In [1]:

    

using RDatasets, NLreg, Gadfly


    

In [2]:

    

cw2 = DataFrame(
 x=[0.5,1,1.75,3.75,5.75,0.875,2.25,3.25,5.25,0.75,1.75,
    2.75,4.75,0.625,1.25,2.25,4.25,0.5,3,0.75,3,1.5,6,
    3,6,1.5,3,0.5,2,4,0.75,2,5,0.75,2.25,3.75,5.75,
	3,0.75,2.5,4,0.75,2.5,4,0.75,2.5,4,0.5,6,3,0.5,
	2.75,0.5,1.75],
 y=[92.9,57.1,31.05,11.5875,8.025,63.6,21.4,14.25,8.475,
    63.8,26.8,16.4625,7.125,67.3,41,21.15,8.175,81.5,13.12,
	59.9,14.62,32.9,5.44,12.56,5.44,32,13.95,75.8,20,10.42,
	59.5,21.67,8.55,62,20.2,7.76,3.75,11.81,54.7,23.7,11.55,
	61.3,17.7,8.74,59.2,16.3,8.62,81,4.87,14.62,81.7,17.17,
    81.3,28.9]);
plot(cw2, x = "x", y = "y")


    

    
Out[2]:

In [3]:

    

fm2 = fit(Chwirut(y ~ x,cw2))


    

    
Out[3]:





Nonlinear least squares fit to 54 observations

     Estimate Std.Error t value Pr(>|t|)
R0    193.599   24.9701 7.75323    <1e-9
rc   0.166576 0.0383033 4.34888    <1e-4
m     2.35223  0.593953 3.96029   0.0002

Residual sum of squares at estimates: 513.048
Residual standard error = 3.17171 on 51 degrees of freedom

In [4]:

    

pur = dataset("datasets","Puromycin");
purtrt = sub(pur, pur[:State] .== "treated");
plot(purtrt,x="Conc",y="Rate")


    

    
Out[4]:

In [5]:

    

pm1 = fit(MicMen(Rate ~ Conc,purtrt),true)


    

    




Iteration:  1, rss = 1679.58, cvg = 0.257787 at [201.837,0.0484065]
   Incr: [0.012547]
  f = 1.0, rss = 1211.92
Iteration:  2, rss = 1211.92, cvg = 0.0122645 at [210.623,0.0609536]
   Incr: [0.00280048]
  f = 1.0, rss = 1195.66
Iteration:  3, rss = 1195.66, cvg = 0.000161307 at [212.448,0.063754]
   Incr: [0.000331041]
  f = 1.0, rss = 1195.45
Iteration:  4, rss = 1195.45, cvg = 1.56158e-6 at [212.661,0.0640851]
   Incr: [3.27084e-5]
  f = 1.0, rss = 1195.45
Iteration:  5, rss = 1195.45, cvg = 1.4557e-8 at [212.681,0.0641178]
   Incr: [3.15934e-6]
  f = 1.0, rss = 1195.45
Iteration:  6, rss = 1195.45, cvg = 1.35192e-10 at [212.684,0.0641209]
   Incr: [3.04477e-7]
  f = 1.0, rss = 1195.45







    
Out[5]:





Nonlinear least squares fit to 12 observations

      Estimate  Std.Error t value Pr(>|t|)
Vm     212.684    6.94715 30.6145   <1e-10
K    0.0641212 0.00828095 7.74323    <1e-4

Residual sum of squares at estimates: 1195.45
Residual standard error = 10.9337 on 10 degrees of freedom

In [6]:

    

pm2 = fit(MicMen(Rate ~ Conc,sub(pur,pur[:State] .== "untreated")))


    

    
Out[6]:





Nonlinear least squares fit to 11 observations

      Estimate  Std.Error t value Pr(>|t|)
Vm      160.28    6.48024 24.7336    <1e-8
K    0.0477081 0.00778187 6.13068   0.0002

Residual sum of squares at estimates: 859.604
Residual standard error = 9.773 on 9 degrees of freedom

In [7]:

    

@time fit(MicMen(Rate ~ Conc,sub(pur,pur[:State] .== "untreated")));


    

    




elapsed time: 0.005277077 seconds (186648 bytes allocated)