sieci_neuronowe.R

library(caret)

## Loading required package: lattice
## Loading required package: ggplot2

dane <- readRDS("~/Pulpit/pm10_new.rds")
trainIndex <- createDataPartition(dane$poznanpm10, p=.7, list=F)
dane.train <- dane[trainIndex, ]
dane.test <- dane[-trainIndex, ]



dane.fit <- train(poznanpm10 ~ mm+tmin+tmax+u+v+ws+ lowcl + prec + pm10_m1 +pm10_m2+pm10_m3, data = dane.train, method = "nnet", maxit = 1000, trace = F, linout = 1)    

## Loading required package: nnet

## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.

dane.predict <- predict(dane.fit, newdata = dane.test)

hist(dane.predict-dane.test$poznanpm10)

cor(dane.predict,dane.test$poznanpm10,use = "pairwise.complete.obs")

## [1] 0.811891

plot(varImp((dane.fit)))