 0.95
## goct 3.13 0.92 12.37
model_pqtp1<- glm(gpb ~ pqtp, data = e1, family = binomial)
summary(model_pqtp1)
##
## Call:
## glm(formula = gpb ~ pqtp, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1591 0.2528 -0.629 0.529
## pqtp -0.3846 0.3895 -0.987 0.323
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.38 on 110 degrees of freedom
## AIC: 155.38
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_pqtp1), confint(model_pqtp1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.8529412 0.5166872 1.398843
## pqtp 0.6807564 0.3140861 1.453899
round(exp(cbind(OR = coef(model_pqtp1), confint(model_pqtp1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.85 0.52 1.40
## pqtp 0.68 0.31 1.45
model_pqgp1 <- glm(gpb ~ pqgp, data = e1, family = binomial)
summary(model_pqgp1)
##
## Call:
## glm(formula = gpb ~ pqgp, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2822 0.1953 -1.445 0.148
## pqgp -1.1041 1.1350 -0.973 0.331
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.23 on 110 degrees of freedom
## AIC: 155.23
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_pqgp1), confint(model_pqgp1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7540984 0.51191942 1.103230
## pqgp 0.3315217 0.01664777 2.333705
round(exp(cbind(OR = coef(model_pqgp1), confint(model_pqgp1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.75 0.51 1.10
## pqgp 0.33 0.02 2.33
model_pqtg1 <- glm(gpb ~ pqtg, data = e1, family = binomial)
summary(model_pqtg1)
##
## Call:
## glm(formula = gpb ~ pqtg, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2683 0.2127 -1.261 0.207
## pqtg -0.2914 0.4916 -0.593 0.553
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.00 on 110 degrees of freedom
## AIC: 156
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_pqtg1), confint(model_pqtg1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7647059 0.5012773 1.157540
## pqtg 0.7472527 0.2742059 1.925474
round(exp(cbind(OR = coef(model_pqtg1), confint(model_pqtg1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.76 0.50 1.16
## pqtg 0.75 0.27 1.93
model_pqdp1 <- glm(gpb ~ pqdp, data = e1, family = binomial)
summary(model_pqdp1)
##
## Call:
## glm(formula = gpb ~ pqdp, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2938 0.2063 -1.424 0.155
## pqdp -0.2171 0.5561 -0.390 0.696
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 152.21 on 110 degrees of freedom
## AIC: 156.21
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_pqdp1), confint(model_pqdp1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7454545 0.4948242 1.114088
## pqdp 0.8048780 0.2558628 2.348363
round(exp(cbind(OR = coef(model_pqdp1), confint(model_pqdp1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.75 0.49 1.11
## pqdp 0.80 0.26 2.35
model_pqtt1 <- glm(gpb ~ pqtt, data = e1, family = binomial)
summary(model_pqtt1)
##
## Call:
## glm(formula = gpb ~ pqtt, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6242 0.2263 -2.759 0.00580 **
## pqtt 1.2601 0.4702 2.680 0.00737 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 144.78 on 110 degrees of freedom
## AIC: 148.78
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_pqtt1), confint(model_pqtt1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.5357143 0.3397819 0.8279557
## pqtt 3.5259259 1.4317904 9.1942009
round(exp(cbind(OR = coef(model_pqtt1), confint(model_pqtt1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.54 0.34 0.83
## pqtt 3.53 1.43 9.19
model_pqdt1 <- glm(gpb ~ pqdt, data = e1, family = binomial)
summary(model_pqdt1)
##
## Call:
## glm(formula = gpb ~ pqdt, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2231 0.2121 -1.052 0.293
## pqdt -0.5390 0.5045 -1.068 0.285
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.17 on 110 degrees of freedom
## AIC: 155.17
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_pqdt1), confint(model_pqdt1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.8000000 0.5253731 1.210362
## pqdt 0.5833333 0.2056770 1.524694
round(exp(cbind(OR = coef(model_pqdt1), confint(model_pqdt1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.80 0.53 1.21
## pqdt 0.58 0.21 1.52
model_xhuyet1 <- glm(gpb ~ xhuyet, data = e1, family = binomial)
summary(model_xhuyet1)
