# Fitting exponential decay with negative y values

I am trying to fit an exponential decay function to y-values that become negative at high x-values, but am unable to configure my nls function correctly.

# Aim

I am interested in the slope of the decay function ($\lambda$ according to some sources). How I get this slope is not important, but the model should fit my data as well as possible (i.e. linearizing the problem is acceptable, if the fit is good; see "linearization"). Yet, previous works on this topic have used a following exponential decay function (closed access article by Stedmon et al., equation 3):

$f(y) = a \times exp(-S \times x) + K$

where S is the slope I am interested in, K the correction factor to allow negative values and a the initial value for x (i.e. intercept).

I need to do this in R, as I am writing a function that converts raw measurements of chromophoric dissolved organic matter (CDOM) to values that researchers are interested in.

# Example data

Due to the nature of the data, I had to use PasteBin. The example data are available here.

Write dt <- and copy the code fom PasteBin to your R console. I.e.

dt <- structure(list(x = ...


The data look like this:

library(ggplot2)
ggplot(dt, aes(x = x, y = y)) + geom_point()


Negative y values take place when $x > 540 nm$.

# Trying to find solution using nls

Initial attempt using nls produces a singularity, which should not be a surprise seeing that I just eyeballed start values for parameters:

nls(y ~ a * exp(-S * x) + K, data = dt, start = list(a = 0.5, S = 0.1, K = -0.1))

# Error in nlsModel(formula, mf, start, wts) :
#  singular gradient matrix at initial parameter estimates


Following this answer, I can try to make better fitting start parameters to help the nls function:

K0 <- min(dt$y)/2 mod0 <- lm(log(y - K0) ~ x, data = dt) # produces NaNs due to the negative values start <- list(a = exp(coef(mod0)[1]), S = coef(mod0)[2], K = K0) nls(y ~ a * exp(-S * x) + K, data = dt, start = start) # Error in nls(y ~ a * exp(-S * x) + K, data = dt, start = start) : # number of iterations exceeded maximum of 50  The function does not seem to be able to find a solution with the default number of iterations. Let's increase the number of iterations: nls(y ~ a * exp(-S * x) + K, data = dt, start = start, nls.control(maxiter = 1000)) # Error in nls(y ~ a * exp(-S * x) + K, data = dt, start = start, nls.control(maxiter = 1000)) : # step factor 0.000488281 reduced below 'minFactor' of 0.000976562  More errors. Chuck it! Let's just force the function to give us a solution: mod <- nls(y ~ a * exp(-S * x) + K, data = dt, start = start, nls.control(maxiter = 1000, warnOnly = TRUE)) mod.dat <- data.frame(x = dt$x, y = predict(mod, list(wavelength = dt$x))) ggplot(dt, aes(x = x, y = y)) + geom_point() + geom_line(data = mod.dat, aes(x = x, y = y), color = "red")  Well, this was definitely not a good solution... # Linearizing the problem Many people have linearized their exponential decay functions with a success (sources: 1, 2, 3). In this case, we need to make sure that no y value is negative or 0. Let's make the minimum y value as close to 0 as possible within the floating point limits of computers: K <- abs(min(dt$y))
dt$y <- dt$y + K*(1+10^-15)

fit <- lm(log(y) ~ x, data=dt)
ggplot(dt, aes(x = x, y = y)) + geom_point() +
geom_line(aes(x=x, y=exp(fit$fitted.values)), color = "red")  Much better, but the model does not trace y values perfectly at low x values. Note that the nls function would still not manage to fit the exponential decay: K0 <- min(dt$y)/2
mod0 <- lm(log(y - K0) ~ x, data = dt) # produces NaNs due to the negative values
start <- list(a = exp(coef(mod0)[1]), S = coef(mod0)[2], K = K0)
nls(y ~ a * exp(-S * x) + K, data = dt, start = start)

# Error in nlsModel(formula, mf, start, wts) :
#  singular gradient matrix at initial parameter estimates


# Do the negative values matter?

The negative values are obviously a measurement error as absorption coefficients cannot be negative. So what if I make the y values generously positive? It is the slope I am interested in. If addition does not affect the slope, I should be settled:

