For this tutorial we are going to use the following sample data, which consists on ten realizations of a brownian motion. The corresponding data for each path is a column of the
# Brownian motion set.seed(1) # Grid t <- seq(0, 1, by = 0.001) p <- length(t) - 1 # 10 paths n <- 10 I <- matrix(rnorm(n * p, 0, 1 / sqrt(p)), n, p) # Matrix B <- apply(I, 1, cumsum)
In order to create a line chart with all the columns of the data at the same time you can make use of
matplot and setting
type = "l". Note that
B is a numeric matrix, but could also be a data frame or a vector and a matrix.
matplot(B, type = "l") # Equivalent to: matplot(as.data.frame(B), type = "l") # Equivalent to: matplot(matrix(t), rbind(rep(0, n), B), type = "l")
By default, the function uses colors 1 to 6, line width of 1 and line types 1 to 5 to create the lines, but his can be overridden with arguments
# Colors cols <- hcl.colors(10, "Temps") matplot(B, type = "l", col = cols, # Colors lwd = 2, # Line width lty = 1) # Line type
matlines function is to
matplot the same as
lines is to
plot. It allows you adding more lines from a data frame or matrix to the previous plot. In the following example we are using it to highlight some paths of the brownian motion.
matplot(B, type = "l", col = "lightgray", lty = 1) # Highlight the first three columns # with a different color matlines(B[, 1:3], type = "l", col = 2, lwd = 2, lty = 1)