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This R Programming tutorial for beginners covers the following topics: Introduction to R; What is R programming language? Why use R Programming? History of R; Features of R Programming; Applications of R; Companies using R; Salary trends for R Programming; Conclusion
5 dni temu · Taxonomic Analysis with R. Overview. Teaching: 40 min Exercises: 20 min. Questions. How can we know which taxa are in our samples? How can we compare depth-contrasting samples? How can we manipulate our data to deliver a message? Objectives. Manipulate data types inside your phyloseq object.
5 dni temu · Calculates p-values from a set of observed test statistics and simulated null test statistics. Usage. empPvals(stat, stat0, pool =TRUE) Arguments. Details. The argument stat must be such that the larger the value is the more deviated (i.e., "more extreme") from the null hypothesis it is.
5 dni temu · Data Analysis is a process of studying, cleaning, modeling, and transforming data with the purpose of finding useful information, suggesting conclusions, and supporting decision-making. This Data Analytics Tutorial will cover all the basic to advanced concepts of Excel data analysis like data visualization, data preprocessing, time series, data ...
2 dni temu · This Data Science tutorial for beginners helps you get an in-depth knowldge in the domain. Learn Data Science from scratch to elevate your career.
4 dni temu · So, we have values increasing positively to the right, and negatively to the left. To make them both positive, we can take the absolute value, or abs: abs(r) do our usual invert trick: 1 - abs(r) The invert lets the numbers go negative beyond the area we're interested in, so we'll clamp the result to be between 0 and 1: clamp( 1 - abs(r) )
4 dni temu · This function calculates the Area Under the Curve of the receiver operating characteristic (ROC) plot, or alternatively the precision-recall (PR) plot, for either a model object or two matching vectors of observed binary (1 for occurrence vs. 0 for non-occurrence) and predicted continuous (e.g. occurrence probability) values, respectively.
