【正文】 默認的是地理上的重疊群不報告。將幾乎永遠是一個二次叢集，幾乎是相同的最可能的集群和幾乎一樣高的似然值，因為擴大或減少的簇大小只有輕微不改變的可能性很。除此之外，也沒有明確的直觀意義的罰款調(diào)整參數(shù)在SaTS can，就可以使用任何一個強大的刑罰（= 1）或中分享到 翻譯結果重試抱歉，系統(tǒng)響應超時，請稍后再試為圓，= 1。主要原因是，橢圓掃描統(tǒng)計的零假設下通常產(chǎn)生一個橢圓最可能的集群因為有更多的橢圓形圓形集群和評價，它往往是一個狹長的橢圓形，因為有更多的人。該SaTS can程序掃描率高的地區(qū)（集群），地區(qū)與低利率，同時或地區(qū)或者高或低利率。值獲得通過蒙特卡洛檢驗假設（數(shù)據(jù)，1957），通過比較等級的最大似然從實際數(shù)據(jù)集的最大可能從隨機數(shù)據(jù)集。似然函數(shù)最大化的所有窗口的位置和大小，和一個與最大似然是最可能的集群。似然函數(shù)的多項式，有序，指數(shù)和正常模式更為復雜，由于更復雜的數(shù)據(jù)的性質(zhì)。時空置換模式使用相同的功能如泊松模型。i()是功能的一個指標。 Kulldorff et al (2006)and Huang et al. for the likelihood functions for these models. The likelihood function for the spatial variation in temporal trends scan statistic is also more plex, as it involves the maximum likelihood estimation of several different trend functions. The likelihood function is maximized over all window locations and sizes, and the one with the maximum likelihood constitutes the most likely cluster. This is the cluster that is least likely to have occurred by chance. The likelihood ratio for this window constitutes the maximum likelihood ratio test statistic. Its distribution under the nullhypothesis is obtained by repeating the same analytic exercise on a large number of random replications of the data set generated under the null hypothesis. The pvalue is obtained through Monte Carlo hypothesis testing (Dwass, 1957), by paring the rank of the maximum likelihood from the real data set with the maximum likelihoods from the random data sets. If this rank is R, then p = R / (1 + simulation). In order for p to be a ‘nice looking’ number, the number of simulations is restricted to 999 or some other number ending in 999 such as 1999, 9999 or 99999. That way it is always clear whether to reject or not reject the null hypothesis for typical cutoff values such as , and . The SaTScan program scans for areas with high rates (clusters), for areas with low rates, or simultaneously for areas with either high or low rates. The latter should be used rather than running two separate tests for high and low rates respectively, in order to make correct statistical inference. The most mon analysis is to scan for areas with high rates, that is, for clusters. NonCompactness Penalty Function When the elliptic window shape is used, there is an option to use a nonpactness (eccentricity) penalty to favor more pact clusters. The main reason for this is that the elliptic scan statistic will under the null hypothesis typically generate an elliptic most likely cluster since there are more elliptic than circular clusters evaluated, and it will often be a long and narrow ellipse, since there are more of those. At the same time, the concept of clustering is based on a pactness criterion in the sense that the cases in the cluster should be close to each other, so we are more interested in pact clusters. When the nonpactness penalty is used, the pure likelihood ratio is no longer used as the test statistic. Rather, the test statistic is defined as the log likelihood ratio multiplied with a nonpactness penalty of the form [4s/(s+1)2]a, where s is the elliptic window shape defined as the ratio of the length of the longest to the shortest axis of the ellipse. For the circle, s=1. The parameter a is a penalty tuning parameter. With a=0, the penalty function is always 1 irrespectively of s, so that there is never a penalty. When a goes to infinity, the penalty function goes to 0 for all s1, so that only circular clusters are considered. Other than this, there is no clear intuitive meaning of the penalty tuning parameter a. In SaTScan, it is possible to use either a strong penalty (a=1) or a medium size penalty (a=1/2). 似然比檢驗對于每一個位置和大小的掃描窗口，替代假設是，有一個高風險的窗口相比，外。與伯努利，時空置換，有序，指數(shù)和正常模式，一時間需要被指定為每一個案件和伯努利模型，為每個控制以及。由于碎片的數(shù)量增加至無限遠，因此，其尺寸減小為零，離散泊松模型是漸近相當于齊次泊松模型。例如，結果都是相同的命令值的1–2–3–4”和“1–10–100–1000 39。Normal versus Ordinal Model The normal model can be used for categorical data when there are very many categories. As such, it is sometimes a putationally faster alternative to the ordinal model. There is an important difference though. With the ordinal model, only the order of the observed values matters. For example, the results are the same for ordered values ‘1 – 2 – 3 – 4’ and ‘1 – 10 – 100 – 1000’. With the normal model, the results will be different, as they depend on the relative distance between the values used to define the categories. 正常與序模型正常模型可用于分類數(shù)據(jù)時，有非常多的類別。指數(shù)型模型的主要目的是為生存時間數(shù)據(jù)，但可用于任何數(shù)據(jù)在所有的意見是積極的。作為一個近似伯努利型數(shù)據(jù)，離散泊松模型產(chǎn)生稍微保守值。如果可選的網(wǎng)格文件提供，圓而為中心的坐標指定的文件中。報告預計數(shù)是根據(jù)全圓，所以地震/進出口比率應被視為一個下界的真實值時，圓外延伸區(qū)域空間研究。因此，它是一種特殊情況的變量窗口大小的掃描統(tǒng)計描述庫爾多夫（1997）。研究領域不需要是連續(xù)的，例如可能由五個不同的島嶼。在SaTS can，研究區(qū)可以是任何集合的凸多邊形，凸區(qū)域內(nèi)的任何數(shù)量的直線。隨機方面的數(shù)據(jù)由隨機空間位置，和我們有興趣，看看是否有任何群是不可能發(fā)生，如果意見是獨立隨機分布在研究區(qū)。 提供一鍵清空、復制功能、支持雙語對照查看，使您體驗更加流暢Continuous Poisson Model All the models described above are based on data observed at discrete locations that are considered to be nonrandom, as defined by a regular or irregular lattice of location points. That is, the locations of the observations are considered to be fixed, and we evaluate the spatial randomness of the observation conditioning on the lattice. Hence, those are all versions of what are called discrete scan statistics. In a continuous scan statistics, observations may be located anywhere within a study area, such as a square or rectangle. The stochastic aspect of the data consists of these random spatial locations, and we are interested to see if there are any clusters that are unlikely to occur if the observations where independently and randomly distributed across the study area. Under the null hypothesis, the observations follow a homogeneous spatial Poisson process with constant intensity throughout the study area, with no observations falling outside the study area. Example: The data may consist of the location of bird nests in a square kilometer area of a forest. The interest may be to see whether the bird nests are randomly distributed spatially, or in other words, whether there are clusters of bird nests or whe