[R] How to make our data normally distributed in R

[Jin Li]

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 <neha.bologna90 using gmail.com> 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 <jinli68 using gmail.com> wrote:
>
>> 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
>> VEcv using tovecv in spm.
>> Hope this helps,
>> Jin
>>
>> On Fri, Mar 13, 2020 at 8:08 AM Neha gupta <neha.bologna90 using gmail.com>
>> wrote:
>>
>>> 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 3500 etc,
>>> but
>>> when I use ylim=(0,1), all the boxplots suddenly disappears. What should
>>> I
>>> do for it?
>>>
>>> Thanks
>>>
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>>>
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>>
>>
>> --
>> Jin
>>
>

-- 
Jin

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