stoatDNA(secr)
stoatDNA()所属R语言包：secr
Stoat DNA Data
白鼬DNA数据库
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Data of A. E. Byrom from a study of stoats (Mustela erminea) in New Zealand. Individuals were identified from DNA in hair samples.
从鼬（鼬erminea）在新西兰的一项研究的AE拜罗姆的数据。个人头发样本中的DNA。
用法----------Usage----------
data(stoatDNA)
Details
详细信息----------Details----------
The data are from a pilot study of stoats in red beech (Nothofagus fusca) forest in the Matakitaki Valley, South Island, New Zealand. Sticky hair-sampling tubes (n = 94) were placed on a 3-km x 3-km grid with 500-m spacing between lines and 250-m spacing along lines. Tubes were baited with rabbit meat and checked daily for 7 days, starting on 15 December 2001. Stoat hair samples were identified to individual using DNA microsatellites amplified by PCR from follicular tissue (Gleeson et al. 2010). Six loci were amplified and the mean number of alleles was 7.3 per locus. Not all loci could be amplified in 27% of samples. A total of 40 hair samples were collected (Gleeson et al. 2010), but only 30 appear in this dataset; the rest presumably did not yield sufficient DNA for genotyping.
这些数据来自在Matakitaki谷，新西兰，南岛假山毛榉脸海番鸭，红榉林鼬的试验性研究。粘毛采样管组（n = 94）被放置在3公里×3公里网格线和250米的间距沿线500米之间的间距。管诱饵与兔肉，7天，每天检查2001年12月15日开始。的白鼬头发样本被确定为个人使用DNA微卫星的PCR扩增，从的卵泡组织（格里森等2010）。 6个位点扩增，平均每个位点的等位基因数为7.3。并非所有的位点可能会被放大，在27％的样本。 ，共收集40个头发样本（格里森等人，2010），但只有30个出现在这个数据集，其余的大概没有产生足够的DNA基因分型。
The data are provided as a single-session capthist object "stoatCH". Hair tubes are treated as "proximity" detectors which allow an individual to be detected at multiple detectors on one occasion (day), although there are no multiple detections in this dataset. Three pre-fitted models are included: stoat.model.HN, stoat.model.HZ, and stoat.model.EX (with halfnormal, hazard-rate and negative exponential detection functions, respectively).
中所提供的数据作为一个单一的会话capthist对象的stoatCH。头发的管子被视为“接近性”的探测器，允许个人被检测到多个探测器上一次（天），虽然没有在该数据集的多个检测。三拟合模型包括：stoat.model.HN，stoat.model.HZ和stoat.model.EX（与halfnormal，危险率和负指数检测功能，分别）。
注意----------Note----------
The log-likelihood values reported for these data by secr.fit differ by a constant from those published by Efford et al. (2009) because the earlier version of DENSITY used in that analysis did not include the multinomial coefficient, which in this case is log(20!) or about +42.336. The previous analysis also used a coarser habitat mask than the default in secr (32 x 32 rather than 64 x 64) and this slightly alters the log-likelihood and deltaAIC values.
对数似然值，这些数据secr.fit报告不同的常数由Efford等人发表的。 （2009年），因为早期版本的密度，分析不包括多项式的系数，在这种情况下，log（20）或约42.336。前面的分析也采用了粗糙的栖息地比默认情况下，在面具secr（32×32，而不是64×64），这稍微改变了对数似然和deltaAIC值。
Fitting the hazard-rate detection function previously required the shape parameter z (or b) to be fixed, but the model can be fitted in secr without fixing z. However, the hazard rate function can cause problems owing to its long tail, and it is not recommended. The check on the buffer width, usually applied automatically on completion of secr.fit, causes an error and must be suppressed with verify = FALSE (see Examples).
装修的危险率检测功能以前需要的形状参数z（或B）是固定的，但该模型可安装在secr没有固定Ž。然而，危险率函数可能会导致问题，由于其长长的尾巴，所以不推荐。检查的缓冲区的宽度，通常用于自动上完成的secr.fit，会导致错误，必须抑制与验证= FALSE（见例）。
Gleeson et al. (2010) address the question of whether there is enough variability at the sampled microsatellite loci to distinguish individuals. The reference to 98 sampling sites in that paper is a minor error (A. E. Byrom pers. comm.).
格里森等。 （2010）解决的问题是是否有足够的采样的微卫星位点的变异性来区分个人。在该论文中的98个采样点的参考是一个小错误（AE拜罗姆私人通信）。
源----------Source----------
Efford, M. G., Borchers D. L. and Byrom, A. E. (2009) Density estimation by spatially explicit capture-recapture: likelihood-based methods. In: D. L. Thomson, E. G. Cooch and M. J. Conroy (eds) Modeling Demographic Processes in Marked Populations. Springer, New York. Pp. 255–269.
Efford，MG，BORCHERS DL和拜罗姆，AE（2009年）密度估计的空间明确的捕获 - 再捕获的可能性为基础的方法。 ：DL汤姆逊，EG库奇和MJ康罗伊（EDS）模型的显着人口的人口进程。施普林格，纽约。 PP。 255-269。
参考文献----------References----------
methods for genotyping of stoats (Mustela erminea) in New Zealand: potential for field applications. New Zealand Journal of Ecology 34, 356–359. Available on-line at http://www.newzealandecology.org.
参见----------See Also----------
capthist, detection functions, secr.fit
capthist，detection functions，secr.fit
实例----------Examples----------
summary(stoatCH)
## Not run: [＃不运行：]
stoat.model.HN <- secr.fit(stoatCH, buffer = 1000, detectfn = 0)
stoat.model.HZ <- secr.fit(stoatCH, buffer = 1000, detectfn = 1,
verify = FALSE)
stoat.model.EX <- secr.fit(stoatCH, buffer = 1000, detectfn = 2)
confint(stoat.model.HN, "D")
## Profile likelihood interval(s)...[＃资料的可能性间隔（S）...]
## lcl ucl[＃LCL UCL]
## D 0.01275125 0.04055662[＃D 0.01275125 0.04055662]
## End(Not run)[＃（不执行）]
## plot fitted detection functions[＃图合身的检测功能]
xv <- seq(0,800,10)
plot(stoat.model.EX, xval = xv, ylim = c(0,0.12), limits = FALSE,
lty = 2)
plot(stoat.model.HN, xval = xv, limits = FALSE, lty = 1, add = TRUE)
plot(stoat.model.HZ, xval = xv, limits = FALSE, lty = 3, add = TRUE)
## review density estimates[＃检查密度估计。]
collate(stoat.model.HZ, stoat.model.HN, stoat.model.EX,
realnames = "D", perm = c(2,3,4,1))
model.average(stoat.model.HN, stoat.model.EX,
realnames = "D")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-30 00:05:46
