In my dataset, I have split my data into testing and training. I have used the training data to fit a sinusoidal curve.
freq = seq(from=21, to=80, by=0.5) amp = c(-45.22247,-44.87434,-44.30659, -44.104, -44.08027 ,-44.32827, -44.52562, -44.81116, -45.11858, -45.61112, -45.88737, -45.9517, -46.00517,-45.78666, -45.64515, -45.32656, -45.12009, -44.92572, -44.58918,-44.18919, -43.75679, -43.4801, -43.13948, -42.86826 ,-42.59199,-42.23767, -42.06125, -41.99265 ,-41.91103, -41.96361, -42.10183,-42.47087, -42.86456, -43.17823 ,-43.41674, -43.6427 ,-43.79429,-43.71881, -43.58482 ,-43.53886, -43.29379, -43.00895 ,-42.69728,-42.331 ,-42.11675, -41.84402, -41.63068 ,-41.28213, -40.88509,-40.64461 ,-40.47236 ,-40.49424, -40.73463, -41.02037, -41.49101, -41.95741 ,-42.36682, -42.83582 ,-43.08535, -43.16926, -43.20281,-43.08085, -43.08461, -42.96666, -42.70192, -42.57608 ,-42.30105,-42.01737, -41.58435 ,-41.24757, -40.96 ,-40.68981 ,-40.39803,-40.2478, -40.23024, -40.48872, -40.68104, -41.04532, -41.53119,-41.88053, -42.17658, -42.32618, -42.41549 ,-42.36513, -42.38881,-42.25508, -42.13853, -41.89402, -41.66664, -41.3869, -41.02896, -40.83986, -40.44932 ,-40.14661, -39.84292, -39.66413, -39.60376, -39.63048, -39.75595 ,-39.88888, -40.18054 ,-40.491, -40.62274, -40.8282, -40.92761, -41.1448, -41.0102 ,-41.06486, -40.96643,-40.93895 ,-40.72338, -40.58406, -40.44275 ,-40.28842, -40.23578, -40.04179, -39.93855, -39.94855, -39.90564) ssp <- data.frame(freq, amp) A<- (max(ssp$amp)-min(ssp$amp)/2) C<-((max(ssp$amp)+min(ssp$amp))/2) res1<- nlsLM(amp ~ A*sin(omega*freq+phi)+C, data=ssp, start=list(A=A,omega=pi/6,phi=1,C=C)) sumres=summary(res1) co <- coef(res1) #resid(res1) #sum(resid(res1)^2) fit <- function(x, a, b, c, d) {a*sin(b*x+c)+d} # Plot result plot(ssp) curve(fit(x, a=co["A"], b=co["omega"], c=co["phi"], d=co["C"]), add=TRUE ,lwd=2, col="steelblue") This provides me with this fit. Please note that I know the fit could be better, but that isn't necessary for the purposes of this post. It's close enough.
sumres Formula: amp ~ A * sin(omega * freq + phi) + C Parameters: Estimate Std. Error t value Pr(>|t|) A -1.00030 0.20105 -4.975 2.3e-06 *** omega 0.53007 0.01196 44.332 < 2e-16 *** phi -0.31121 0.64156 -0.485 0.629 C -42.19834 0.14390 -293.248 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.552 on 115 degrees of freedom Number of iterations to convergence: 7 Achieved convergence tolerance: 1.49e-08 Now, I have testing data, that I would like to compare this modeled sine wave to. In theory, I should be able to once again use the nls (nonlinear least squares) function to get the residual standard error value, to understand how well this template fits other data. Rather than 'start' any of the variables, I simply inform the function what they are. However, I receive the following error when I do so:
newssp = similar to data above res2<- nls(amp ~ co["A"]*sin(co["omega"]*freq+co["phi"])+co["C"], data=newssp) Error in getInitial.default(func, data, mCall = as.list(match.call(func, : no 'getInitial' method found for "function" objects It is my understanding that this is because the provided values may not be the best fit, but that is what I am looking for. I want to see how well my modeled sine wave fits against raw data. The only thing I can think of, is that for my coefficients, not all of them were significant.
Is there a better way to test how my modeled sine wave compares against raw data? Thanks!
