ovensong(secr)
ovensong()所属R语言包：secr
Ovenbird Acoustic Dataset
Ovenbird声集
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Data from an acoustic survey of ovenbirds (Seiurus aurocapilla) at a site in Maryland, USA.
从一个声调查的ovenbirds（Seiurus aurocapilla），在美国马里兰州的网站的数据。
用法----------Usage----------
data(ovensong)
Details
详细信息----------Details----------
In June 2007 D. K. Dawson and M. G. Efford used a moving 4-microphone array to survey breeding birds in deciduous forest at the Patuxent Research Refuge near Laurel, Maryland, USA. The data for ovenbirds were used to demonstrate a new method for analysing acoustic data (Dawson and Efford 2009). See ovenbird for mist-netting data from the same site over 2005–2009, and for other background.
2007年6月DK道森和，MG Efford使用移动四麦克风阵列繁殖鸟在落叶林进行调查，在附近的马里兰州Laurel，美国的的帕塔克森特研究庇护。使用的数据为ovenbirds展示出一种新的用于分析声学数据（道森和Efford 2009）的方法。见ovenbird雾网超过2005-2009年的数据来自同一个站点，和其他背景。
Over five days, four microphones were placed in a square (21-m side) centred at each of 75 points in a rectangular grid (spacing 50 m); on each day points 100 m apart were sampled sequentially. Recordings of 5 minutes duration were made in .wav format on a 4-channel digital sound recorder.
五天，四个麦克风被放置在一个正方形（21米侧）为中心，各75分在矩形网格（间距50米）的每一天点上相距百米顺序采样。一个4通道数字录音机进行了5分钟的时间。wav格式的录音。
The data are estimates of average power on each channel (microphone) for the first song of each ovenbird distinguishable in a particular 5-minute recording. Power was estimated with the sound analysis software Raven Pro 1.4 (Charif et al. 2008), using a window of 0.7 s duration and frequencies between 4200 and 5200 Hz, placed manually at the approximate centre of each ovenbird song. Sometimes this frequency range was obscured by insect noise so an alternative 1000-Hz range was measured and the values were adjusted by regression.
的数据为估计值，平均在每个通道的电源输入（麦克风）的第一首歌曲在一个特定的5分钟记录每个ovenbird区分的。功率估计的声音分析软件乌鸦专业版1.4（谢里夫等人，2008），用0.7秒的持续时间和频率在4200和5200赫兹之间，手动的近似中心每个ovenbird歌的窗口。有时候，这个频率范围内昆虫噪音所掩盖，因此替代1000 Hz范围内的测量值进行了调整，由回归。
The data are provided as a single-session, single-occasion capthist object signalCH. The "signal" attribute contains the power measurement in decibels for each detected sound on each channel where the power threshold is exceeded. As the threshold signal (attribute cutval = 35) is less than any signal value in this dataset, all detection histories are complete (1,1,1,1) across microphones. For analysis Dawson and Efford applied a higher threshold that treated weaker signals as "not detected" (see Examples).
的数据提供一个单一的会议上，单场合capthist对象signalCH。 “信号”属性包含每个检测到的每个通道上的功率超过阈值时的声音分贝的功率测量。作为阈值信号（属性cutval= 35）小于在这个数据集的任何信号值，所有的检测历史是完整的（1,1,1,1）跨麦克风。有关人士分析道森和Efford的采用了较高的阈值，为“不检测”（见例）治疗较弱的信号。
The row names of signalCH (e.g. "3755AX") are formed from a 4-digit number indicating the sampling location (one of 75 points on a 50-m grid) and a letter A–D to distinguish individual ovenbirds within a 5-minute recording; "X" indicates power values adjusted by regression.
行signalCH（例如，“3755AX”）的名称由一个4位数字表示的取样位置（75点50米的网格之一）和一个字母A-D区分个人ovenbirds的的在5分钟的录音中，X表示调整回归的功率值。
The default model for sound attenuation is a log-linear decline with distance from the source (linear decline on dB scale). Including a spherical spreading term in the sound attenuation model causes the likelihood surface to become multimodal in this case. Newton-Raphson, the default maximization method in secr.fit, is particularly inclined to settle on a local maximum; in the example below we use a set of starting values that have been found by trial and error to yield the global maximum.
默认的声音衰减模型是对数线性下降，从源（直线下降对dB的规模）的距离。中的声音衰减模型包括一个球面扩散术语导致的可能性表面成为在这种情况下，多峰。牛顿 - 拉夫逊，默认情况下最大化的方法secr.fit，特别倾向于解决在当地最大的例子，下面我们用一组已发现的试验和错误产生全球最大的初始值。
Two fitted models are included (see Examples for details).
两个拟合模型（见细节的例子）。
源----------Source----------
D. K. Dawson (ddawson@usgs.gov) and M. G. Efford unpublished data.
DK森（ddawson@usgs.gov）的和MG Efford的未发表的数据。
参考文献----------References----------
Manual. Cornell Laboratory of Ornithology, Ithaca, New York.
acoustic signals. Journal of Applied Ecology 46, 1201–1209.
from locations of individuals on a passive detector array. Ecology 90, 2676–2682.
参见----------See Also----------
capthist, ovenbird, detection functions
capthist，ovenbird，detection functions
实例----------Examples----------
summary(signalCH)
traps(signalCH)
signal(signalCH)
## apply signal threshold[＃适用于信号阈值]
signalCH.525 <- subset(signalCH, cutval = 52.5)
## Not run: [＃不运行：]
## models with and without spherical spreading[＃和球面扩散模型]
omask <- make.mask(traps(signalCH), buffer = 200)
ostart <- c(log(20), 80, log(0.1), log(2))
ovensong.model.1 <- secr.fit( signalCH.525, mask = omask,
start = ostart, detectfn = 11 )
ovensong.model.2 <- secr.fit( signalCH.525, mask = omask,
start = ostart, detectfn = 10 )
## End(Not run)[＃（不执行）]
## compare fit of models[比较适合的车型]
AIC(ovensong.model.1, ovensong.model.2)
## density estimates, dividing by 75 to allow for replication[密度估计，除以75，以便复制]
collate(ovensong.model.1, ovensong.model.2)[1,,,"D"]/75
## plot attenuation curves cf Dawson & Efford (2009) Fig 5[＃图衰减曲线比照道森和Efford的（2009年）图5]
pars1 <- predict(ovensong.model.1)[c("beta0", "beta1"), "estimate"]
pars2 <- predict(ovensong.model.2)[c("beta0", "beta1"), "estimate"]
attenuationplot(pars1, xval=0:150, spherical = TRUE, ylim = c(40,110))
attenuationplot(pars2, xval=0:150, spherical = FALSE, add = TRUE,
col = "red")
## spherical spreading only[＃球面扩散]
pars1[2] <- 0
attenuationplot(pars1, xval=0:150, spherical = TRUE, add = TRUE, lty=2)
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 00:00:29
