Packages <- c("moonBook", "tidyverse", "jtools","car","readxl","dlookr","knitr")
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## [19] "graphics" "grDevices" "utils" "datasets" "methods" "base"d3 <- read.csv("/Users/koho0/Desktop/20201104_trim.csv") asfactor =function(var){
var=as.factor(var)}d4 <- d3 %>%
select(no,sex,age,wt,ibw,ht,optime,antime,asa,pacu,ioh_sbp90,ioh_mbp65,iobc_50,poh_sbp90,poh_mbp65,pobc_p,group,group1,dexm_ml,dexm_u,HTN,CCB,arbacei,bb,thiazide,furo,statin,CAD,DM,clopi,pre_cr,post_cr,pre_bun,post_bun)%>%
mutate_at(
vars(sex,asa,ioh_sbp90,ioh_mbp65,iobc_50,poh_mbp65,poh_sbp90,pobc_p),
funs(asfactor))%>%
mutate(iohypo = ifelse(ioh_sbp90 ==1|ioh_mbp65 ==1,1,0))%>%
mutate(periophypo =ifelse(ioh_sbp90 ==1|ioh_mbp65 ==1 |poh_sbp90==1|poh_mbp65==1,1,0))%>%
mutate(periopbc = ifelse(iobc_50 ==1|pobc_p==1,1,0))%>%
mutate(periophbe =ifelse(periophypo ==1|periopbc ==1,1,0)) ## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.glimpse(d4)## Rows: 2,302
## Columns: 38
## $ no <int> 920, 231, 1494, 1097, 1114, 989, 506, 1959, 998, 434, 88...
## $ sex <fct> M, F, M, F, F, F, F, F, F, M, F, M, M, M, F, F, F, F, F,...
## $ age <int> 25, 85, 64, 37, 47, 52, 68, 69, 36, 54, 15, 82, 34, 51, ...
## $ wt <dbl> 60.00, 48.50, 67.30, 54.00, 59.00, 51.90, 55.31, 45.30, ...
## $ ibw <dbl> 70.00, 44.60, 64.40, 59.00, 59.00, 43.90, 41.30, 27.20, ...
## $ ht <dbl> 174.00, 152.60, 168.40, 167.00, 167.00, 151.90, 149.30, ...
## $ optime <int> 54, 70, 63, 64, 64, 61, 70, 54, 140, 150, 75, 120, 56, 9...
## $ antime <int> 71, 76, 84, 85, 74, 75, 90, 68, 153, 175, 93, 135, 86, 1...
## $ asa <fct> 3, 3, 2, 2, 1, 2, 3, 2, 1, 2, 1, 3, 1, 2, 2, 2, 2, 2, 2,...
## $ pacu <int> 29, 20, 27, 29, 35, 22, 30, 26, 61, 22, 51, 54, 29, 45, ...
## $ ioh_sbp90 <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ ioh_mbp65 <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,...
## $ iobc_50 <fct> 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ poh_sbp90 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ poh_mbp65 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ pobc_p <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,...
## $ group <chr> "d", "m", "m", "n", "m", "d", "n", "n", "d", "n", "d", "...
## $ group1 <chr> "d", "o", "o", "o", "o", "d", "o", "o", "d", "o", "d", "...
## $ dexm_ml <dbl> 9.00, 0.00, 0.00, 0.00, 0.00, 15.20, 0.00, 0.00, 22.12, ...
## $ dexm_u <dbl> 0.5142857, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1...
## $ HTN <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,...
## $ CCB <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,...
## $ arbacei <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,...
## $ bb <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ thiazide <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ furo <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ statin <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,...
## $ CAD <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ DM <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,...
## $ clopi <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ pre_cr <dbl> 0.66, 4.85, 0.54, 0.17, 0.50, 0.55, 0.56, 0.68, 0.46, 0....
