hspider(VGAM)
hspider()所属R语言包：VGAM
Hunting Spider Data
狩猎蜘蛛数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Abundance of hunting spiders in a Dutch dune area.
丰度的狩猎蜘蛛在荷兰的沙丘。
用法----------Usage----------
data(hspider)
格式----------Format----------
A data frame with 28 observations (sites) on the following 18 variables.
有28个观测值（点）在以下18个变量的数据框。
WaterCon Log percentage of soil dry mass.
WaterCon登录土壤干质量的百分比。
BareSand Log percentage cover of bare sand.
裸沙BareSandlog盖的百分比。
FallTwig Log percentage cover of fallen leaves and twigs.
FallTwiglog的百分比盖的落叶和树枝。
CoveMoss Log percentage cover of the moss layer.
CoveMoss登录百分比的苔藓层盖。
CoveHerb Log percentage cover of the herb layer.
CoveHerblog草本层盖率。
ReflLux Reflection of the soil surface with cloudless sky.
ReflLux反射的土壤表面，万里无云的天空。
Alopacce Abundance of Alopecosa accentuata.
Alopacce丰度Alopecosa accentuata。
Alopcune Abundance of Alopecosa cuneata.
Alopcune Alopecosa藤的丰度。
Alopfabr Abundance of Alopecosa fabrilis.
Alopfabr丰度Alopecosa fabrilis的。
Arctlute Abundance of Arctosa lutetiana.
Arctlute三齿熊蛛lutetiana的丰度。
Arctperi Abundance of Arctosa perita.
三齿熊蛛perita Arctperi的丰富性。
Auloalbi Abundance of Aulonia albimana.
Auloalbi丰度Aulonia albimana的。
Pardlugu Abundance of Pardosa lugubris.
Pardlugu丰富的豹lugubris。
Pardmont Abundance of Pardosa monticola.
Pardmont丰度的环纹豹蛛白松。
Pardnigr Abundance of Pardosa nigriceps.
Pardnigr丰富的豹nigriceps。
Pardpull Abundance of Pardosa pullata.
Pardpull豹pullata的丰度。
Trocterr Abundance of Trochosa terricola.
Trochosa栖土Trocterr丰度。
Zoraspin Abundance of Zora spinimana.
Zoraspin丰富的佐拉spinimana。
Details
详细信息----------Details----------
The data, which originally came from Van der Aart and Smeek-Enserink (1975) consists of abundances (numbers trapped over a 60 week period) and 6 environmental variables. There were 28 sites.
原本从范德阿尔特和Smeek Enserink的的（1975年）的数据，包括丰（数字被困在60周的时间）和6个环境变量。有28个。
This data set has been often used to illustrate ordination, e.g., using canonical correspondence analysis (CCA). In the example below, the data is used for constrained quadratic ordination (CQO; formerly called canonical Gaussian ordination or CGO), a numerically intensive method that has many superior qualities. See cqo for details.
该数据集常常被用来说明协调，例如，采用典范对应分析（CCA）。在下面的例子中，数据将被用于约束的二次排序（CQO以前称为规范的高斯协调或CGO），计算密集的方法，有很多出众的。见cqo的详细信息。
参考文献----------References----------
Correlations between distributions of hunting spiders (Lycosidae, Ctenidae) and environmental characteristics in a dune area. Netherlands Journal of Zoology, 25, 1–45.
实例----------Examples----------
str(hspider)
## Not run: [＃不运行：]
# Fit a rank-1 Poisson CQO[适合一个等级1的泊松CQO]
set.seed(111) # This leads to the global solution[这将导致全球性的解决方案]
hspider[,1:6]=scale(hspider[,1:6]) # Standardize the environmental variables[标准化的环境变量]
# vvv p1 = cqo(cbind(Alopacce, Alopcune, Alopfabr, Arctlute, Arctperi, Auloalbi,[VVV P1 = cqo（CBIND（Alopacce，Alopcune，Alopfabr，Arctlute，Arctperi，Auloalbi]
# vvv Pardlugu, Pardmont, Pardnigr, Pardpull, Trocterr, Zoraspin) ~[VVV Pardlugu，Pardmont，Pardnigr，Pardpull，Trocterr，Zoraspin）~]
# vvv WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux,[VVV WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux，]
# vvv fam = poissonff, data = hspider, Crow1posit=FALSE)[VVV FAM = poissonff，数据hspider，Crow1posit = FALSE）]
# vvv nos = ncol(p1@y)[VVV NOS NCOL（P1 @ Y）]
# vvv lvplot(p1, y=TRUE, lcol=1:nos, pch=1:nos, pcol=1:nos) [：VVV lvplot（P1，Y = TRUE，LCOL = 1：一氧化氮合酶，PCH = 1：NOS，PCOL = 1：NOS）]
# vvv Coef(p1)[VVV系数（P1）]
# vvv summary(p1)[VVV摘要（P1）]
# Fit a rank-1 binomial CAO[装一个排名1二项式曹]
hsbin = hspider # Binary species data[的二进制种数据]
hsbin[,-(1:6)] = as.numeric(hsbin[,-(1:6)] > 0)
set.seed(123)
ahsb1 = cao(cbind(Alopcune,Arctlute,Auloalbi,Zoraspin) ~
WaterCon + ReflLux, family = binomialff(mv=TRUE),
df1.nl = 2.2, Bestof=3, data = hsbin)
par(mfrow=2:1, las=1)
lvplot(ahsb1, type="predictors", llwd=2, ylab="logit p", lcol=1:9)
persp(ahsb1, rug=TRUE, col=1:10, lwd=2)
coef(ahsb1)
## End(Not run)[＃（不执行）]
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 2012-10-1 15:38:06
