I am just trying to learn the logistic map, so here are some notes and the R code snippets. Basically what the logistic map does, is a equation that returns the next value based on current value. The formula is Xn+1 = R * Xn * (1-Xn). - Xn is the value of X in the nth iteration. - Xn+1 is the next value of Xn, which we calculate. - R is the parameter. This equation has been used in several real life problems to model the situation. One example is [population growth](https://plus.maths.org/content/maths-minute-logistic-map). In its crude form, it looks like this when coding in R. ``` R=3 x0=0.5 x1=R*x0*(1-x0) x2=R*x1*(1-x1) x3=R*x2*(1-x2) x4=R*x2*(1-x3) x5=R*x2*(1-x4) x6=R*x2*(1-x5) ``` We can try to plot it and iterate over many rounds through a loop. ``` R=3 a=0.5 points<-c(a) for (i in 1:30){ b<-R*a*(1-a) a=b points[i]<-a } plot(points) ```  We see a stabilisation with R being 3. This is also call a fixed point when there is a stabilisation to a value. Let's try with R being 3.4. ``` R=3.4 a=0.5 points<-c(a) for (i in 1:30){ b<-R*a*(1-a) a=b points[i]<-a } plot(points) ``` Now we see a bistable state, i.e. X value fluctuating up and down.  Apparently, at R of approximately 3.5699456, not far from 3.4, something strange starts to happen. ``` R=3.5699456 a=0.5 points<-c(a) for (i in 1:30){ b<-R*a*(1-a) a=b points[i]<-a } plot(points) ``` The bistable states starts to be changed.  And when R is 3.65, some kind of messy plot emerges.  At R at 3.999999999999...  Finally, at R = 4.0. We get back another fixed point. 
author | snippets |
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