## $ post_cr <dbl> 0.70, 3.02, 0.48, 0.15, 0.45, 0.53, 0.49, 0.90, 0.54, 0....
## $ pre_bun <dbl> 3.4, 3.8, 4.8, 5.0, 5.0, 5.0, 5.0, 5.0, 5.4, 5.4, 5.5, 5...
## $ post_bun <dbl> 3.6, 13.2, 6.3, 4.7, 5.0, 12.0, 6.7, 6.3, 6.0, 4.0, 5.7,...
## $ iohypo <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,...
## $ periophypo <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,...
## $ periopbc <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,...
## $ periophbe <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0,...na.count=apply(d4,2,function(x) sum(is.na(x)))
na.count[na.count>0]## pre_cr post_cr pre_bun post_bun
## 77 27 79 27mytable(group1~.,d4,method=3,catMethod=0)##
## Descriptive Statistics by 'group1'
## ____________________________________________________________
## d o p
## (N=1086) (N=1216)
## ------------------------------------------------------------
## no 1262.0 [840.0;1686.0] 969.5 [424.5;1788.5] 0.000
## sex 0.678
## - F 506 (46.6%) 555 (45.6%)
## - M 580 (53.4%) 661 (54.4%)
## age 55.0 [37.0;66.0] 57.0 [40.5;67.0] 0.009
## wt 63.9 [56.2;73.3] 62.5 [55.0;71.0] 0.012
## ibw 58.0 [47.9;67.0] 57.3 [47.0;66.2] 0.226
## ht 163.0 [155.8;171.2] 163.0 [155.0;170.3] 0.150
## optime 61.0 [47.0;84.0] 56.0 [42.0;73.0] 0.000
## antime 80.0 [65.0;101.0] 73.0 [59.0;91.5] 0.000
## asa 0.187
## - 1 239 (22.0%) 253 (20.8%)
## - 2 696 (64.1%) 761 (62.6%)
## - 3 149 (13.7%) 195 (16.0%)
## - 4 2 ( 0.2%) 7 ( 0.6%)
## pacu 36.0 [27.0;49.0] 27.0 [20.0;35.0] 0.000
## ioh_sbp90 0.000
## - 0 967 (89.0%) 1169 (96.1%)
## - 1 119 (11.0%) 47 ( 3.9%)
## ioh_mbp65 0.000
## - 0 904 (83.2%) 1130 (92.9%)
## - 1 182 (16.8%) 86 ( 7.1%)
## iobc_50 0.000
## - 0 862 (79.4%) 1121 (92.2%)
## - 1 224 (20.6%) 95 ( 7.8%)
## poh_sbp90 0.000
## - 0 1014 (93.4%) 1210 (99.5%)
## - 1 72 ( 6.6%) 6 ( 0.5%)
## poh_mbp65 0.000
## - 0 968 (89.1%) 1199 (98.6%)
## - 1 118 (10.9%) 17 ( 1.4%)
## pobc_p 0.000
## - 0 901 (83.0%) 1155 (95.0%)
## - 1 185 (17.0%) 61 ( 5.0%)
## group 0.000
## - d 1086 (100.0%) 0 ( 0.0%)
## - m 0 ( 0.0%) 710 (58.4%)
## - n 0 ( 0.0%) 506 (41.6%)
## dexm_ml 16.8 [13.5;20.1] 0.0 [ 0.0; 0.0] 0.000
## dexm_u 1.2 [ 1.0; 1.4] 0.0 [ 0.0; 0.0] 0.000
## HTN 0.550
## - 0 990 (91.2%) 1118 (91.9%)
## - 1 96 ( 8.8%) 98 ( 8.1%)
## CCB 0.627
## - 0 1001 (92.2%) 1113 (91.5%)
## - 1 85 ( 7.8%) 103 ( 8.5%)
## arbacei 0.977
## - 0 998 (91.9%) 1116 (91.8%)
## - 1 88 ( 8.1%) 100 ( 8.2%)
## bb 0.156
## - 0 1058 (97.4%) 1196 (98.4%)
## - 1 28 ( 2.6%) 20 ( 1.6%)
## thiazide 0.193
## - 0 1061 (97.7%) 1176 (96.7%)
## - 1 25 ( 2.3%) 40 ( 3.3%)
## furo 0.027
## - 0 1078 (99.3%) 1193 (98.1%)
## - 1 8 ( 0.7%) 23 ( 1.9%)
## statin 0.402
## - 0 1002 (92.3%) 1134 (93.3%)
## - 1 84 ( 7.7%) 82 ( 6.7%)
## CAD 0.539
## - 0 1070 (98.5%) 1193 (98.1%)
## - 1 16 ( 