More advanced users may prefer to use R from their computer’s command prompt (Windows) or terminal (Mac OS X and Linux), especially users interested in sequencing and other high throughput biological data. Package libraries can be downloaded and installed from the point-and-click GUI in R, but otherwise most commands and procedures will need to be executed from commands, or code. There are installation instructions available for Windows, Mac and Linux operating systems on the linked webpage. The R Project software, R, can be used many ways, but the simplest choice is to download the software and its standard graphic user interface (GUI) from. It sounds inconvenient, but the modular nature of the R Project software forces you to customize the software with your own unique blend of basic features and cutting-edge developments created by statistics and biology researchers from all around the world. One key aspect of the R Project software is that its features are broken up into thousands of different “package libraries”, most of which need to be downloaded, installed and loaded to use. These protocols will mostly use software from The R Project for Statistical Computing ( ), which is a free and open source software platform that mostly uses typed commands. Other statistics software requires the use of typed commands and looks a little bit more like computer programming. Some statistics software offers a point-and-click graphic user interface (GUI) familiar to most anyone using Apple Macintosh or Microsoft Windows operating systems and their web browsers, office productivity suites, etc. There are many statistics software options, some free and others quite expensive, some very simple and others quite difficult to learn. The flow between topics shows how researchers will often move from looking at data to choosing new analyses, and how design of experiments can often circle the process back around to looking at newly collected data.īefore delving into specific statistical methods, it may be wise to consider the statistical software you might use to produce your graphs and calculations. The lower left-hand corner shows some methods for statistical design of experiments (Protocol 5), which is used to plan new experiments and choose analyses for existing experiments. When those typical statistical analyses are inadequate, the graphs in the lower right-hand corner show some examples of advanced methods (Protocol 6). Moving to the right, the second table shows how a researcher might choose among basic statistical tests like t-tests and ANOVA (Protocol 2), correlations and regression (Protocol 3), or contingency tables and generalized linear models (Protocol 4). Starting at the upper left, the first table describes how a researcher might choose an appropriate graph (Protocol 1). These concepts and more would be used to determine how a data set should be graphed, which statistical tests should be used to analyze the data and how that information might be used to plan better experiments in the future.įlow-chart illustrating general approaches to use of statistics in immunology. gender is male or female, a person’s disease status can be infected or uninfected). 68.3 inches, 154.8 lbs, 17.1 micrograms, …), and categorical variables, which have a finite list of discrete outcomes (e.g. age, height, weight, mass, concentration of specific compounds, …) that can be measured precisely in fractions of a unit (e.g. Another important concept is the difference between continuous variables (e.g. dependent variables), which are typically the variables outside of our control or the variables most influenced by our experimental conditions. independent variables), which are typically variables we can control or variables we expect to influence an outcome, and response variables (i.e. One key idea is the distinction between predictor variables (i.e. 1) describes the organization of the statistical concepts presented here, starting with statistical graphs and branching out into different statistical tests and concepts for specific types of immunological data.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |