Description Usage Arguments Value Author(s) Examples

View source: R/PlotMC.analysis.R

This is an internal function invoked by `MC.analysis`

function to generate an event plot of the time series under analysis.
A event means a time series with length lower to one month i.e. sub-montly time series.

1 | ```
PlotMC.event(summ, summ1, obs, det.var, det.var1, namePlot, ylab, ylab1, ntick, qUpper)
``` |

`summ` |
A data.frame with n observations of m variables as is provided by the output of function |

`summ1` |
A data.frame with n observations of m variables as is provided by the output of function |

`obs` |
A numeric value equal to 0. used for internal use. |

`det.var` |
A character string defining the name of the first variable from |

`det.var1` |
A character string defining the name of the second variable from |

`namePlot` |
A character string defining the name of the plot. The file created with the plot has this name. |

`ylab` |
A character string to define the label of the axes y for the first variable sub-plot. |

`ylab1` |
A character string to define the label of the axes y for the second variable sub-plot. |

`ntick` |
A numeric value integer which defines the number of tick marks in the axis x of the sub-plots. |

`qUpper` |
A character string that defines the upper percentile to plot the confidence band of results, several options are possible "q999" the 99.9th percentile, "q995" the 99.5th percentile, "q99" the 99th percentile, "q95" the 95th percentile, "q50" the 50th percentile. The lower boundary of the confidence band (showed in gray in the output plots) is the 5th percentile in all cases. |

The function creates the plot in the current working directory. The format of the plot is pdf.

J.A. Torres-Matallana

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ```
library(stUPscales)
library(EmiStatR)
# definition of the first summary.agg object
data("P1")
P1 <- P1[1:1100,]
new_data <- matrix(data = NA, nrow = nrow(P1), ncol = 55)
for(i in 1:55){
new_data[,i] <- matrix(data = rnorm(nrow(P1), 45, 15),
nrow = nrow(P1), ncol = 1)
}
new_data <- t(new_data)
new_summary <- MC.summary(p1 = P1, data = new_data)
# deterministic simulation
det <- rnorm(nrow(P1), 45, 15)
det <- cbind(det, rnorm(nrow(P1), 55, 23))
colnames(det) <- c("det1", "det2")
# level of aggregation
delta <- 60*2 # 2 hours
new_summary_agg <- MC.summary.agg(summ = new_summary, det, delta,
func.agg = mean, func.agg.p = sum)
# definition of the second summary.agg object
new_data1 <- matrix(data = NA, nrow = nrow(P1), ncol = 55)
for(i in 1:55){
new_data1[,i] <- matrix(data = rnorm(nrow(P1), 55, 23),
nrow = nrow(P1), ncol = 1)
}
new_data1 <- t(new_data1)
new_summary1 <- MC.summary(p1 = P1, data = new_data1)
new_summary_agg1 <- MC.summary.agg(summ = new_summary1, det, delta,
func.agg = mean, func.agg.p = sum)
# creating the plot for the event
PlotMC.event(summ = new_summary_agg, summ1 = new_summary_agg1, obs = 0,
det.var = "det1", det.var1 = "det2", namePlot = "ExamplePlot",
ylab = "Variable 1 [units]", ylab1 = "Variable 2 [units]",
ntick=10, qUpper= "q95")
``` |

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