On Wed, 20 Mar 2019 09:43:11 +
akshay kulkarni wrote:
> But doesn't removing some of the parameters reduce the precision of
> the relationship between the response variable and the
> predictors(inefficient estimates of the coefficients)?
No, it doesn't, since there is already more variables
On Wed, 20 Mar 2019 08:02:45 +
akshay kulkarni wrote:
> formulaDH5 <- as.formula(HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2 +
> (A*sin(w*HM3 + c) + C))
The problem with this formula is simple: the partial derivative with
respect to `a` is the same as the partial derivative with respect to
`C`. Th
dear members,
I am getting the "singular gradient error" when I use nls
for a function of two variables:
> formulaDH5
HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2 + (A * sin(w * HM3 + a) +
C)
HM1 is the response variable, and HM2 and HM3 are predictors.
The problem is I get the
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