tab=datas
tab1=na.omit(tab)
shapiro.test(tab1$TTG) #нет
shapiro.test(tab1$Mochevina)
shapiro.test(tab1$Ferritin)
shapiro.test(tab1$VitD)
shapiro.test(tab1$MKislota)
shapiro.test(tab1$B12)
shapiro.test(tab1$CholesterinVP)
shapiro.test(tab1$GGT)
shapiro.test(tab1$Cbelok)
shapiro.test(tab1$Kreatinin)
shapiro.test(tab1$Albumin)
shapiro.test(tab1$Trigleceridy)
shapiro.test(tab1$Cholesterin)
shapiro.test(tab1$ALAT)
shapiro.test(tab1$CholesterinNP)
shapiro.test(tab1$Age) #нет

cor(tab1, method = "kendal")

cor.test(tab1$Trigleceridy, tab1$Mochevina,method = "kendall") #p-value = 1.115e-06
cor.test(tab1$Trigleceridy, tab1$Ferritin,method = "kendall") #p-value = 2.626e-16
cor.test(tab1$Trigleceridy, tab1$MKislota,method = "kendall") #p-value < 2.2e-16
cor.test(tab1$Trigleceridy, tab1$GGT,method = "kendall") #p-value < 2.2e-16
cor.test(tab1$Trigleceridy, tab1$Cbelok,method = "kendall") #p-value < 2.2e-16
cor.test(tab1$Trigleceridy, tab1$Kreatinin,method = "kendall") #p-value = 3.853e-11
cor.test(tab1$Trigleceridy, tab1$Cholesterin,method = "kendall") #p-value < 2.2e-16
cor.test(tab1$Trigleceridy, tab1$ALAT,method = "kendall") #p-value < 2.2e-16
cor.test(tab1$Trigleceridy, tab1$CholesterinNP,method = "kendall") #p-value < 2.2e-16
cor.test(tab1$Trigleceridy, tab1$Age,method = "kendall") #p-value < 2.2e-16


library(caret)

fitControl <- trainControl(method="repeatedcv",number=10,repeats=10)

tr1=train(tab1[,-13],tab1$Trigleceridy,method ="rpart",trControl=fitControl)
#важные значение
tab33<-data.frame(Trigleceridy = tab1$Trigleceridy, Mochevina=tab1$Mochevina, Ferritin = tab1$Ferritin,
                  MKislota=tab1$MKislota, GGT = tab1$GGT, Cbelok=tab1$Cbelok, Kreatinin = tab1$Kreatinin,
                  Cholesterin = tab1$Cholesterin, ALAT = tab1$ALAT, CholesterinNP = tab1$CholesterinNP,
                  Age = tab1$Age)
tab33

lm.p<-lm(tab33$Trigleceridy~., data = tab33)
summary(lm.p)

lm.p<-lm(tab33$Trigleceridy~tab1$Ferritin+
           tab1$Cbelok+tab1$Kreatinin+tab1$Cholesterin+tab1$CholesterinNP+tab1$Ferritin+tab1$MKislota+tab1$ALAT+tab1$Cbelok, data = tab33)
summary(lm.p)

mse<-MSE(y_pred = exp(lm.p$fitted.values), y_true = tab33$Tcrigleceridy)
mse
rmse<-RMSE(y_pred = exp(lm.p$fitted.values), y_true = tab33$Trigleceridy)
rmse

cacao<-mean(abs(lm.p$residuals/tab33$Trigleceridy))*100
cacao
#другие значение
tab44<-data.frame(Trigleceridy = tab1$Trigleceridy, TTG = tab1$TTG, B12=tab1$B12, CholesterinVP = tab1$CholesterinVP,
                  VitD = tab1$VitD, Albumin = tab1$Albumin)

lm.p44<-lm(tab44$Trigleceridy~., data = tab44)
summary(lm.p44)


mse44<-MSE(y_pred = exp(lm.p44$fitted.values), y_true = tab44$Trigleceridy)
mse44
rmse44<-RMSE(y_pred = exp(lm.p44$fitted.values), y_true = tab44$Trigleceridy)
rmse44

cacao44<-mean(abs(lm.p44$residuals/tab44$Trigleceridy))*100
cacao44


set.seed(11)


tr2=train(tab33,tab33$Trigleceridy,method ="nnet", trControl=fitControl)
tr2$finalModel

library(nnet)
library(MLmetrics)

for(i in 1:10){
  nnet <- nnet(tab33$Trigleceridy ~ ., tab33, decay=0.1, size=i)
  ind <- sample(2, nrow(tab33), replace = TRUE, prob=c(0.7, 0.3))
  trainset = parsedTen[ind == 1,]
  testset = parsedTen[ind == 2,]

  mse <- MSE(y_pred = exp(nnet$fitted.values), y_true = tab33$Trigleceridy)
  mse
  RMSE <- RMSE(y_pred = exp(nnet$fitted.values), y_true = tab33$Trigleceridy)
  COOA = mean(abs(nnet$residuals/tab33$Trigleceridy)) * 100

  print(i)
  print(mse)
  print(RMSE)
  print(COOA)
}


ind = sample(2, nrow(tab33), replace = TRUE, prob=c(0.7, 0.3))
trainset = tab33[ind == 1,]
testset = tab33[ind == 2,]
###
nnmodel<-nnet(tab33$Trigleceridy~., data=tab33, decay=0.1, size=3)

nnmodel1<-nnet(trainset$Trigleceridy~., trainset, decay=0.1, size=3)

pp<-predict(nnmodel1, testset)
pp


mse66<-MSE(y_pred = exp(nnmodel$fitted.values), y_true = tab33$Trigleceridy)
mse66
rmse66<-RMSE(y_pred = exp(nnmodel$fitted.values), y_true = tab33$Trigleceridy)
rmse66

cacao66<-mean(abs(nnmodel$residuals/tab33$Trigleceridy))*100
cacao66
######


nnmodel<-nnet(tab33$Trigleceridy~., data=tab33, decay=0.1, size=50)

nnmodel1<-nnet(trainset$Trigleceridy~., trainset, decay=0.1, size=50)

pp<-predict(nnmodel1, testset)
pp


mse66<-MSE(y_pred = exp(nnmodel$fitted.values), y_true = tab33$Trigleceridy)
mse66
rmse66<-RMSE(y_pred = exp(nnmodel$fitted.values), y_true = tab33$Trigleceridy)
rmse66

cacao66<-mean(abs(nnmodel$residuals/tab33$Trigleceridy))*100
cacao66