maxent原版英文說明-wenkub
2023-04-07 13:01:31 本頁面
【正文】 closely related to “deviance”, as used in statistics.The run produces a number of output files, of which the most important is an html file called “”. Part of this file gives pointers to the other outputs, like this:Looking at a predictionTo see what other (more interesting) content there can be in c:\temp\tutorialdata\outpus\, we will turn on a couple of options and rerun the model. Press the “Make pictures of predictions” button, then click on “Settings”, and type “25” in the “Random test percentage” entry. Lastly, press the “Run” button again. You may have to say “Replace All” for this new run. After the run pletes, the file contains this picture:The image uses colors to show prediction strength, with red indicating strong prediction of suitable conditions for the species, yellow indicating weak prediction of suitable conditions, and blue indicating very unsuitable conditions. For Bradypus, we see strong prediction through most of lowland Central America, wet lowland areas of northwestern South America, the Amazon basin, Caribean islands, and much of the Atlantic forests in southeastern Brazil. The file pointed to is an image file (.png) that you can just click on (in Windows) or open in most image processing software. The test points are a random sample taken from the species presence localities. Test data can alternatively be provided in a separate file, by typing the name of a “Test sample file” in the Settings panel. The test sample file can have test localities for multiple species.Statistical analysisThe “25” we entered for “random test percentage” told the program to randomly set aside 25% of the sample records for testing. This allows the program to do some simple statistical analysis. It plots (testing and training) omission against threshold, and predicted area against threshold, as well as the receiver operating curve show below. The area under the ROC curve (AUC) is shown here, and if test data are available, the standard error of the AUC on the test data is given later on in the web page.A second kind of statistical analysis that is automatically done if test data are available is a test of the statistical significance of the prediction, using a binomial test of omission. For Bradypus, this gives:Which variables matter?To get a sense of which variables are most important in the model, we can run a jackknife test, by selecting the “Do jackknife to measure variable important” checkbox . When we press the “Run” button again, a number of models get created. Each variable is excluded in turn, and a model created with the remaining variables. Then a model is created using each variable in isolation. In addition, a model is created using all variables, as before. The results of the jackknife appear in the “” files in three bar charts, and the first of these is shown below.We see that if Maxent uses only pre6190_l1 (average January rainfall) it achieves almost no gain, so that variable is not (by itself) a good predictor of the distribution of Bradypus. On the other hand, October rainfall (pre619
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