1.5%) 23 ( 1.9%)
## DM 0.399
## - 0 996 (91.7%) 1102 (90.6%)
## - 1 90 ( 8.3%) 114 ( 9.4%)
## clopi 1.000
## - 0 1074 (98.9%) 1202 (98.8%)
## - 1 12 ( 1.1%) 14 ( 1.2%)
## pre_cr 0.8 [ 0.6; 0.9] 0.8 [ 0.6; 0.9] 0.064
## post_cr 0.7 [ 0.6; 0.9] 0.8 [ 0.6; 0.9] 0.368
## pre_bun 14.0 [11.2;17.1] 14.5 [12.0;18.0] 0.002
## post_bun 13.0 [10.0;16.0] 13.0 [10.0;16.0] 0.604
## iohypo 0.000
## - 0 880 (81.0%) 1118 (91.9%)
## - 1 206 (19.0%) 98 ( 8.1%)
## periophypo 0.000
## - 0 823 (75.8%) 1107 (91.0%)
## - 1 263 (24.2%) 109 ( 9.0%)
## periopbc 0.000
## - 0 809 (74.5%) 1101 (90.5%)
## - 1 277 (25.5%) 115 ( 9.5%)
## periophbe 0.000
## - 0 607 (55.9%) 998 (82.1%)
## - 1 479 (44.1%) 218 (17.9%)
## ------------------------------------------------------------dexmedetomidine만 추출
d5<- d4 %>% filter(group == "d")Logistic regression
model <- glm(periophypo ~ ht + wt+ dexm_ml + sex + age + asa + HTN +CCB + arbacei +bb +thiazide +furo +statin +CAD +DM +clopi, data=d5, family="binomial")
summary(model)##
## Call:
## glm(formula = periophypo ~ ht + wt + dexm_ml + sex + age + asa +
## HTN + CCB + arbacei + bb + thiazide + furo + statin + CAD +
## DM + clopi, family = "binomial", data = d5)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1451 -0.7745 -0.6737 -0.4847 2.0436
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.180985 2.024435 1.571 0.11611
## ht -0.022230 0.013475 -1.650 0.09899 .
## wt -0.010674 0.007648 -1.396 0.16283
## dexm_ml 0.034795 0.015124 2.301 0.02141 *
## sexM -0.223142 0.206872 -1.079 0.28075
## age -0.014356 0.005402 -2.658 0.00787 **
## asa2 0.242800 0.204081 1.190 0.23416
## asa3 0.398128 0.296867 1.341 0.17989
## asa4 -12.046265 376.836964 -0.032 0.97450
## HTN 0.267225 0.279878 0.955 0.33968
## CCB 0.072235 0.306941 0.235 0.81395
## arbacei -0.175359 0.332770 -0.527 0.59822
## bb 0.445197 0.427290 1.042 0.29745
## thiazide 0.041923 0.534014 0.079 0.93743
## furo 0.676943 0.765221 0.885 0.37635
## statin 0.054672 0.303289 0.180 0.85695
## CAD 0.464522 0.623625 0.745 0.45635
## DM -0.246079 0.303288 -0.811 0.41715
## clopi 0.319390 0.675962 0.472 0.63657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1202.4 on 1085 degrees of freedom
## Residual deviance: 1172.1 on 1067 degrees of freedom
## AIC: 1210.1
##
## Number of Fisher Scoring iterations: 12anova(model, test="Chisq")## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: periophypo
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1085 1202.4