##
## Call:
## glm(formula = gpb ~ xhuyet, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4055 0.2214 -1.831 0.0671 .
## xhuyet 0.3314 0.4443 0.746 0.4558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.80 on 110 degrees of freedom
## AIC: 155.8
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_xhuyet1), confint(model_xhuyet1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.6666667 0.4285028 1.024185
## xhuyet 1.3928571 0.5788131 3.343733
round(exp(cbind(OR = coef(model_xhuyet1), confint(model_xhuyet1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.67 0.43 1.02
## xhuyet 1.39 0.58 3.34
model_thamnhiem1 <- glm(gpb ~ thamnhiem, data = e1, family = binomial)
summary(model_thamnhiem1)
##
## Call:
## glm(formula = gpb ~ thamnhiem, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4700 0.4031 1.166 0.2436
## thamnhiem -1.0433 0.4615 -2.261 0.0238 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 147.08 on 110 degrees of freedom
## AIC: 151.08
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_thamnhiem1), confint(model_thamnhiem1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 1.6000000 0.7362311 3.6509338
## thamnhiem 0.3522727 0.1386214 0.8586579
round(exp(cbind(OR = coef(model_thamnhiem1), confint(model_thamnhiem1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 1.60 0.74 3.65
## thamnhiem 0.35 0.14 0.86
model_noilong <- glm (gpb ~ noilong, data=e1, family = binomial)
summary(model_noilong)
##
## Call:
## glm(formula = gpb ~ noilong, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1787 0.3301 -3.570 0.000356 ***
## noilong 1.4759 0.4195 3.518 0.000435 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 138.88 on 110 degrees of freedom
## AIC: 142.88
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_noilong), confint(model_noilong)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.3076923 0.1542453 0.5696271
## noilong 4.3750000 1.9640835 10.2575700
round(exp(cbind(OR = coef(model_noilong), confint(model_noilong))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.31 0.15 0.57
## noilong 4.37 1.96 10.26
model_usui1 <- glm(gpb ~ usui, data = e1, family = binomial)
summary(model_usui1)
##
## Call:
## glm(formula = gpb ~ usui, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7621 0.2643 -2.884 0.00393 **
## usui 1.0245 0.3979 2.575 0.01003 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 145.55 on 110 degrees of freedom
## AIC: 149.55
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_usui1), confint(model_usui1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.4666667 0.2726621 0.772966
## usui 2.7857143 1.2883247 6.161661
round(exp(cbind(OR = coef(model_usui1), confint(model_usui1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.47 0.27 0.77
## usui 2.79 1.29 6.16
model_chenep1 <- glm(gpb ~ chenep, data = e1, family = binomial)
summary(model_chenep1)
##
## Call:
## glm(formula = gpb ~ chenep, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2461 0.2036 -1.209 0.227
## chenep -0.6702 0.6256 -1.071 0.284
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 151.14 on 110 degrees of freedom
## AIC: 155.14
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_chenep1), confint(model_chenep1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.7818182 0.5221964 1.162895
## chenep 0.5116279 0.1330431 1.645438
round(exp(cbind(OR = coef(model_chenep1), confint(model_chenep1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.78 0.52 1.16
## chenep 0.51 0.13 1.65
# Tạo biến mới vitri3
e1$vitri3 <- ifelse(e1$vtri == 1, 1, 0)
# Chuyển thành factor với nhãn rõ ràng
e1$vitri3 <- factor(e1$vitri3, levels = c(0, 1),
labels = c("Niêm mạc/dưới niêm", "Khối u"))
