Reputation: 934
I analyzed non-linear regression using nls package.
power<- nls(formula= agw~a*area^b, data=calibration_6, start=list(a=1, b=1))
summary(power)
I heard in non-linear model, R-squared is not valid and rather than R-squared, we usually show residual standard error which R provides
However, I just want to know what R-squared is. Is that possible to check R-squared in nls package?
Many thanks!!!
Upvotes: 0
Views: 2548
Reputation: 934
I found the solution. This method might not be correct in terms of statistics (As R^2 is not valid in non-linear model), but I just want see the overall goodness of fit for my non-linear model.
Step 1> to transform data as log (common logarithm)
When I use non-linear model, I can't check R^2
nls(formula= agw~a*area^b, data=calibration, start=list(a=1, b=1))
Therefore, I transform my data to log
x1<- log10(calibration$area)
y1<- log10(calibration$agw)
cal<- data.frame (x1,y1)
Step 2> to analyze linear regression
logdata<- lm (formula= y1~ x1, data=cal)
summary(logdata)
Call:
lm(formula = y1 ~ x1)
This model provides, y= -0.122 + 1.42x
But, I want to force intercept to zero, therefore,
Step 3> to force intercept to zero
logdata2<- lm (formula= y1~ 0 + x1)
summary(logdata2)
Now the equation is y= 1.322x, which means log (y) = 1.322 log (x),
so it's y= x^1.322.
In power curve model, I force intercept to zero. The R^2 is 0.9994
Upvotes: 1