Plots {Greymodels}

plots

Description

The plots function gives an interactive plot of the model.

Usage

plots(x0,x0cap2,ci,model)
plotrm(x0,x0cap2,ci,model)
plotsmv1(actual1,fp1,ci,model)
plotsmv2(actual1,fitted,ci,model)
plotsigndgm(actual,pred,ci,model)
plots_mdbgm12(actual,pred,ci,model)

Arguments

x0

Raw data

x0cap2

Fitted and predicted data

actual

Raw data of interval sequences

actual1

Raw data of multi-variate sequences

fp1

Fitted and predicted data of first variable

fitted

Fitted data of multi-variate sequences

pred

Fitted and predicted data of interval sequences

ci

The confidence level chosen by the user. Values range between 90%, 95% and 99%.

model

The model under considration

Value

plots

Examples

  # Plots - EPGM (1, 1) model

  x0cap2<-c(560,541.4,517.8,495.3,473.7,453.1,433.4,414.5,396.5)

  x0<-c(560,540,523,500,475)

  # length of x0
  n <- length(x0)

  fitted2 <- t(x0cap2)

  x0cap <- x0cap2[1:n]

  # Last 4 values of x0cap2
  fitted3 <- tail(x0cap2,4)

  x0cap5 <- fitted3

  w <- length(x0cap2)
  t <- length(x0cap5)

  # Performance errors
  # Root mean square error
  s <- rmse(x0, x0cap)

  # Sum of square error
  sse <- sum((x0 - x0cap)^2)

  # Mean square error
  mse <- sse / (n - 2)

  # Calculate confidence interval
  ci <- 95
  cc <- (ci + 100)/200

  t.val <- qt(cc, n - 2)

  u <- numeric(t)
  l <- numeric(t)
  for (i in 1:t) {
    u[i] = x0cap5[i] + (t.val * (sqrt(mse) * sqrt(i)))
    l[i] = x0cap5[i] - (t.val * (sqrt(mse) * sqrt(i)))
  }
  UB <- c(u[1:t])
  LB <- c(l[1:t])

  LB1 <- c(x0cap[n],LB)
  UB2 <- c(x0cap[n],UB)

  l1 <- length(LB1)
  d3 <- seq(1, l1, 1)
  u1 <- length(UB2)
  d4 <- seq(1, u1, 1)

  set3 <- data.frame(x=d3, y=LB1)
  set4 <- data.frame(x=d4, y=UB2)

  d0 <- seq(1, n, 1)
  xy1 <- data.frame(x=d0, y=x0)

  d1 <- seq(1, w, 1)
  xy2 <- data.frame(x=d1, y=x0cap2)

  # Create data frame
  df <- rbind(xy1, xy2, set3, set4)

  # Plots
  colors <- c("Raw Data"="red","Fitted&Forecasts"="blue","LowerBound"="green","UpperBound"="yellow")
  CI <- c(n:w)

  x=y=NULL

  p <- ggplot(df) +
    theme_bw() +
    labs(title = 'EPGM (1, 1) model',x = 'Number of observation',y = 'Data Forecast & Prediction') +
    scale_x_continuous(breaks=1:w) +
    scale_y_continuous(labels = scales::comma) +
    geom_point(data = xy1, aes(x = x, y = y), shape = 24, color = "black") +
    geom_point(data = xy2, aes(x = x, y = y), shape = 21, color = "black") +
    geom_point(data = set3, aes(x = CI, y = y), shape = 23, color = "black") +
    geom_point(data = set4, aes(x = CI, y = y), shape = 23, color = "black") +
    geom_line(data = xy1, aes(x = x, y = y,color = "Raw Data")) +
    geom_line(data = xy2, aes(x = x, y = y,color = "Fitted&Forecasts")) +
    geom_line(data = set3, aes(x = CI, y = y,color = "LowerBound"), linetype=2) +
    geom_line(data = set4, aes(x = CI, y = y,color = "UpperBound"), linetype=2) +
    scale_color_manual(name = "Label",values = colors)
  r <- ggplotly(p)
  r