model_vtri1 <- glm(gpb ~ vitri3, data = e1, family = binomial)
summary(model_vtri1)
##
## Call:
## glm(formula = gpb ~ vitri3, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8535 0.2490 -3.428 0.000609 ***
## vitri3Khối u 1.6336 0.4411 3.704 0.000213 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 137.48 on 110 degrees of freedom
## AIC: 141.48
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(model_vtri1), confint(model_vtri1)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.4259259 0.2566226 0.684543
## vitri3Khối u 5.1225296 2.2054349 12.555950
round(exp(cbind(OR = coef(model_vtri1), confint(model_vtri1))), 2)
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.43 0.26 0.68
## vitri3Khối u 5.12 2.21 12.56
e1$sluong1 <- factor(e1$soluong, levels = c(1, 2, 3, 4, 5))
model_sluong1 <- glm(gpb ~ sluong1, data = e1, family = binomial)
summary(model_sluong1)
##
## Call:
## glm(formula = gpb ~ sluong1, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.69315 1.22474 -0.566 0.571
## sluong12 0.69315 1.25974 0.550 0.582
## sluong13 -0.02899 1.25992 -0.023 0.982
## sluong14 1.09861 1.38444 0.794 0.427
## sluong15 -14.87292 1455.39805 -0.010 0.992
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 146.77 on 107 degrees of freedom
## AIC: 156.77
##
## Number of Fisher Scoring iterations: 14
exp(cbind(OR = coef(model_sluong1), confint(model_sluong1)))
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## OR 2.5 % 97.5 %
## (Intercept) 5.000000e-01 0.02323545 5.220163e+00
## sluong12 2.000000e+00 0.17944219 4.481807e+01
## sluong13 9.714286e-01 0.08704157 2.176502e+01
## sluong14 3.000000e+00 0.21372310 7.876075e+01
## sluong15 3.473541e-07 NA 9.736723e+101
round(exp(cbind(OR = coef(model_sluong1), confint(model_sluong1))), 2)
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## OR 2.5 % 97.5 %
## (Intercept) 0.50 0.02 5.220000e+00
## sluong12 2.00 0.18 4.482000e+01
## sluong13 0.97 0.09 2.177000e+01
## sluong14 3.00 0.21 7.876000e+01
## sluong15 0.00 NA 9.736723e+101
model_full1 <- glm(
gpb ~ hutthuocla2 + chanan + daunguc +
tn + nt + hang + notmo + trenp + trent + bo4
+ xltt + pqtt + usui + noilong + vitri3,
data = e1,
family = binomial
)
summary(model_full1)
##
## Call:
## glm(formula = gpb ~ hutthuocla2 + chanan + daunguc + tn + nt +
## hang + notmo + trenp + trent + bo4 + xltt + pqtt + usui +
## noilong + vitri3, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.4285 1.4969 -2.958 0.00309 **
## hutthuocla2co 1.0586 0.5970 1.773 0.07620 .
## hutthuocla2Ngung 1.3235 0.9646 1.372 0.17002
## chanan 0.8013 0.6219 1.288 0.19763
## daunguc 0.8056 0.5437 1.482 0.13841
## tn 0.6541 0.6452 1.014 0.31064
## nt -0.2837 0.6299 -0.450 0.65246
## hang -2.4922 1.2392 -2.011 0.04431 *
## notmo -0.9076 0.6764 -1.342 0.17968
## trenp -0.5973 0.6721 -0.889 0.37416
## trent 0.7382 0.7660 0.964 0.33517
## bo41 1.9450 1.3110 1.484 0.13792
## bo42 2.2943 1.2923 1.775 0.07583 .
## xltt 0.3118 0.6267 0.498 0.61881
## pqtt 0.9327 0.7164 1.302 0.19291
## usui -0.7136 0.9875 -0.723 0.46992
## noilong 1.2173 0.8212 1.482 0.13826
## vitri3Khối u 1.3872 0.8761 1.583 0.11332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.360 on 111 degrees of freedom
## Residual deviance: 96.431 on 94 degrees of freedom
## AIC: 132.43
##
## Number of Fisher Scoring iterations: 5
model_step <- step(glm(gpb ~ hutthuocla2 + chanan + daunguc +
tn + nt + hang + notmo + trenp + trent + bo4
+ xltt + pqtt + usui + noilong + vitri3, family = binomial,
data = e1), scope = formula(model_full1), direction = "forward")
## Start: AIC=132.43
## gpb ~ hutthuocla2 + chanan + daunguc + tn + nt + hang + notmo +
## trenp + trent + bo4 + xltt + pqtt + usui + noilong + vitri3
summary(model_step)
