maxent原版英文说明.doc

1RESM 575 Spatial Analysis Spring 2010Lab 6 Maximum Entropy Assigned:Monday March 1 Due: Monday March 820 pointsThis lab exercise was primarily written by Steven Phillips, Miro Dudik and Rob Schapire, with support from AT for example, if the gain is 2, it means that the average sample likelihood is exp(2) ≈ 7.4 times higher than that of a random background pixel. The uniform distribution has gain 0, so you can interpret the gain as representing how much better the distribution fits the sample points than the uniform distribution does. The gain is closely related to “deviance”, as used in statistics.4The run produces a number of output files, of which the most important is an html file called “bradypus.html”. 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\tutorial- data\outpus\bradpus_variegatus.html, 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 completes, the file bradypus.html 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 5South America, the Amazon basin, Caribean islands, and much of the Atlantic forests in south-eastern 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:6Which 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 “bradypus.html” files in three bar charts, and the first of these is shown below.7We 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 (pre6190_l10) is a much better predictor. Turning to the lighter blue bars, it appears that no variable has a lot of useful information that is not already contained in the others, as omitting each one in turn did not decrease the training gain much.The bradypus_variegatus.html file has two more jackknife plots, using test gain and AUC in place of training gain. This allows the importance of each variable to be measure both in terms of the model fit on training data, and its predictive ability on test data.How does the prediction depend on the variables?Now press the “Create response curves”, deselect the jackknife option, and rerun the model. This results in the following section being added to the “bradypus_variegatus.html” file:Each of the thumbnail images can be clicked on to get a more detailed plot. Looking at frs6190_ann, we see that the response is highest for frs6190_ann = 0, and is fairly high 8for values of frs6190_ann below about 75. Beyond that point, the response drops off sharply, reaching -50 at the top of the variable’s range.So what do the values on the y-axis mean? The maxent model is an exponential model, which means that the probability assigned to a pixel is proportional to the exponential of some additive co。