How to assign non-linear regression output parameter to a variable

Question

I would like to use the parameters estimated by the nls function

I am performing a non linear regression on data using the m1<-nls(y1~v1*x/(k1+x)) function. I can display the predicted v1 and k1 values that are stored in m1. How can I assign these values to a specific variable (sort of "parameter <- v1")? v1 and k1 object do not exist ("Error: object 'v1' not found")

>\> m1<-nls(y1~v1*x/(k1+x))

>\> m1

> Nonlinear regression model
>  model: y1 ~ v1 * x/(k1 + x)

>   data: parent.frame()

>   v1    k1 

> 16.83 30.05 

> residual sum-of-squares: 0.8571

> Number of iterations to convergence: 5

> Achieved convergence tolerance: 1.4e-06

>\> parameter <- v1
>
Error: object 'v1' not found

Answer 1

This gives a vector of coefficients

co <- coef(m1)

and this gives them individually:

v1 <- coef(m1)[["v1"]]
k1 <- coef(m1)[["k1"]]

or if you just want to compute an expression using the coefficients:

with(as.list(coef(m1)), k1 + v1)

This would work to copy all individual elements of coef(m1) to your workspace:

list2env(as.list(coef(m1)), .GlobalEnv)