In ecology, we often use the logistic growth equation:

$$ N_t = \frac{ K N_0 e^{rt} }{K + N_0 e^{rt-1}} $$


$$ N_t = \frac{ K N_0}{N_0 + (K -N_0)e^{-rt}} $$

where $K$ is the carrying capacity (maximum density reached), $N_0$ is the initial density, $r$ is the growth rate, $t$ is time since initial.

The value of $N_t$ has a soft upper bound $(K)$ and a lower bound $(N_0)$, with a strong lower bound at $0$.

Furthermore, in my specific context, measurements of $N_t$ are done using optical density or fluorescence, both of which have a theoretical maxima, and thus a strong upper bound.

The error around $N_t$ is thus probably best described by a bounded distribution.

At small values of $N_t$, the distribution probably has a strong positive skew, while at values of $N_t$ approaching K, the distribution probably has a strong negative skew. The distribution thus probably has a shape parameter that can be linked to $N_t$.

The variance may also increase with $N_t$.

Here is a graphical example

enter image description here



which can be produced in r with

  • What would be the theoretical error distribution around $N_t$ (in consideration of both the model and the empirical information provided)?

  • How doe the parameters of this distribution relate to the value of $N_t$ or time (if using parameters were the mode can not be directly associated with $N_t$ eg. logis normal)?

  • Does this distribution have a density function implemented in $R$?

Directions explored so far:

  • Assuming normality around $N_t$ (leads to over estimates of $K$)
  • Logit normal distribution around $N_t/max$, but difficulty in fitting shape parameters alpha and beta
  • Normal distribution around the logic of $N_t/max$
  • 1
    $\begingroup$ By focusing on the distribution of error this question reflects sophisticated thinking about a model, but please note that the error distribution for a functional form does not necessarily have any relationship to the form itself. The ingredients of a valid answer are instead to be found in information about how the growth occurs, about natural variations in $K$ and $r$ over time (which will necessarily be absorbed in the error), about possible model mis-specification, and how $N_t$ (and $t$) are measured. $\endgroup$
    – whuber
    Commented Mar 31, 2012 at 22:08
  • $\begingroup$ @whuber, I tried to address some of your comments in a recent edit. $\endgroup$ Commented Apr 1, 2012 at 1:41
  • 1
    $\begingroup$ 5 think that if you can characterize the properties of the noise distribution the way you have then you can pick a parametric form with those properties. I think to summarize the family must 1. be defined on a finite interval, 2. allow left skew, right skew and symmetry. and 3. has a variance that increases as Nt increases. The beta distribution fits the bill for 1 and 2. The fixed interval is [0, 1]. So to allow the variance to increase we could add a parameter c that spreads the distribution to the intervsl [0,c]. $\endgroup$ Commented May 5, 2012 at 13:14

2 Answers 2


As Michael Chernick pointed out, the scaled beta distribution makes the best sense for this. However, for all practical purposes, and expecting that you will NEVER get the model perfectly right, you would be better off just modeling the mean via nonlinear regression according to your logistic growth equation and wrapping this up with standard errors that are robust to heteroskedasticity. Putting this into maximum likelihood context will create a false feeling of great accuracy. If the ecological theory would produce a distribution, you should fit that distribution. If your theory only produces the prediction for the mean, you should stick to this interpretation and don't try to come up with anything more than that, like a full-blown distribution. (Pearson's system of curves was surely fancy 100 years ago, but random processes do not follow differential equations to produce the density curves, which was his motivation with these density curves -- rather, one would think in terms of the central limit theorem as a way things may be working to produce distributions approximating what we see in practice.) I would expect that the variability goes up with the $N_t$ itself -- I am thinking of the Poisson distribution as an example -- and I am not entirely sure that this effect will be captured by the scaled beta distribution; it would, on the contrary, get compressed as you pull the mean towards its theoretical upper bound, which you may have to do. If your measurement device has an upper bound of the measurements, it does not mean that your actual process must have an upper bound; I would rather say that the measurement error introduced by your devices becomes critical as the process reaches that upper bound of being measured reasonably accurately. If you confound the measurement with the underlying process, you should recognize that explicitly, but I would imagine you have a greater interest in the process than in describing how your device works. (The process will be there 10 years from now; new measurement devices may become available, so your work will become obsolete.)

  • $\begingroup$ Thanks a bunch! I agree that a separation of process and measure is interesting. I would however suggest that most measurement methods have this strong upper bound, but it might be important to isolate this. If I where to use the scaled beta, despite your warning about MLE fitting confidence, any suggestions as to how to relate the shape parameters to this system to model variables to allow for MLE? $\endgroup$ Commented May 7, 2012 at 12:38
  • $\begingroup$ If you are convinced that your boundaries are really important in your application, you can just stick to this scaled beta. All I am saying is that I am not convinced. There are models for truncated data, where all you know is that the actual value exceeds the maximum you can measure; they are sometimes used together with top-coding of incomes, whereas for confidentiality reasons the incomes greater than say USD 100K/year are truncated down to USD 100K/year. $\endgroup$
    – StasK
    Commented May 8, 2012 at 2:47

@whuber is correct that there is no necessary relationship of the structural part of this model to the distribution of error terms. So there is no answer to your question for the theoretical error distribution.

This doesn't mean that it isn't a good question though - just that the answer will have to be largely empirical.

You seem to be assuming that the randomness is additive. I see no reason (other than computational convenience) for this to be the case. Is an alternative that there is a random element somewhere else in the model? For example see the following, where randomness is introduced as Normally distributed with mean of 1, variance the only thing to estimate. I have no reason for thinking this is the right thing to do other than that it produces plausible results that seem to match what you want to see. Whether it would be practical to use something like this as the basis for estimating a model I don't know.

loggrowth <- function(K, N, r, time, rand=1){

plot(1:100, loggrowth(100,20,.08,1:100, rnorm(100,1,0.1)), 
    type="p", ylab="", xlab="time")
lines(1:100, loggrowth(100,20,.08,1:100))

enter image description here

  • $\begingroup$ In this case, you could have Nt values below zero and above the hard upper boundary. Furthermore, noise is expected in all parameters (not necessarily in the product of a parameter with time), hence the noise on the response variable. I would still be interested in the maximum likelihood interpretation of you approach. $\endgroup$ Commented May 5, 2012 at 13:01
  • $\begingroup$ This doesn't allow the distribution to be bounded for each Nt and does not allow the noise component to be skewed. I don't know if my idea of a scaled beta distribution has been used in the literature but it satisfies the restrictions well. I haven't tried it but maybe maximum likelihood could be tried. I am not sure but maybe there would be a problem if c is included in the likelihood estimation. Maybe c could be estimated separately based only on Nt and then the rest of the model could be fit by maximum likelihood for each fixed Nt. $\endgroup$ Commented May 5, 2012 at 13:24
  • $\begingroup$ I am just thinking out loud. Does anyone think this problem could be turned into a good research paper? $\endgroup$ Commented May 5, 2012 at 13:24
  • $\begingroup$ A paper from 1966 did look into this a little, however I have not seen one more recent. I maybe things have changed since? jstor.org/discover/10.2307/… $\endgroup$ Commented May 7, 2012 at 12:29
  • $\begingroup$ Please let me know if you decide to go this route. $\endgroup$ Commented May 7, 2012 at 12:31

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