[R] Weighting data with normal distribution

[Mark Lyman]

Alice Lin <alice.ly <at> gmail.com> writes:

> 
> 
> I have a vector of binary data – a string of 0’s and 1’s. 
> I want to weight these inputs with a normal kernel centered around entry x
> so it is transformed into a new vector of data that takes into account the
> values of the entries around it (weighting them more heavily if they are
> near).
> 
> Example:
>       -
>    -     -
> -          -
> 0 1 0 0 1 0 0 1 1 1 1 
> If x = 3, it’s current value is 0 but it’s new value with the Gaussian
> weighting around would be something like .1*0+.5*1+1*0+0.5*0+.1*1= 0.6
> 
> I want to be able to play with adjusting the variance to different values as
> well.
> I’ve found wkde in the mixtools library and think it may be useful but I
> have not figured out how to use it yet.
> 
> Any tips would be appreciated.
> 
> Thanks!
> 

I don't know anything about wkde. But the filter function in stats package 
should do what you want.

> x <- c(0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1)
> filter(x, c(.1, .5, 1, .5, .1))
Time Series:
Start = 1 
End = 11 
Frequency = 1 
 [1]  NA  NA 0.6 0.6 1.0 0.6 0.7 1.6 2.1  NA  NA

In the signal package, there is also a variety of windows, including the 
gausswin function. However, the filter function in the signal package masks the 
filter function from the stats package

> stats::filter(x, gausswin(5, 2.68))

Mark Lyman
Statistician, ATK Launch Systems