## ht 1 9.3679 1084 1193.0 0.002208 **
## wt 1 1.6105 1083 1191.4 0.204423
## dexm_ml 1 5.6097 1082 1185.8 0.017861 *
## sex 1 1.6662 1081 1184.1 0.196770
## age 1 3.9894 1080 1180.1 0.045788 *
## asa 3 3.4879 1077 1176.6 0.322340
## HTN 1 0.8603 1076 1175.8 0.353668
## CCB 1 0.0434 1075 1175.7 0.834932
## arbacei 1 0.3837 1074 1175.3 0.535648
## bb 1 1.3430 1073 1174.0 0.246512
## thiazide 1 0.0010 1072 1174.0 0.974874
## furo 1 0.6984 1071 1173.3 0.403310
## statin 1 0.0074 1070 1173.3 0.931236
## CAD 1 0.3254 1069 1173.0 0.568356
## DM 1 0.6361 1068 1172.3 0.425117
## clopi 1 0.2170 1067 1172.1 0.641353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1reduced.model=step(model,direction="backward")## Start: AIC=1210.11
## periophypo ~ ht + wt + dexm_ml + sex + age + asa + HTN + CCB +
## arbacei + bb + thiazide + furo + statin + CAD + DM + clopi
##
## Df Deviance AIC
## - asa 3 1175.2 1207.2
## - thiazide 1 1172.1 1208.1
## - statin 1 1172.1 1208.1
## - CCB 1 1172.2 1208.2
## - clopi 1 1172.3 1208.3
## - arbacei 1 1172.4 1208.4
## - CAD 1 1172.7 1208.7
## - DM 1 1172.8 1208.8
## - furo 1 1172.8 1208.8
## - HTN 1 1173.0 1209.0
## - bb 1 1173.2 1209.2
## - sex 1 1173.3 1209.3
## - wt 1 1174.1 1210.1
## <none> 1172.1 1210.1
## - ht 1 1174.8 1210.8
## - dexm_ml 1 1177.4 1213.4
## - age 1 1179.2 1215.2
##
## Step: AIC=1207.16
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + CCB + arbacei +
## bb + thiazide + furo + statin + CAD + DM + clopi
##
## Df Deviance AIC
## - thiazide 1 1175.2 1205.2
## - CCB 1 1175.2 1205.2
## - statin 1 1175.2 1205.2
## - arbacei 1 1175.3 1205.3
## - clopi 1 1175.4 1205.4
## - DM 1 1175.8 1205.8
## - CAD 1 1175.8 1205.8
## - furo 1 1176.0 1206.0
## - HTN 1 1176.2 1206.2
## - bb 1 1176.3 1206.3
## - sex 1 1176.4 1206.4
## - wt 1 1177.0 1207.0
## <none> 1175.2 1207.2
## - ht 1 1177.8 1207.8
## - age 1 1180.3 1210.3
## - dexm_ml 1 1180.6 1210.6
##
## Step: AIC=1205.16
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + CCB + arbacei +
## bb + furo + statin + CAD + DM + clopi
##
## Df Deviance AIC
## - CCB 1 1175.2 1203.2
## - statin 1 1175.2 1203.2
## - arbacei 1 1175.3 1203.3
## - clopi 1 1175.4 1203.4
## - DM 1 1175.8 1203.8
## - CAD 1 1175.8 1203.8
## - furo 1 1176.0 1204.0
## - HTN 1 1176.3 1204.3
## - bb 1 1176.3 1204.3
## - sex 1 1176.4 1204.4
## - wt 1 1177.0 1205.0
## <none> 1175.2 1205.2
## - ht 1 1177.8 1205.8
## - age 1 1180.4 1208.4
## - dexm_ml 1 1180.7 1208.7
##
## Step: AIC=1203.21
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + arbacei +
## bb + furo + statin + CAD + DM + clopi
##
## Df Deviance AIC
## - statin 1 1175.3 1201.3
## - arbacei 1 1175.4 1201.4
## - clopi 1 1175.5 1201.5
## - CAD 1 1175.8 1201.8
## - DM 1 1175.8 1201.8
## - furo 1 1176.0 1202.0
## - sex 1 1176.4 1202.4
## - bb 1 1176.4 1202.4
## - HTN 1 1176.5 1202.5
## - wt 1 1177.0 1203.0
## <none> 1175.2 1203.2
## - ht 1 1177.8 1203.8
## - age 1 1180.4 1206.4
## - dexm_ml 1 1180.7 1206.7
##
## Step: AIC=1201.28
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + arbacei +