##
## Call:
## glm(formula = gpb ~ hutthuocla2 + chanan + daunguc + tn + nt +
## hang + notmo + trenp + trent + bo4 + xltt + pqtt + usui +
## noilong + vitri3, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.4285 1.4969 -2.958 0.00309 **
## hutthuocla2co 1.0586 0.5970 1.773 0.07620 .
## hutthuocla2Ngung 1.3235 0.9646 1.372 0.17002
## chanan 0.8013 0.6219 1.288 0.19763
## daunguc 0.8056 0.5437 1.482 0.13841
## tn 0.6541 0.6452 1.014 0.31064
## nt -0.2837 0.6299 -0.450 0.65246
## hang -2.4922 1.2392 -2.011 0.04431 *
## notmo -0.9076 0.6764 -1.342 0.17968
## trenp -0.5973 0.6721 -0.889 0.37416
## trent 0.7382 0.7660 0.964 0.33517
## bo41 1.9450 1.3110 1.484 0.13792
## bo42 2.2943 1.2923 1.775 0.07583 .
## xltt 0.3118 0.6267 0.498 0.61881
## pqtt 0.9327 0.7164 1.302 0.19291
## usui -0.7136 0.9875 -0.723 0.46992
## noilong 1.2173 0.8212 1.482 0.13826
## vitri3Khối u 1.3872 0.8761 1.583 0.11332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.360 on 111 degrees of freedom
## Residual deviance: 96.431 on 94 degrees of freedom
## AIC: 132.43
##
## Number of Fisher Scoring iterations: 5
#Chạy bằng pp Stepwise
# Model khởi đầu: chỉ có intercept
model_start <- glm(gpb ~ 1, family = binomial, data = e1)
# Model đầy đủ: chứa tất cả biến độc lập
model_full <- glm(gpb ~ hutthuocla2 + chanan + daunguc +
tn + nt + hang + notmo + trenp + trent + bo4
+ xltt + pqtt + usui + noilong + vitri3,
family = binomial, data = e1)
# Stepwise forward
model_step <- step(model_start,
scope = list(lower = model_start, upper = model_full),
direction = "forward")
## Start: AIC=154.36
## gpb ~ 1
##
## Df Deviance AIC
## + vitri3 1 137.48 141.48
## + noilong 1 138.88 142.88
## + trenp 1 144.10 148.10
## + tn 1 144.64 148.64
## + pqtt 1 144.78 148.78
## + bo4 2 143.28 149.28
## + usui 1 145.55 149.55
## + hang 1 145.91 149.91
## + chanan 1 147.10 151.10
## + notmo 1 147.58 151.58
## + nt 1 147.85 151.85
## + daunguc 1 148.15 152.15
## + trent 1 148.20 152.20
## + xltt 1 148.75 152.75
## + hutthuocla2 2 147.85 153.85
## <none> 152.36 154.36
##
## Step: AIC=141.48
## gpb ~ vitri3
##
## Df Deviance AIC
## + trenp 1 129.77 135.77
## + daunguc 1 131.06 137.06
## + pqtt 1 131.59 137.59
## + hang 1 132.06 138.06
## + tn 1 132.20 138.20
## + bo4 2 130.46 138.46
## + chanan 1 133.05 139.05
## + notmo 1 134.00 140.00
## + trent 1 134.11 140.11
## + noilong 1 134.65 140.65
## + xltt 1 135.00 141.00
## + nt 1 135.04 141.04
## <none> 137.48 141.48
## + usui 1 137.12 143.12
## + hutthuocla2 2 135.31 143.31
##
## Step: AIC=135.77
## gpb ~ vitri3 + trenp
##
## Df Deviance AIC
## + tn 1 123.62 131.62
## + daunguc 1 124.26 132.26
## + bo4 2 123.11 133.11
## + hang 1 125.26 133.26
## + chanan 1 126.07 134.07
## + pqtt 1 126.74 134.74
## + notmo 1 127.40 135.40
## + xltt 1 127.63 135.63
## <none> 129.77 135.77
## + noilong 1 128.09 136.09
## + nt 1 128.45 136.45
## + hutthuocla2 2 126.85 136.85
## + trent 1 129.06 137.06
## + usui 1 129.65 137.65
##
## Step: AIC=131.62
## gpb ~ vitri3 + trenp + tn
##
## Df Deviance AIC
## + hang 1 119.95 129.95
## + daunguc 1 120.31 130.31
## + pqtt 1 120.47 130.47
## + chanan 1 120.50 130.50
## + bo4 2 118.65 130.65
## <none> 123.62 131.62
## + noilong 1 121.74 131.74
## + notmo 1 121.82 131.82
## + trent 1 122.77 132.76
## + nt 1 123.07 133.07
## + xltt 1 123.22 133.22
## + hutthuocla2 2 121.26 133.26
## + usui 1 123.30 133.30
