我是循环的新手，我正在努力格式化我的输出。我试图在不同时间点（i）修改物种丰度的时间序列，对于多个物种（k）以各种大小（j）。对于这些选项中的每一个，我想要一个随时间显示丰富度的列。
我编写了一个循环，当我手动输入i，j和k的各种值时（即我得到具有正确值的单列），但是我无法弄清楚如何正确地索引输出矩阵。
虚拟数据集看起来像这样（其中x和y是不同的物种，样本数是时间）：
dat<- data.frame(x = sample(1:100, 100, replace = TRUE), y = sample(1:100, 100, replace = TRUE))
对于我的循环，我还创建了一些其他对象：
length <- nrow(dat)
change <- as.matrix(seq(0.0,0.99,0.2))
change.length <- nrow(change)
和填充的最终矩阵（具有正确的尺寸）。 -40是因为我没有修改时间序列中的前20个或后20个丰度
final_matrix <- array(0,c(length,(((length-40)*change.length)*2))) # 2 is the number of species in this example
循环如下：
for(i in 1:(length-40)) {
for(j in 1:change.length){
for(k in 1:2){
timestep1 <- dat[0:(19+(i)),k] # selecting rows that will not be modified based on min + i for a given species k
timestep2 <- dat[(20+i):(length),k] # selecting rows that will be modified for any given species k (columns)
result1 <- timestep1*1 # not making any changes to the abundance data
result2 <- timestep2*change[j,1] # multiplying abundance by change (j)
resultloop<-c(result1,result2) # binding the two matrices into a single column with 100 rows
final_matrix[,i*j*k] <- resultloop
}}}
按原样索引的最终矩阵产生的列数不正确，结果很奇怪，例如全零的列。
如何索引此矩阵，以便为i，j和k的每个值索引每列（100行代表随时间的丰度）？
编辑：一个示例（模拟）虚拟数据集，用于说明我希望输出看起来像什么：
dat<- data.frame(Spx_I1_j1 = sample(1:100, 100, replace = TRUE),
Spx_I2_j1 = sample(1:100, 100, replace = TRUE),
Spx_I3_j1 = sample(1:100, 100, replace = TRUE),
# etc...
Spx_I1_k2 = sample(1:100, 100, replace = TRUE),
Spx_I2_k2 = sample(1:100, 100, replace = TRUE),
#etc...
Spy_I1_k1 = sample(1:100, 100, replace = TRUE),
Spy_I2_k1 = sample(1:100, 100, replace = TRUE))
对于每个物种（k）的每个幅度变化（j），每个修改的时间步长序列（i）的各列表示100个丰度值的每列。
非常感谢您提供有关此事的任何帮助或建议。
答案 0 :(得分：0)
我不太确定你在问什么。这是我的解释。 顺便提一下，请查看How to make a great R reproducible example?以便将来重新提出您的问题。
#Make some sample data, 10 observations, ignore the first 2 and last 2
ss<-function(x,n){set.seed(x); sample(1:100,n,replace=TRUE)}
dat<-as.matrix(data.frame("Dog"=ss(1,10),"Cat"=ss(2,10))) #Note, make this a matrix!
ind_ignore<-c(1:2,9:10) #these will be ignored, like your first and last x abundances
change <- seq(0.0,0.99,0.2) #5 values, so the expected output is expected to have a dimension of 10 obs by (2 species * 5 changes)
result <- matrix(NA,ncol=ncol(dat)*length(change), nrow=nrow(dat))
colnames(result)<-paste0("V",1:ncol(result))
n_counter<-ncol(dat)-1
counter<-1
> change
[1] 0.0 0.2 0.4 0.6 0.8
> dat
Dog Cat
[1,] 27 19
[2,] 38 71
[3,] 58 58
[4,] 91 17
[5,] 21 95
[6,] 90 95
[7,] 95 13
[8,] 67 84
[9,] 63 47
[10,] 7 55
然后，我只是将dat值分配给结果矩阵，并更改组合名称。
for(magnitude in change){
#Indexes of columns in result that should be modified
ind_col<-counter:(counter+n_counter)
#Change names
colnames(result)[ind_col]<-paste0(colnames(dat),"_",magnitude)
#Apply the change and assign it to the result
result[-ind_ignore,ind_col]<-dat[-ind_ignore,]*magnitude
result[ind_ignore,ind_col]<-dat[ind_ignore,]
counter<-counter+ncol(dat)
}
输出
> result
Dog_0 Cat_0 Dog_0.2 Cat_0.2 Dog_0.4 Cat_0.4 Dog_0.6 Cat_0.6 Dog_0.8 Cat_0.8
[1,] 27 19 27.0 19.0 27.0 19.0 27.0 19.0 27.0 19.0
[2,] 38 71 38.0 71.0 38.0 71.0 38.0 71.0 38.0 71.0
[3,] 0 0 11.6 11.6 23.2 23.2 34.8 34.8 46.4 46.4
[4,] 0 0 18.2 3.4 36.4 6.8 54.6 10.2 72.8 13.6
[5,] 0 0 4.2 19.0 8.4 38.0 12.6 57.0 16.8 76.0
[6,] 0 0 18.0 19.0 36.0 38.0 54.0 57.0 72.0 76.0
[7,] 0 0 19.0 2.6 38.0 5.2 57.0 7.8 76.0 10.4
[8,] 0 0 13.4 16.8 26.8 33.6 40.2 50.4 53.6 67.2
[9,] 63 47 63.0 47.0 63.0 47.0 63.0 47.0 63.0 47.0
[10,] 7 55 7.0 55.0 7.0 55.0 7.0 55.0 7.0 55.0
我再也不知道我是否理解了这些评论，这里什么都没有：
ss<-function(x,n){set.seed(x); sample(1:100,n,replace=TRUE)}
dat<-as.matrix(data.frame("Dog"=ss(1,10),"Cat"=ss(2,10))) #Note, make this a matrix!
ignore<-2 #these will be ignored, like your first and last x abundances
change <- seq(0.0,0.99,0.2) #5 values, so the expected output is expected to have a dimension of 10 obs by (2 species * 5 changes * (10-4 ignored points) timepoints)
n_timepoints<-nrow(dat) - ignore*2
result <- matrix(NA,ncol=ncol(dat)*length(change)*n_timepoints, nrow=nrow(dat))
colnames(result)<-paste0("V",1:ncol(result))
n_counter<-ncol(dat)*n_timepoints-1
counter<-1
for(magnitude in change){
#Indexes of columns in result that should be modified
ind_col<-counter:(counter+n_counter)
#Change names
colnames(result)[ind_col]<-paste0(colnames(dat),"_",magnitude,"_",rep(1:n_timepoints, each=ncol(dat)))
for (timepoint in 1:n_timepoints){
#Apply the change and assign it to the result
ind_col_timepoint <- counter:(counter+ncol(dat)-1)
ind_ignore_timepoint<-c(1:(ignore+timepoint-1), (nrow(dat)-ignore+1):nrow(dat))
result[-ind_ignore_timepoint,ind_col_timepoint]<-dat[-ind_ignore_timepoint,]*magnitude
result[ind_ignore_timepoint,ind_col_timepoint]<-dat[ind_ignore_timepoint,]
counter<-counter+ncol(dat)
}
}