【文章內(nèi)容簡介】 reliability of the estimates differs, one should instead use the weighted normal scan statistic that takes these unequal variances into account. The weighted version is obtained in SaTScan by simply specifying a weight for each observation as an extra column in the input file. This weight may for example be proportional to the sample size used for each estimate or it may be the inverse of the variance of the observation. If all values are multiplied with or added to the same constant, the statistical inference will not change, meaning that the same clusters with the same log likelihoods and pvalues will be found. Only the estimated means and variances will differ. If the weight is the same for all observations, then the weighted normal scan statistic will produce the same results as the standard normal version. If all the weights are multiplied by the same constant, the results will not change. 正常模式正常模式是用于連續(xù)數(shù)據(jù)。每個(gè)人或每個(gè)觀察，稱為例，有一個(gè)單一的連續(xù)屬性可以是正的或負(fù)的。該模型還可以用來序的數(shù)據(jù)時(shí)，有許多類別。這是不同的情況下，允許具有相同的屬性值。例如：為正常模式，數(shù)據(jù)可能包括出生體重及住宅普查道的所有新生兒，有興趣的調(diào)查組的低出生體重。一個(gè)人是一個(gè)39。情況39。另外，數(shù)據(jù)可能包括平均出生體重在每個(gè)普查道。它是那么的普查道，“事件”，而且重要的是使用加權(quán)正常模式，因?yàn)槊總€(gè)平均會(huì)有不同的差異，由于不同數(shù)量的出生在每個(gè)道。值得注意的是，雖然正常模型使用似然函數(shù)的正態(tài)分布，真實(shí)分布的連續(xù)屬性必須是不正常的。統(tǒng)計(jì)推斷（P值）是有效的任何連續(xù)分布。這是因?yàn)?，不是隨機(jī)產(chǎn)生所做的模擬數(shù)據(jù)的正態(tài)分布，但相反，排樣的時(shí)空位置和連續(xù)屬性（例如出生體重）的意見。同時(shí)還被正式有效，結(jié)果可以大大影響極端離群，所以它可能是明智的截?cái)?，這樣的意見之前做分析。在標(biāo)準(zhǔn)模型中，假定每個(gè)觀察是相同的測量方差。這可能并非總是如此。例如，如果一個(gè)觀察的基礎(chǔ)上更大的樣本在一個(gè)位置和一個(gè)較小的樣本在另一個(gè)，然后方差的不確定性的估計(jì)將是更大的小樣本。如果可靠性估計(jì)不同，應(yīng)使用加權(quán)正常的掃描統(tǒng)計(jì)，考慮到這些不平等的差異。加權(quán)版本獲得SaTS can通過簡單地指定一個(gè)體重為每個(gè)觀察作為一個(gè)額外的列中輸入文件。這個(gè)重量，例如可能是成正比的樣本大小用于每一個(gè)估計(jì)，也可以是逆差額觀察。如果所有的值乘以或添加到同一常數(shù)，統(tǒng)計(jì)推斷是不會(huì)改變的，即同一簇具有相同的日志可能性和P 值會(huì)被發(fā)現(xiàn)。只有估計(jì)均值和方差將不同。如果重量是相同的所有意見，然后加權(quán)正常掃描統(tǒng)計(jì)會(huì)產(chǎn)生相同的結(jié)果的標(biāo)準(zhǔn)版本。如果所有的重量乘以同一常數(shù)，結(jié)果不會(huì)改變。分享到 翻譯結(jié)果重試抱歉，系統(tǒng)響應(yīng)超時(shí)，請稍后再試 支持中英、中日在線互譯 支持網(wǎng)頁翻譯，在輸入框輸入網(wǎng)頁地址即可 提供一鍵清空、復(fù)制功能、支持雙語對照查看，使您體驗(yàn)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 whether they are located independently of each other. In SaTScan, the study area can be any collection of convex polygons, which are convex regions bounded by any number straight lines. Triangles, squares, rectangles, rhombuses, pentagons and hexagons are all examples of convex polygons. In the simplest case, there is only one convex polygon, but the study area can also be the union of multiple convex polygons. If the study area is not convex, divide it into multiple convex polygons and define each one separately. The study area does not need to be contiguous, and may for