![]() Think about drawing \(n\) balls from an urn of infinite size containing \(D\) different colors of balls. The Multinomial distribution is a very important distribution that provides a good model for many real world counting processes. Here I have chosen to focus on the Multinomial distribution, however, much of what I discuss also relates to the Multivariate Hypergeometric Distribution as well. I will focus on describing how counting processes introduce uncertainty into estimates of relative abundances and I will end with a discussion of how understanding the Multinomial has impacted my view of analyses of sequence count data (e.g., data from 16s studies of the microbiome, RNA-seq, and more). In this process I created a few visualizations that I thought might help others visualize the Multinomial distribution. In the next edition of this blog we will look at how to use the ggplot2 package to add colour and a wide range of other features to our graphs.Lately I have been working on figures for a manuscript. Of course, we have not yet even drawn upon any of R's custom plotting packages. Moreover, the code would not increase even if we were plotting 100 charts. But note that once we have created the plotM function, we only have to write 3 lines of code to make 4 separate charts. Okay, so the graph does not look like it came from NASA – or to be honest with NASA's ailing reputation maybe it does. Hence we can iterate over a vector containing the values of a, calling plotM each time. Whatever plots we now create will be placed sequentially into this grid. Using the par(mfrow=c(2,2)) we create a 2x2 grid in which to place the next four plots. Finally, we use the abline function to plot a linear fit to highlight the trend. The next line simply creates a standard R plot of y versus z. The lineįilters the data frame to include only those rows where the variable a=l. This function takes an argument "l" that determines which value of the variable we are plotting. The relevant code to create this plot starts with the function " plotM", standing for plot Multiple. It seems sensible to plot z against y for each of these different a values. A preliminary inspection shows that there are only 4 observed values of a. Suppose that we are analysing this data set, unaware of the relationship between the x,y and z variables. Three of these variables are randomly generated, with the z variable dependent upon the other 3. The first few lines of code create a data frame with 4 variables: x,y,z,a. ![]() # Loop through each value of "a" and call the plotM function # Create a grid to plot the different values of "a" Main=paste("Value of key variable: ",toString(l)))Ībline(lm(df.temp$z~df.temp$y),col="red") Plot(df.temp$y,df.temp$z,xlab="Y Value",ylab="Z Value", # Create a function that plots the value of "z" against the "y" value We start with a simple script that allows us to plot several graphs at the same time, each with a different value of a key variable. ![]() Instead we will progress one layer at a time, adding additional levels of complexity and functionality. ![]() Of course, Rome was not built in a day, and a thorough knowledge of R plotting cannot be built in one. In the next few editions of this blog we will build up a basic repertoire of plotting techniques, focusing on graphics that are sickeningly impressive. I have noticed that the most common reason that people avoid R is that they cannot rapidly make graphs that meet the high standards of their clients. Here at least is my contribution to the collective intelligence of biologicals. No longer can a lack of computing power or software be blamed for a lack of productivity growth: rate-limiting factors are now exclusively human. The level of computational analysis that can be conducted in a few hours with nothing more than a desktop PC, a broadband connection, and copious amounts of caffeine is phenomenal. Although the majority of the time taken consisted of collecting the data and making various adjustments, it took a not inconsiderable amount of work to write the code.Īs I was cursing the apply function – not for the last time I am sure – I suddenly realised the insane level of productivity that I have come to see as “par for the course”. Today, in one of my more productive days, I managed to create a sleek R script that plotted several histograms in a lattice, allowing for easy identification of the underlying trend.
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