##
## Step: AIC=129.95
## gpb ~ vitri3 + trenp + tn + hang
##
## Df Deviance AIC
## + daunguc 1 116.19 128.19
## + bo4 2 114.77 128.77
## + pqtt 1 116.81 128.81
## + chanan 1 116.95 128.96
## + noilong 1 117.53 129.53
## <none> 119.95 129.95
## + notmo 1 118.28 130.28
## + trent 1 118.29 130.29
## + hutthuocla2 2 116.85 130.85
## + xltt 1 119.41 131.41
## + nt 1 119.66 131.66
## + usui 1 119.82 131.82
##
## Step: AIC=128.19
## gpb ~ vitri3 + trenp + tn + hang + daunguc
##
## Df Deviance AIC
## + bo4 2 110.90 126.90
## + pqtt 1 113.19 127.19
## + notmo 1 113.64 127.64
## + noilong 1 113.67 127.67
## + chanan 1 113.87 127.87
## <none> 116.19 128.19
## + trent 1 114.68 128.68
## + hutthuocla2 2 113.29 129.29
## + xltt 1 115.59 129.59
## + nt 1 116.00 130.00
## + usui 1 116.14 130.14
##
## Step: AIC=126.9
## gpb ~ vitri3 + trenp + tn + hang + daunguc + bo4
##
## Df Deviance AIC
## + notmo 1 107.69 125.69
## + chanan 1 108.23 126.23
## + pqtt 1 108.68 126.68
## <none> 110.90 126.90
## + noilong 1 109.53 127.53
## + trent 1 109.72 127.72
## + hutthuocla2 2 108.06 128.06
## + xltt 1 110.60 128.60
## + nt 1 110.81 128.81
## + usui 1 110.81 128.81
##
## Step: AIC=125.69
## gpb ~ vitri3 + trenp + tn + hang + daunguc + bo4 + notmo
##
## Df Deviance AIC
## + chanan 1 105.45 125.45
## <none> 107.69 125.69
## + pqtt 1 105.87 125.87
## + trent 1 106.51 126.51
## + noilong 1 106.78 126.78
## + hutthuocla2 2 104.80 126.80
## + xltt 1 107.33 127.33
## + nt 1 107.34 127.34
## + usui 1 107.62 127.62
##
## Step: AIC=125.45
## gpb ~ vitri3 + trenp + tn + hang + daunguc + bo4 + notmo + chanan
##
## Df Deviance AIC
## + pqtt 1 103.34 125.34
## <none> 105.45 125.45
## + trent 1 104.40 126.40
## + noilong 1 104.57 126.57
## + hutthuocla2 2 102.86 126.86
## + xltt 1 105.20 127.20
## + nt 1 105.21 127.21
## + usui 1 105.44 127.44
##
## Step: AIC=125.34
## gpb ~ vitri3 + trenp + tn + hang + daunguc + bo4 + notmo + chanan +
## pqtt
##
## Df Deviance AIC
## + hutthuocla2 2 99.29 125.29
## <none> 103.34 125.34
## + noilong 1 102.34 126.34
## + xltt 1 102.79 126.79
## + nt 1 102.95 126.95
## + trent 1 103.14 127.14
## + usui 1 103.33 127.33
##
## Step: AIC=125.29
## gpb ~ vitri3 + trenp + tn + hang + daunguc + bo4 + notmo + chanan +
## pqtt + hutthuocla2
##
## Df Deviance AIC
## <none> 99.290 125.29
## + noilong 1 98.125 126.12
## + trent 1 99.049 127.05
## + xltt 1 99.097 127.10
## + nt 1 99.215 127.22
## + usui 1 99.289 127.29
summary(model_step)
##
## Call:
## glm(formula = gpb ~ vitri3 + trenp + tn + hang + daunguc + bo4 +
## notmo + chanan + pqtt + hutthuocla2, family = binomial, data = e1)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.8530 1.2963 -2.972 0.00296 **
## vitri3Khối u 1.6128 0.5728 2.816 0.00486 **
## trenp -1.0681 0.5664 -1.886 0.05935 .
## tn 0.6032 0.5816 1.037 0.29974
## hang -2.1643 1.1470 -1.887 0.05918 .
## daunguc 0.8692 0.5297 1.641 0.10080
## bo41 2.0534 1.2043 1.705 0.08819 .
## bo42 2.5895 1.1749 2.204 0.02752 *
## notmo -1.0061 0.6501 -1.547 0.12174
## chanan 0.9276 0.6138 1.511 0.13072
## pqtt 1.1950 0.6479 1.845 0.06511 .
## hutthuocla2co 0.9271 0.5671 1.635 0.10210
## hutthuocla2Ngung 1.4688 0.8844 1.661 0.09677 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 152.36 on 111 degrees of freedom
## Residual deviance: 99.29 on 99 degrees of freedom
## AIC: 125.29
##
## Number of Fisher Scoring iterations: 5