example consist of five different islands. The analysis is conditioned on the total number of observations in the data set. Hence, the scan statistic simply evaluates the spatial distribution of the observation, but not the number of observations. The likelihood function used as the test statistic is the same as for the Poisson model for the discrete scan statistic, where the expected number of cases is equal to the total number of observed observations, times the size of the scanning window, divided by the size of the total study area. As such, it is a special case of the variable window size scan statistic described by Kulldorff (1997). When the scanning window extends outside the study area, the expected count is still based on the full size of the circle, ignoring the fact that some parts of the circle have zero expected counts. This is to avoid strange noncircular clusters at the border of the study area. Since the analysis is based on Monte Carlo randomizations, the pvalues are automatically adjusted for these boundary effects. The reported expected counts are based on the full circle though, so the Obs/Exp ratios provided should be viewed as a lower bound on the true value whenever the circle extends outside the spatial study region. The continuous Poisson model can only be used for purely spatial data. It uses a circular scanning window of continuously varying radius up to a maximum specified by the user. Only circles centered on one of the observations are considered, as specified in the coordinates file. If the optional grid file is provided, the circles are instead centered on the coordinates specified in that file. The continuous Poisson model has not been implemented to be used with an elliptic window. 連續(xù)泊松模型所有的模型描述是基于上述數(shù)據(jù)觀察離散地點(diǎn)被認(rèn)為是隨機(jī)的，所確定的規(guī)則或不規(guī)則格點(diǎn)的位置。就是說，該地點(diǎn)的意見被認(rèn)為是固定的，和我們評估的空間隨機(jī)性的觀察空調(diào)格。因此，這些都是什么版本被稱為離散掃描統(tǒng)計(jì)。在連續(xù)掃描統(tǒng)計(jì)，觀察可能位于內(nèi)的任何一個(gè)研究領(lǐng)域，如正方形或矩形。隨機(jī)方面的數(shù)據(jù)由隨機(jī)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(dú)立隨機(jī)分布在研究區(qū)。零假設(shè)下，觀察遵循同質(zhì)空間泊松過程的光強(qiáng)恒定在整個(gè)研究區(qū)，沒有意見以外的研究領(lǐng)域。例如：數(shù)據(jù)可能包括位置的鳥巢一平方公里面積的森林。興趣可以看看鳥巢是隨機(jī)分布的空間，或在其他的話，是否有集群筑巢，或它們是否位于相互獨(dú)立。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。三角形，正方形，長方形，菱形，五邊形和六邊形的例子有凸多邊形。在最簡單的情況下，只能有一個(gè)凸多邊形，但研究區(qū)域也可以結(jié)合多個(gè)凸多邊形。如果該區(qū)不是凸，分為多個(gè)凸多邊形的每一個(gè)單獨(dú)的定義。研究領(lǐng)域不需要是連續(xù)的，例如可能由五個(gè)不同的島嶼。分析條件的總?cè)藬?shù)的觀測數(shù)據(jù)集。因此，掃描統(tǒng)計(jì)只是評估的空間分布的觀察，而不是數(shù)量的觀察。似然函數(shù)作為檢驗(yàn)統(tǒng)計(jì)量是一樣的泊松模型的離散掃描統(tǒng)計(jì)，在預(yù)期的案件數(shù)量等于總?cè)藬?shù)的觀察意見，倍大小的掃描窗口，除以總規(guī)模的研究領(lǐng)域。因此，它是一種特殊情況的變量窗口大小的掃描統(tǒng)計(jì)描述庫爾多夫（1997）。當(dāng)掃描窗口外的研究領(lǐng)域，預(yù)期值的基礎(chǔ)上仍然是全尺寸的圓，忽略了一個(gè)事實(shí)，一些