将数据转换为INEXT分析的正确格式

Question

我计划使用iNEXT函数（来自iNEXT包）分析两种池塘类型之间的潜在差异。这些数据是存在/不存在的，并且按照物种基质在某个位置上。

Pond.type <- c("A", "A", "A", "B", "B", "B") 
Sample.no <- c(1,2,3,1,2,3) 
Species1 <- c(0,1,1,1,0,0) 
Species2 <- c(0,1,1,0,1,0) 
Species3 <- c(1,1,1,1,0,1) 
Species4 <- c(0,1,0,1,0,0) 
Species5 <- c(1,1,1,0,0,0) 

mydata <- cbind.data.frame(Pond.type, Sample.no, Species1, Species2, Species3, Species4, Species5) 

如果我将我的数据框划分成池类型并将它们保存为矩阵，我可以运行该函数，例如， iNEXT(pond.type.a.dataframe)，但后来我无法比较一个地块上不同的池塘类型。

我的问题是，有没有办法将我的数据转换为与iNEXT库中提供的纤毛虫示例数据相同的格式？这是作为矩阵列表给出的。

Answer 1

如果你有2个.csvs结构这样

池A型：

Species, Sample1, Sample2, Sample3 
Species1, 0,1,1 
Species2, 0,1,1 
Species3, 1,1,1 
Species4, 0,1,0 
Species5, 1,1,1 

池塘B型：

Species, Sample1, Sample2, Sample3 
Species1, 1,0,0 
Species2, 0,1,0 
Species3, 1,0,1 
Species4, 1,0,0 
Species5, 0,0,0 

然后假设你叫 “读CSV” S庞达和pondB你可以做到以下几点：

library(iNEXT) 
library(ggplot2) 

# make a matrix from pondA as type "integer" 
mPondA <- as.matrix(apply(pondA[,-1],2,as.integer)) 

# use your species names as row names 
row.names(mPondA) <- pondA[,1] 

# do the same for pondB 
mPondB <- as.matrix(apply(pondB[,-1],2,as.integer)) 
row.names(mPondB) <- pondB[,1] 

# create a list of your matrices (named so the output looks nice) 
pondABM = list(A = mPondA, B = mPondB) 

# have a look at the raw data 
out.raw <- iNEXT(pondABM, datatype="incidence_raw", endpoint=20) 
ggiNEXT(out.raw) 

，这将给你一个像这样的输出：

如果你想要做更多使用这种输入型的数据，不要忘了更改数据类型：

# note that datatype has been changed to "incidence_raw" instead of "abundance" 
iNEXT(pondABM, q=0, datatype="incidence_raw", size=NULL, se=TRUE, conf=0.95, nboot=50) 

Answer 2

我来到了一个解决方案，可能比它需要的更啰嗦，但它的工作原理。

# I've altered the dataset as I usually type these data in long format 
Pond.type <- c(rep("A", 15), rep("B", 15)) 
Site.no <- rep(seq(1,6, by=1), 5) 
Species <- c(rep("Spp1", 6), rep("Spp2", 6), rep("Spp3", 6), rep("Spp4", 6), rep("Spp5", 6)) 
Presence <- rep(1, 30) 

# join the vectors together into a dataframe 
mydata.long <- cbind.data.frame(Pond.type, Site.no, Species, Presence) 

# I then cast the data into a species x site matrix (dcast is from the reshape2 library) 
mydata.cast <- dcast(mydata.long, Species + Pond.type ~ Site.no) 

# Changes the NAs to zeros. 
mydata.cast[is.na(mydata.cast)] <- 0 

# Separate the dataframe into each pond tyoe 
pondA <- mydata.cast[grep("A", mydata.cast$Pond.type),] 
pondB <- mydata.cast[grep("B", mydata.cast$Pond.type),] 

# Calculate the frequency counts using the function from the iNEXT library 
pondA.freq <- as.incfreq(pondA[,3:ncol(pondA)]) 
pondB.freq <- as.incfreq(pondB[,3:ncol(pondB)]) 

# join them as a list 
pond.list = list(A = pondA.freq, B = pondB.freq) 

# ready for comparison 
pond.out <- iNEXT(pond.list, datatype = "incidence_freq", q=0) 
pond.out 

Answer 3

我试图从奥利弗伯德其中大工作的答案 - 感谢

任一方式作出是否使用大量的数据 - 应用as.abucount函数矩阵列表，运行iNEXT前功能

# create a list of your matrices (named so the output looks nice) 
pondABM = list(A = mPondA, B = mPondB) 

# apply as.abucount to the list of matrices 
pondABM2 = lapply(pondABM, as.abucount) 

# run the iNEXT function 
iNEXT(pondABM2, datatype="abundance")