uo.zip_shock

（一）数据处理 （已经删掉survive) a=read.csv("E:\\课件\\吴喜之\\shock\\uo.csv",header=T) a$SEX=as.factor(a$SEX) a$SHOCK_TYP=as.factor(a$SHOCK_TYP) a$status=as.factor(a$status) a$ID=as.factor(a$ID) a$RECORD=as.factor(a$RECORD) a=a[,-5] ########################################################################## (二）描述图 table(a$SEX,a$status) b1=barplot(table(a$SEX,a$status),ylim=c(0,200),col=c(5,6),legend.text = c("male", "female"),xlab="status",ylab="number of people",main="status and SEX") table(a$SHOCK_TYP,a$status) b2=barplot(table(a$SHOCK_TYP,a$status),ylim=c(0,200),col=c(3,5,4,2,6,7),legend.text = c("Non-shock", "Hypovolemic shock","Cardiogenic shock","Bacterial shock","Neurogenic shock","Other"),xlab="status",ylab="number of people",main="status and SHOCK_TYP") table(a$RECORD,a$status) b3=barplot(table(a$RECORD,a$status),ylim=c(0,200),col=c(5,6),legend.text = c("record1", "record2"),xlab="status",ylab="number of people",main="status and RECORD") par(mfcol=c(3,2)) plot(a$status,a$SBP,xlab="status",main="status-SBP");plot(a$status,a$MAP,xlab="status",main="status-MAP") plot(a$status,a$HR,xlab="status",main="status-HR");plot(a$status,a$DBP,xlab="status",main="status-DBP") plot(a$status,a$MCVP,xlab="status",main="status-MCVP");plot(a$status,a$BSI,xlab="status",main="status-BSI") plot(a$status,a$CI,xlab="status",main="status-CI");plot(a$status,a$AT,xlab="status",main="status-AT") plot(a$status,a$MCT,xlab="status",main="status-MCT");plot(a$status,a$PVI,xlab="status",main="status-PVI") plot(a$status,a$RCI,xlab="status",main="status-RCI");plot(a$status,a$HG,xlab="status",main="status-HG") plot(a$status,a$HCT,xlab="status",main="status-HCT") ################################################################################################################################################## (1)因子分析降维 u=a[,c(6:18)] fac=factanal(factors=3,scale(u),scores="regression") k=a[,-c(6:18)] d=cbind(k,fac$scores) #降为3个因子 ################################################################################################################################################### (三）模型 （1）方差分析进行观察 a.aov=aov(UO~SHOCK_TYP*AGE*HT*SEX*RECORD+Error(ID/RECORD),data=a) summary(a.aov) （2）随机效应模型 library(lme4) rl1=lmer(status~SHOCK_TYP+AGE+HT+SEX+RECORD+Factor1+Factor2+Factor3+(SHOCK_TYP+AGE+HT+SEX+RECORD+Factor1+Factor2+Factor3|ID),family=binomial,data=d) rl2=lmer(status~RECORD+Factor1+Factor3+(RECORD+Factor1+Factor3|ID),family=binomial,data=d)########去掉不显著变量 (3)固定效应模型 fl1=lmer(status~SHOCK_TYP+AGE+HT+SEX+RECORD+Factor1+Factor2+Factor3+(1|ID),family=binomial,data=d) fl2=lmer(status~SHOCK_TYP+AGE+RECORD+Factor1+(1|ID),family=binomial,data=d) （3）决策树及随机效应模型 library(REEMtree) d=na.omit(d) k=REEMtree(UO~SHOCK_TYP+RECORD+Factor1+Factor2+Factor3+Factor4,data=d,random=~SEX+SHOCK_TYP+RECORD+Factor1+Factor2+Factor3+Factor4|ID) print(k) plot(k) #电脑跑不出来