cinf_plot_d2m_error: Produce distance to model - error plot for applicability...
In conmolfields: Continuous molecular fields

Produce distance to model - error plot for applicability domain studies

1	cinf_plot_d2m_error(y_err, d2m, mean_parts = TRUE, nparts = 3, color_parts = "red", ...)

`y_err`
`d2m`
`mean_parts`
`nparts`
`color_parts`
`...`

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (y_err, d2m, mean_parts = TRUE, nparts = 3, color_parts = "red", 
    ...) 
{
    npoints <- length(y_err)
    oldnum <- order(d2m)
    y_err <- y_err[oldnum]
    d2m <- d2m[oldnum]
    plot(d2m, y_err, main = "Distance to Model - Error Plot", 
        xlab = "Distance to model", ylab = "Error")
    if (mean_parts) {
        p_first <- integer(nparts)
        p_last <- integer(nparts)
        p_size <- floor(npoints/nparts)
        for (ip in 1:nparts) p_first[ip] <- (ip - 1) * p_size + 
            1
        if (nparts > 1) 
            for (ip in 1:(nparts - 1)) p_last[ip] <- p_first[ip] + 
                p_size
        p_last[nparts] <- npoints
        p_mean <- integer(nparts)
        for (ip in 1:nparts) p_mean[ip] <- mean(y_err[p_first[ip]:p_last[ip]])
        for (ip in 1:nparts) {
            x <- c(d2m[p_first[ip]], d2m[p_last[ip]])
            y <- c(p_mean[ip], p_mean[ip])
            lines(list(x = x, y = y), col = color_parts)
        }
    }
  }