將數據轉換爲INEXT分析的正確格式

Question

我計劃使用iNEXT函數（來自iNEXT包）分析兩種池塘類型之間的潛在差異。這些數據是存在/不存在的，並且按照物種基質在某個位置上。

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) 

如果我將我的數據框劃分成池類型並將它們保存爲矩陣，我可以運行該函數，例如， iNEXT(pond.type.a.dataframe)，但後來我無法比較一個地塊上不同的池塘類型。

我的問題是，有沒有辦法將我的數據轉換爲與iNEXT庫中提供的纖毛蟲示例數據相同的格式？這是作爲矩陣列表給出的。

Answer 1

如果你有2個.csvs結構這樣

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 

然後假設你叫 「讀CSV」 S龐達和pondB你可以做到以下幾點：

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) 

，這將給你一個像這樣的輸出：

如果你想要做更多使用這種輸入型的數據，不要忘了更改數據類型：

# 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

我來到了一個解決方案，可能比它需要的更囉嗦，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

我試圖從奧利弗伯德其中大工作的答案 - 感謝

任一方式作出是否使用大量的數據 - 應用as.abucount函數矩陣列表，運行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")