Search code examples
rplotlinear-regressionnoise

logarithmic linear fit plotting in r

I have a dataframe correspond on frequency of each variable (example:variable 1 appear 1984 times and variable 2 appear 974 ...)

dff<-data.frame(Var1=c(1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,10 ,11, 12 ,13 ,14 ,15 ,16 ,27, 30 ,35 ,36 ,38 ,39 ,40 ,41), Freq=c(1984,  974,  464 , 251 , 127 ,  83 ,  45 ,  26  , 16  , 12 ,   9   , 5 ,   5  ,  2    ,3  ,  1   , 1  ,  1 ,   1   , 2  ,  5,4,2,1))

plot(dff$Var1,log(dff$Freq))

log plot log plot

as we see in the picture, there is a linear regression,

I just want to find a method that can fit only the regression part where the linear fit intersect the x axis.

because, I need to extract the other points that are not fitted in the linear fit (points after dff$Var1=20) that is mean is not a noise point

enter image description here

Solution

  • What about this:

    plot(dff$Var1, log(dff$Freq))
lr <- lm(log(Freq) ~ Var1, data = dff[dff$Var1 < 20, ])
abline(lr)

    enter image description here

    The cutoff point is 20. But you can vary it according to what you are doing.

    If you want to calculate

    where the linear fit intersect the x axis.

    Get the coefficients:

    coef(lr)
(Intercept)        Var1 
  7.4636699  -0.4741615

    And solve the equation 7.4636699 + Var1*(-0.4741615) = 0.