Sine curve fit using lm and nls in R

Question

I am a beginner in curve fitting and several posts on Stackoverflow really helped me.

I tried to fit a sine curve to my data using lm and nls

Answer 1

Not sure if this might help - I get a similar fit using sine only:

y = amplitude * sin(pi * (x - center) / width) + Offset

amplitude =  2.0009690806953033E+00
center = -2.5813588834888215E+01
width =  1.8077550471975817E+02
Offset =  2.6872265116104828E+01

Fitting target of lowest sum of squared absolute error = 3.6755174406241423E+01

Degrees of freedom (error): 90
Degrees of freedom (regression): 3
Chi-squared: 36.7551744062
R-squared: 0.816419142696
R-squared adjusted: 0.810299780786
Model F-statistic: 133.415731033
Model F-statistic p-value: 1.11022302463e-16
Model log-likelihood: -89.2464811027
AIC: 1.98396768304
BIC: 2.09219299292
Root Mean Squared Error (RMSE): 0.625309918107

amplitude = 2.0009690806953033E+00
       std err squared: 1.03828E-02
       t-stat: 1.96374E+01
       p-stat: 0.00000E+00
       95% confidence intervals: [1.79853E+00, 2.20340E+00]
center = -2.5813588834888215E+01
       std err squared: 2.98349E+01
       t-stat: -4.72592E+00
       p-stat: 8.41245E-06
       95% confidence intervals: [-3.66651E+01, -1.49621E+01]
width = 1.8077550471975817E+02
       std err squared: 3.54835E+00
       t-stat: 9.59680E+01
       p-stat: 0.00000E+00
       95% confidence intervals: [1.77033E+02, 1.84518E+02]
Offset = 2.6872265116104828E+01
       std err squared: 5.15458E-03
       t-stat: 3.74289E+02
       p-stat: 0.00000E+00
       95% confidence intervals: [2.67296E+01, 2.70149E+01]

Coefficient Covariance Matrix
[ 0.02542366 0.01786683 -0.05016085 -0.00652111]
[ 1.78668314e-02 7.30548346e+01 -2.18160818e+01 1.24965136e-01]
[ -5.01608451e-02 -2.18160818e+01 8.68860810e+00 -1.27401806e-02]
[-0.00652111 0.12496514 -0.01274018 0.0126217 ]

James Phillips zunzun@zunzun.com