ggrothendieck / nls2 Goto Github PK

    library(nlmrt)

    # setup and run nlfb example
    shobbs.res  <-  function(x) 100.0*x[1]/(1+x[2]*10.*exp(-0.1*x[3]*seq(12))) - y
    y  <-  c(5.308, 7.24, 9.638, 12.866, 17.069, 23.192, 31.443, 
              38.558, 50.156, 62.948, 75.995, 91.972)
    st  <-  c(b1=1, b2=1, b3=1)
    ans1n <- nlfb(st, shobbs.res)

    # get nls object
    ans <- nls2(y ~ shobbs.res(c(b1, b2, b3)) + y, start = coef(ans1n), alg = "brute")

    # this fails with error
    confint(ans)

> nls2(y ~ a * x + b * x^2, start = list(a = 0, b = 0), alg = "brute-force")
Nonlinear regression model
  model: y ~ a * x + b * x^2
   data: NULL
a b 
0 0 
 residual sum-of-squares: 8.243

Number of iterations to convergence: 2 
Achieved convergence tolerance: NA

if (exists("out")) rm(out)
out <- nls2(demand ~ Time ^ b, BOD, start = data.frame(b = c(-10, 10)), algorithm = "plinear-brute")
out
## Error: object 'out' not found

> nls2(demand ~ a + b * Time, BOD, start = c(a = 1, b = 1))
Nonlinear regression model
  model: demand ~ a + b * Time
   data: structure(list(Time = c(1, 2, 3, 4, 5, 7), demand = c(8.3, 10.3, 19, 16, 15.6, 19.8)), class = "data.frame", row.names = c(NA, -6L), reference = "A1.4, p. 270")
    a     b 
8.521 1.721 
 residual sum-of-squares: 38.07

Number of iterations to convergence: 1 
Achieved convergence tolerance: 3.967e-09