## Nonlinear fitting and parameter estimation

(This is seventh article in the series Using R for Gambet statistical analysis.[1]) To this point, we have carried out basic statistical calculations with R. With some effort, we could perform the operations in spreadsheets or our own programs. In this article, we’ll turn to a much more sophisticated calculation that would be difficult to reproduce: […]

## Working with RStudio: multi-dimensional fitting

(This is sixth article in the series Using R for Gambet statistical analysis.[1]) RStudio is an integrated development environment (IDE) for working with R, similar to the IDEs available for most computer languages. Figure 1 shows a screenshot. There are work areas for the script editor, console and plots. The difference is that they are combined […]

## Linear curve fitting and plotting, part B

(This is fifth article in the series Using R for Gambet statistical analysis.[1]) We’ll continue the examination of linear fitting and plots with another example, a statistical Fourier analysis. It illustrates that 1) the lm() command can deal with any function and 2) data frame components need not always be called y and x. Here is […]

## Linear curve fitting and plotting, part A

(This is fourth article in the series Using R for Gambet statistical analysis.[1]) In this article and the next, we’ll discuss linear curve fitting (or parameter estimation). Here, the term linear does not mean we are restricted to linear fitting functions, but rather that the parameters we seek are linear multipliers of the functions. To illustrate […]

## Creating and importing CSV files

(This is third article in the series Using R for Gambet statistical analysis.[1]) Data from experiments and computer outputs may include thousands of items, so it is clearly impractical to enter values in R by typing them. Generally, data are recorded in files and we use special R commands to read them. R recognizes some binary […]