No. Why would there be?
You have 12 videos, so that's what gets plotted.
Whether you are doing an ROC, running a regression, calculating AUC or even just computing means and medians, the data you have is the data you have.
Adding more points to the ROC is adding more data.
I think Li-Mak (Li & Mak, 1994) (univariate) and Ling-Li (Ling & Li, 1997) (multivariate) tests are suitable candidates for error diagnostics in univariate and multivariate GARCH models, respectively. Unlike some other (popular) tests*, they account for the fact that standardized residuals from GARCH models are not equal to true standardized ...
One should choose the variables to include based on theoretical considerations. Try different models with different combinations of variables and check how good the model fit is. You will see that the level of explanatory power varies between the variables. But never include more variables into the model than cases. Rather try to include as few variables as ...
Three ways has worked for me:
using sample() with rnorm():
sample(x=min:max, replace= TRUE, rnorm(n, mean))
using the msm package and the rtnorm function:
rtnorm(n, mean, lower=min, upper=max)
using the rnorm() and specifying the lower and upper limits, as Hugh has posted above:
sample <- rnorm(n, mean=mean); sample <- sample[x > min & x <...