## bb + furo + CAD + DM + clopi
##
## Df Deviance AIC
## - arbacei 1 1175.4 1199.4
## - clopi 1 1175.6 1199.6
## - CAD 1 1175.8 1199.8
## - DM 1 1175.9 1199.9
## - furo 1 1176.1 1200.1
## - sex 1 1176.5 1200.5
## - HTN 1 1176.5 1200.5
## - bb 1 1176.5 1200.5
## - wt 1 1177.1 1201.1
## <none> 1175.3 1201.3
## - ht 1 1177.9 1201.9
## - age 1 1180.4 1204.4
## - dexm_ml 1 1180.8 1204.8
##
## Step: AIC=1199.39
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + bb + furo +
## CAD + DM + clopi
##
## Df Deviance AIC
## - clopi 1 1175.7 1197.7
## - CAD 1 1176.0 1198.0
## - DM 1 1176.0 1198.0
## - furo 1 1176.2 1198.2
## - HTN 1 1176.5 1198.5
## - sex 1 1176.6 1198.6
## - bb 1 1176.7 1198.7
## - wt 1 1177.3 1199.3
## <none> 1175.4 1199.4
## - ht 1 1178.0 1200.0
## - age 1 1180.7 1202.7
## - dexm_ml 1 1180.9 1202.9
##
## Step: AIC=1197.66
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + bb + furo +
## CAD + DM
##
## Df Deviance AIC
## - DM 1 1176.2 1196.2
## - CAD 1 1176.2 1196.2
## - furo 1 1176.5 1196.5
## - HTN 1 1176.8 1196.8
## - sex 1 1176.9 1196.9
## - bb 1 1177.1 1197.1
## - wt 1 1177.5 1197.5
## <none> 1175.7 1197.7
## - ht 1 1178.3 1198.3
## - age 1 1180.8 1200.8
## - dexm_ml 1 1181.2 1201.2
##
## Step: AIC=1196.23
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + bb + furo +
## CAD
##
## Df Deviance AIC
## - CAD 1 1176.6 1194.6
## - furo 1 1177.0 1195.0
## - HTN 1 1177.1 1195.1
## - sex 1 1177.5 1195.5
## - bb 1 1177.6 1195.6
## <none> 1176.2 1196.2
## - wt 1 1178.3 1196.3
## - ht 1 1178.8 1196.8
## - age 1 1181.9 1199.9
## - dexm_ml 1 1181.9 1199.9
##
## Step: AIC=1194.6
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + bb + furo
##
## Df Deviance AIC
## - furo 1 1177.4 1193.4
## - HTN 1 1177.8 1193.8
## - sex 1 1177.8 1193.8
## - bb 1 1177.9 1193.9
## <none> 1176.6 1194.6
## - wt 1 1178.7 1194.7
## - ht 1 1179.1 1195.1
## - age 1 1182.0 1198.0
## - dexm_ml 1 1182.3 1198.3
##
## Step: AIC=1193.36
## periophypo ~ ht + wt + dexm_ml + sex + age + HTN + bb
##
## Df Deviance AIC
## - HTN 1 1178.4 1192.4
## - sex 1 1178.5 1192.5
## - bb 1 1178.9 1192.9
## - wt 1 1179.3 1193.3
## <none> 1177.4 1193.4
## - ht 1 1180.0 1194.0
## - age 1 1182.6 1196.6
## - dexm_ml 1 1182.9 1196.9
##
## Step: AIC=1192.44
## periophypo ~ ht + wt + dexm_ml + sex + age + bb
##
## Df Deviance AIC
## - sex 1 1179.6 1191.6
## - bb 1 1180.1 1192.1
## - wt 1 1180.3 1192.3
## <none> 1178.4 1192.4
## - ht 1 1181.1 1193.1
## - age 1 1182.9 1194.9
## - dexm_ml 1 1183.8 1195.8
##
## Step: AIC=1191.58
## periophypo ~ ht + wt + dexm_ml + age + bb
##
## Df Deviance AIC
## - bb 1 1181.3 1191.3
## - wt 1 1181.4 1191.4
## <none> 1179.6 1191.6
## - dexm_ml 1 1184.5 1194.5
## - age 1 1184.6 1194.6
## - ht 1 1186.6 1196.6
##
## Step: AIC=1191.29
## periophypo ~ ht + wt + dexm_ml + age
##
## Df Deviance AIC
## - wt 1 1182.9 1190.9
## <none> 1181.3 1191.3
## - age 1 1185.8 1193.8
## - dexm_ml 1 1186.3 1194.3
## - ht 1 1188.8 1196.8
##
## Step: AIC=1190.93
## periophypo ~ ht + dexm_ml + age
##
## Df Deviance AIC
## <none> 1182.9 1190.9
## - dexm_ml 1 1187.5 1193.5
## - age 1 1187.9 1193.9
## - ht 1 1201.8 1207.8summary(reduced.model)##
## Call:
## glm(formula = periophypo ~ ht + dexm_ml + age, family = "binomial",
## data = d5)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0908 -0.7741 -0.6870 -0.5295 2.0930
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.192985 1.552082 3.346 0.00082 ***
## ht -0.039093 0.009107 -4.293 1.76e-05 ***
## dexm_ml 0.031922 0.014900 2.142 0.03216 *
## age -0.010120 0.004548 -2.225 0.02608 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1202.4 on 1085 degrees of freedom
## Residual deviance: 1182.9 on 1082 degrees of freedom
## AIC: 1190.9
##
## Number of Fisher Scoring iterations: 4ORtable=function(x,digits=2){
suppressMessages(a<-confint(x))
result=data.frame(exp(coef(x)),exp(a))
result=round(result,digits)
result=cbind(result,round(summary(x)$coefficient[,4],3))
colnames(result)=c("OR","2.5%","97.5%","p")
result
}
j_summ(model, scale = FALSE,
confint = getOption("summ-confint", TRUE),
ci.width = getOption("summ-ci.width", 0.95),
digits = getOption("jtools-digits", default = 3),
pvals = getOption("summ-pvals", TRUE),
n.sd = 1,
center = FALSE,
transform.response = FALSE,
exp = TRUE)| Observations | 1086 |
| Dependent variable | periophypo |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(18) | 30.247 |
| Pseudo-R² (Cragg-Uhler) | 0.041 |
| Pseudo-R² (McFadden) | 0.025 |
| AIC | 1210.111 |
| BIC | 1304.926 |
| exp(Est.) | 2.5% | 97.5% | z val. | p | |
|---|---|---|---|---|---|
| (Intercept) | 24.070 | 0.455 | 1272.583 | 1.571 | 0.116 |
| ht | 0.978 | 0.953 | 1.004 | -1.650 | 0.099 |
| wt | 0.989 | 0.975 | 1.004 | -1.396 | 0.163 |
| dexm_ml | 1.035 | 1.005 | 1.067 | 2.301 | 0.021 |
| sexM | 0.800 | 0.533 | 1.200 | -1.079 | 0.281 |
| age | 0.986 | 0.975 | 0.996 | -2.658 | 0.008 |
| asa2 | 1.275 | 0.855 | 1.902 | 1.190 | 0.234 |
| asa3 | 1.489 | 0.832 | 2.664 | 1.341 | 0.180 |
| asa4 | 0.000 | 0.000 | Inf | -0.032 | 0.974 |
| HTN | 1.306 | 0.755 | 2.261 | 0.955 | 0.340 |
| CCB | 1.075 | 0.589 | 1.962 | 0.235 | 0.814 |
| arbacei | 0.839 | 0.437 | 1.611 | -0.527 | 0.598 |
| bb | 1.561 | 0.676 | 3.606 | 1.042 | 0.297 |
| thiazide | 1.043 | 0.366 | 2.970 | 0.079 | 0.937 |
| furo | 1.968 | 0.439 | 8.818 | 0.885 | 0.376 |
| statin | 1.056 | 0.583 | 1.914 | 0.180 | 0.857 |
| CAD | 1.591 | 0.469 | 5.402 | 0.745 | 0.456 |
| DM | 0.782 | 0.431 | 1.417 | -0.811 | 0.417 |
| clopi | 1.376 | 0.366 | 5.177 | 0.472 | 0.637 |
| Standard errors: MLE |