dt$y <- dt$y + 0.1

fit <- lm(log(y) ~ x, data=dt)
ggplot(dt, aes(x = x, y = y)) + geom_point() + geom_line(aes(x=x, y=exp(fit$fitted.values)), color = "red")  Well, this did not go that well...High x values should obviously be as close to zero as possible. # The question I am obviously doing something wrong here. What is the most accurate way to estimate slope for an exponential decay function fitted on data that have negative y values using R? • nls converged for me using the starting values$a=1, S =0.01, K=-0.0001$. Alternatively, you could use the self-starting function: nls(y~SSasymp(x, Asym, r0, lrc), data = dt). That converges for me too. – COOLSerdash Dec 15 '17 at 9:59 ## 2 Answers Use a selfstarting function: ggplot(dt, aes(x = x, y = y)) + geom_point() + stat_smooth(method = "nls", formula = y ~ SSasymp(x, Asym, R0, lrc), se = FALSE)  fit <- nls(y ~ SSasymp(x, Asym, R0, lrc), data = dt) summary(fit) #Formula: y ~ SSasymp(x, Asym, R0, lrc) # #Parameters: # Estimate Std. Error t value Pr(>|t|) #Asym -0.0001302 0.0004693 -0.277 0.782 #R0 77.9103278 2.1432998 36.351 <2e-16 *** #lrc -4.0862443 0.0051816 -788.604 <2e-16 *** #--- #Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # #Residual standard error: 0.007307 on 698 degrees of freedom # #Number of iterations to convergence: 0 #Achieved convergence tolerance: 9.189e-08 exp(coef(fit)[["lrc"]]) #lambda #[1] 0.01680222  However, I would seriously consider if your domain knowledge doesn't justify setting the asymptote to zero. I believe it does and the above model doesn't disagree (see the standard error / p-value of the coefficient). ggplot(dt, aes(x = x, y = y)) + geom_point() + stat_smooth(method = "nls", formula = y ~ a * exp(-S * x), method.args = list(start = list(a = 78, S = 0.02)), se = FALSE, #starting values obtained from fit above color = "dark red")  • Perfect. I did not know about SSasymp function. Thank you! I believe the researchers want to refer to the article I cited in the question and use the K term, but I will suggest them to modify their equation. I think they want to keep the K, because negative values mean that the instrument did not behave as expected, but they are interested in the slope. Removing the negative asymptote might affect the slope in some instances. – Mikko Dec 15 '17 at 10:37 • @Mikko If you measure absorption and the asymptote gets significantly zero, I would say that you have problems with your calibration or instrument stability. – Roland Dec 15 '17 at 11:12 • The problem often takes place when water is very clear (oceanic water). Some values become below zero. I think we have an instrument that suffers from temperature problems. When it overheats, the values become unstable, but these details should probably not be handled in Crossvalidated. – Mikko Dec 15 '17 at 11:44 This question has relationships with several other questions I have three additional remarks regarding some points in this question. ### 1: Why linearized model does not fit well the large values of $$y$$ Much better, but the model does not trace y values perfectly at low x values. The linearized fit is not minimizing the same residuals. At the logarithmic scale the residuals for smaller values will be larger. The image below shows the comparison by plotting the y-axis on a log scale in the right image: When necessary you could add weights to the least squares loss function. ### 2: Using linearized fit as starting values After you have obtained estimates with your linearized fit you could have used these as starting point for the non linear fitting.* # vectors x and y from data x <- dat$$x y <- dat$$y # linearized fit with zero correction K <- abs(min(y)) dty <- y + K*(1+10^-15) fit <- lm(log(dty) ~x) # old fit that had a singluar gradient matrix error # nls(y ~ a * exp(-S * x) + K, # start = list(a = 0.5, # S = 0.1, # K = -0.1)) # # new fit fitnls <- nls(y ~ a * exp(-S * x) + K, start = list(a = exp(fit$$coefficients[1]), S = -fit$$coefficients[2], K = -0.1)) #  ### 3: Using a more general method to obtain the starting point If you have enough points then you can also obtain the slope without having to worry about asymptotic value and negative values (no computation of a logarithm needed). You can do this by integrating the data points. Then with $$y = a e^{sx} + k$$ and $$Y = \frac{a}{s} e^{sx} + kx + Const$$ you can use a linear model to obtain the value of $$s$$ by describing $$y$$ as a linear combination of the vectors $$Y$$, $$x$$ and an intercept: $$\begin{array}{rccccl}y &=& a e^{sx} + k &=& s(\frac{a}{s} e^{s x} + k x + Const) &- s k x - s Const \\ &&&=& sY &- sk x - s Const \end{array}$$ The advantage of this method (see Tittelbach and Helmrich 1993 "An integration method for the analysis of multiexponential transient signals") is that you can extend it to more than a single exponentially decaying component (adding more integrals). # # using Tittelbach Helmrich # # integrating with trapezium rule assuming x variable is already ordered ys <- c(0,cumsum(0.5*diff(x)*(y[-1]+y[-length(y)]))) # getting slope parameter modth <- lm(y ~ ys + x) slope <- modth$coefficients[2]

# getting other parameters
modlm <- lm(y ~ 1 + I(exp(slope*x)))
K <- modlm$$coefficients[1] a <- modlm$$coefficients[2]

# fitting with TH start

fitnls2 <- nls(y ~ a * exp(-S * x) + K,
start = list(a = a,
S = -slope,
K = K))


Footnote: *This use of the slope in the linearized problem is exactly what what the SSasymp selfstarting function does. It first estimates the asymptote

> stats:::NLSstRtAsymptote.sortedXyData
function (xy)
{
in.range <- range(xy$$y) last.dif <- abs(in.range - xy$$y[nrow(xy)])
if (match(min(last.dif), last.dif) == 2L)
in.range[2L] + diff(in.range)/8
else in.range[1L] - diff(in.range)/8
}


and then the slope by (subtracting the asymptote value and taking the log values)

> stats:::NLSstAsymptotic.sortedXyData
function (xy)
{
xy\$rt <- NLSstRtAsymptote(xy)
setNames(coef(nls(y ~ cbind(1, 1 - exp(-exp(lrc) * x)), data = xy,
start = list(lrc = log(-coef(lm(log(abs(y - rt)) ~ x,
data = xy))[[2L]])), algorithm = "plinear"))[c(2,
3, 1)], c("b0", "b1", "lrc"))
}


Note the line start = list(lrc = log(-coef(lm(log(abs(y - rt)) ~ x, data = xy))[[2L]]))

Sidenote: In the special case that $$K=0$$ you can use

plot(x,y)
mod <- glm(y~x, family = gaussian(link = log), start = c(2,-0.01))
lines(x,exp(predict(mod)),col=2)


which models the observed parameter $$y$$ as

$$y = exp(X\beta) + \epsilon = exp(\beta_0) \cdot exp(\beta_1 \cdot x) + \epsilon$$