Multifractal estimation - Day 3

Multifractal estimation

  • We will use box counting to estimate $D_q$

$$D_q = \lim_{r \to 0} \frac{1}{q-1}\frac{log \left[ M_q(r) \right]}{\log r}$$

  • We cover the object with square non-overlapping boxes of size $r$ and repeat the procedure using a range of $r$ values. This range is determined by the size of our sample.

  • in practice $D_q$ is estimated by the slope of the scaling relation for a range of $q$

    $$M_q(r) = A r^{-\tau_q}$$

    $$\log(M_q(r)) = \log A - \tau_q log(r)$$

    $$D_q = \frac{\tau_q}{(q-1)}$$

  • Estimating $D_q$ is not very difficult but is no so simple. We need the following steps:

    1. Chose a range of $q$ and a range of $r$

    2. Start with the first $q$

    3. Start with the first $r$

    4. Cover the object with boxes of side $r_1$

    5. Calculate $M_q(r_1)$ store the result.

    6. Do this for all $r$'s in the range.

    7. Now we have all $r$'s and $M_q(r)$ to calculate $\tau_q$ by regression

    8. Repeat 3. - 7. for all the $q$s

  • If somebody want to do this in R as a final project, we cant talk later.

  • We will use an open source software mfSBA available at http://github/lsaravia/mfsba

Using mfSBA

  • To automate repetitive task and perform visualizations we will use R but we need to know how to use mfSBA

  • To invoke an external program from R we use:


    or under windows

  • In the console appears some information about the parameters

    Parameter Description
    inputFile The file we want to use
    qFile A file that indicates the $q$'s range
    minBox The size of the first box
    maxBox The size of the last box.
    The intermediate boxes are calculated as powers of 2.
    numBoxSizes limit the number of boxes, not very useful.
    option we will use the option S that makes $\sum p_i=1$
  • mfSBA can use tif files but I can’t make it work in windows so we will use its own format that is called “sed”. Sed files are text files with a matrix structure.

      s <- "./mfSBA K1_laSelva.sed q.sed 2 512 20 S"
  • This generates several files:


    a.file/f.file have data for $f(\alpha)$ and $\alpha$ an equivalent way to express $D_q$ that we will not use.

    t.file has $\log( M_q(r))$ and the box sizes used, useful to check the validity of the regression.

    s.file has $\tau_q$, $\alpha$ and $f(\alpha)$, the $R^2$'s and standard deviations, so we will use mostly this file.

      sf <- read.table("s.K1_laSelva.sed", header=T)


  • make a function to read the s.file and discard all the things we don’t need and calculate $D_q$

  • The first step to do a function like this is to test if commands works.

  • A possible answer:

      readDq <- function(sname) {
          pp <- read.table(sname, header=T)
          pp$Dq  <- with(pp,ifelse(q==1,alfa,Tau/(q-1)))
          pp$SD.Dq  <- with(pp,ifelse(q==1,SD.alfa,abs(SD.Tau/(q-1))))
          pp$R.Dq <- with(pp,ifelse(q==1,R.alfa,R.Tau))

Analyze the output

  • First use our function to get all we need

      dq <- readDq("s.K1_laSelva.sed")
      pp <- ggplot(dq, aes(x=R.Dq)) + geom_histogram()
  • $R^2$ seems very high, that means a very good fit, we could check more using the t.file. Let’s try:

      tf <- read.table("t.K1_laSelva.sed", header=T)

    Oops error: the first line do not have the same number of labels as columns.

      tf <- read.table("t.K1_laSelva.sed", skip=1)
      names(tf)[1] <- "Box"
      names(tf)[2] <- "logBox"
  • And check with the Durbin-Watson statistic

      lm0 <- lm(V3~logBox,data=tf)
  • Let’s check another $q$

      lm0 <- lm(V23~logBox,data=tf)
  • Thus there is some autocorrelation. Let’s check graphically

      pp <- ggplot(data=tf,aes(x=logBox,y=V23))+geom_point()+geom_smooth(method="lm")
  • When there is low $R^2$ or autocorrelation is better to check graphically.

$D_q$ for La Selva

  • We can plot $D_q$ with using SD as error bars

      pp <- ggplot(dq, aes(x=q, y=Dq)) +
        geom_errorbar(aes(ymin=Dq-SD.Dq, ymax=Dq+SD.Dq), width=.1) +
        geom_line() +

Exercise 1

  • Plot $\tau_q$

Exercise 2

  • Can we make more functions to automate the task if we need to repeat the analysis?

  • A function to read the data, show the $R^2$ histogram and add $D_q$ to a data frame.

  • A function to plot all the $D_q$ linear fit for all $q$