R语言 spaa包 spaa-package()函数中文帮助文档(中英文对照)

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spaa-package(spaa)
spaa-package()所属R语言包：spaa

                                        SPecies Association Analysis
                                         种间联结分析

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Miscellaneous functions for analysis of species association and niche overlap.
其它功能分析的种间联结和生态位重叠。


Details

详细信息----------Details----------

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（作者）----------Author(s)----------



Author: Jinlong Zhang <a href="mailto:jinlongzhang01@gmail.com">jinlongzhang01@gmail.com</a>


Qiong Ding <a href="mailto:dingqiong@ibcas.ac.cn">dingqiong@ibcas.ac.cn</a>


Jihong Huang <a href="mailto:xjhjh@ibcas.ac.cn">xjhjh@ibcas.ac.cn</a>


Maintainer: Jinlong Zhang <a href="mailto:jinlongzhang01@gmail.com">jinlongzhang01@gmail.com</a>




实例----------Examples----------


data(testdata)
testdata
data(splist)
splist

## adding information[＃添加信息]
## add genera from dataframe B to dataframe A.[＃属从数据框乙到数据框A.]
add.col(inputA = testdata, inputB = splist, add = "genera",
according = "species")
## add family from dataframe B to dataframe A.[＃添加家庭从数据框乙A.数据框]
add.col(inputA = testdata, inputB = splist, add = "family",
according = "species")

### data tranformation[＃＃数据魏宏]
(spmatrix <- data2mat(testdata))
#Species association[物种协会]
sp.assoc(spmatrix)

# Species association between each pair of species[物种之间的关联每个物种对]
(result <- sp.pair(spmatrix))

# simple network with positive lines in red and negative lines [简单的网络与正红色线和负极线]
# in blue[在蓝]
plotnetwork(result$Pearson)
title("Pearson Correlation Network")

# The lower matrix plot illustrating Pearson's correlation [较低的矩阵图说明Pearson相关]
# between each pair of species Note the triangle didn't appeared[每个物种对之间，请注意三角形没有出现]
# in the plots, but have been added to the legend. This is due [在图，但已加入的传说。这是由于]
# to the distribution of data. Be carefull in seletion of intervals.[到的数据的分布。在选订的时间间隔要小心。]

plotlowertri(result$Pearson, int = 0.5, cex=3, interval = 4,
pchlist = c(19, 17, 15, 1, 5), size = TRUE)
title("Pearson Correlation Lower Matrix Plot")

## plot lower matrix[＃图矩阵]
## Using BCI data for lower matrix plot[＃使用BCI数据较低的矩阵图]
library(vegan)
data(BCI)
## select the top 30 species according to relative frequeny.[＃选择最上面的30种根据相对frequeny。]
sub <- sub.sp.matrix(BCI, common = 30)
## Set the digits to 1[＃设置为1位数]
plotlowertri(cor(sub), size = TRUE, cex = 3, digits = 1)

#### Niche width and niche overlap[＃＃＃生态位宽度和生态位重叠]
data(datasample)
niche.overlap.boot(datasample[,1:3], method = "levins")
niche.overlap(datasample, method = "levins")
niche.width(datasample[,1:3], method = "shannon")

##example turnover()[例如营业额（）]
plotlab1 <- XYname(4,6)
xxx <- 1:240
dim(xxx) <- c(24, 10)
rownames(xxx) <- plotlab1
### Distance between each pair of plots[＃＃图每对之间的距离]
ddd <- dist(xxx)
### label matrix[＃＃标签矩阵]
labmat1 <- lab.mat(plotlab1)
yyy <- turnover(labmat1, ddd)


## geodist() example[：＃geodist（）的例子]
## Paris[＃巴黎]
L1 = deg2dec(-2,20,14)
phi1 = deg2dec(48, 50, 11)
## Washington DC[＃华盛顿DC]
L2 = deg2dec(77,03,56)
phi2 = deg2dec(38,55,17)
##High precision Great Circle distance[＃高精密大圆距离]
geodist(L1, phi1, L2, phi2)


转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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