Please note that mu and sd are the mean and standard deviation of
validation samples. You may use pred.acc in spm to calculate a number of
error and accuracy measures including RMSE and VEcv from the observed and
predicted values directly.
On Sat, Mar 14, 2020 at 2:07 AM Neha gupta wrote:
>
Thanks a lot Jin..
If my total number of observations are 500,
n will be 500,
mu will be average (500)
s will be sd (500)
and m will be RMSE value i.e. 4500 in this case?
tovecv(n=500, mu=average (500), s=sd, m=4500, measure="rmse")
On Fri, Mar 13, 2020 at 12:46 AM Jin Li wrote:
> Hi,
> Why
Hello,
Why would it be awkward to show values like 4600? If those are the
values, show them. When there is a large difference, orders of
magnitude, you can plot logs by setting parameter log = "y" as in
boxplot(10^(0:5), log = "y")
But I don't see why to have values in the range 2900-4600
Hi,
Why do you want to re-scale RMSE to 0-1? You can change ylim=(0,1) to
ylim=(0, 4600). You may use VEcv (Variance explained by predictive models
based on cross-validation) that ranges from 0 to 100% instead. It can be
calculated using vecv function in library(spm) or you can convert RMSE to
Thanks Hasan and Rui
Rui, as you mentioned
As for the second question, if your RMSE vector had values in the range
2900 to 4600 and the y axis limits are c(0, 1), how can you expect to
see anything?
Then what should be the values of ylim in boxplots? I need to show them as
boxplot between 0-1
Hello,
To rescale data so that their values are between 0 and 1, use this function:
scale01 <- function(x, na.rm = FALSE){
(x - min(x, na.rm = na.rm))/(max(x, na.rm = na.rm) - min(x, na.rm =
na.rm))
}
x <- c(SVM=3500,
ANN=4600,
R.Forest=2900)
scale01(x)
# SVM ANN
Hi
I have a regression based data where I get the RMSE results as:
SVM=3500
ANN=4600
R.Forest=2900
I want to know how can I make it so that its values comes as 0-1
I plot the boxplot for it to indicate their RMSE values and used,
ylim=(0,1), but the boxplot which works for RMSE values like
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