尝试在R中创建并循环通过不平衡数据的矩阵 [英] Trying to create and loop through matrix of unbalanced data in R

问题描述

我试图进行分层贝叶斯分析，但是在R和WinBUGS代码上遇到了一些麻烦。我没有平衡的数据，正在努力与编码。我每天都用iButtons（温度记录设备）在横断面上收集温度数据，并试图生成一个模型，将其与遥感数据相关联。不幸的是，每个横断面有不同数量的iButtons，因此在横断面（j）中创建一个按钮（i）的3D矩阵，在第（t）天重复采样对我来说是一个问题。

最终，我的模型会是这样的：

$ 1
Temp [ijk]〜N（theta [ijk]，tau ）
theta [ijk] = b0 + b1 * x1 +。 。 。 + bn * xn

等级2
b0 = a00 + a01 * y1 + 。 。等级3（也许？） - 随机等级2拦截$ b $ a $ y
b1 = a10 + a11 * y1 ... $ / b>


$ b通常我会这样做：
Wide < - reshape（Data1，idvar = c（iButton，block），timevar =julian，direction =宽度）

  J < -  length（unique（Data $ block））
 I < -  length （数据$ iButton））
 Ti < -  length（unique（Data $ julian））
 
 Temp < -  array（NA，dim = c（I，Ti，J） ）
 
 for（t in 1：Ti）{
 sel.rows<  -  Wide $ block == t 
 Temp [,, t]<  -  as.matrix （宽）[sel.rows，3：Ti] 
} 
  

然后我可以一个3D矩阵，我可以在WinBUGS或OpenBUGS循环这样的：

pre $ for（i in 1：J）{＃Loop （＃1循环）$＃
$ b $（b
$ 1





$ b Temp [i，j，t] 〜dnorm（theta [i，j，t]）
theta [i，j，t]-α.lam[i] + blam1 * radiation [i，j] + blam2 * cwd [i，j] + blam3 * swd [i，j]
}}}


无论如何，不​​要担心上面的代码的细节，它只是作为一个例子从其他分析。我的主要问题是如何进行这种类型的分析，当我没有一个平衡的设计与相同数量的iButtons每个横断面？任何帮助将不胜感激。我明显是新的R和WinBUGS，并没有太多的电脑编码经验。
$ b $ p

谢谢！

<这里是数据看起来像长（堆）格式：

 >数据[1:15,1：4] 
 iButton julian块aveT 
 1 1 1 1 -4.5000000 
 2 1 2 1 -5.7500000 
 3 1 3 1 -3.5833333 
 4 1 4 1 -4.6666667 
 5 1 5 1 -2.5833333 
 6 1 6 1 -3.0833333 
 7 1 7 1 -1.5833333 
 8 1 8 1 -8.3333333 
 9 1 9 1 -5.0000000 
 10 1 10 1 -2.4166667 
 11 1 11 1 -1.7500000 
 12 1 12 1 -3.2500000 
 13 1 13 1 -3.4166667 
 14 1 14 1 -2.0833333 
 15 1 15 1 -1.7500000 
 
 $ div class =h2_lin>解决方案 
 
 
 
这允许每个变量的长度在这个列表中，每个索引都对应于横断面。
 
 
 
所以像这样：
 
 
  theta < -  list（）
 
 for（i （Data $ block））{
 ibuttons<  -  unique（Data $ iButton [Data $ block == i]）
 days<  -  unique（Data $ julian [Data $ block == （i，长度（ibuttons））的空矩阵与NA的
对于（j中的1：长度（ibuttons）） {
 for（t in 1：length（days））{
 theta [[i]] [j，t] <-fn（i，ibuttons [j]，days [t]）
} 
} 
} 
  
 
I am trying to conduct an hierarchical bayesian analysis but am having a little trouble with R and WinBUGS code. I don't have balanced data and am struggling with the coding. I have temperature data collected daily with iButtons (temperature recording devices) in transects and am trying to generate a model that relates this to remote sensing data. Unfortunately, each transect has a different number of iButtons so creating a 3D matrix of button(i), in transect(j), repeatedly "sampled" on day(t) is a problem for me.

Ultimately, my model will be something like:

Level 1
Temp[ijk] ~ N(theta[ijk], tau)
theta[ijk] = b0 + b1*x1 + . . . + bn*xn

Level 2
b0 = a00 + a01*y1 + . . . an*yn
b1 = a10 + a11*y1 ...

Level 3 (maybe?) - random level 2 intercepts

Normally I would do something like this:
    Wide <- reshape(Data1, idvar = c("iButton","block"), timevar = "julian", direction = "wide")
J <- length(unique(Data$block))
I <- length(unique(Data$iButton))
Ti <- length(unique(Data$julian))

Temp <- array(NA, dim = c(I, Ti, J))

for(t in 1:Ti) {
sel.rows <- Wide$block == t
Temp[,,t] <- as.matrix(Wide)[sel.rows, 3:Ti]
}
Then I could have a 3D matrix that I could loop through in WinBUGS or OpenBUGS as such:
for(i in 1:J) {          # Loop over transects/blocks
  for(j in 1:I) {        # Loop over buttons
    for(t in 1:Ti) {     # Loop over days
    Temp[i,j,t] ~ dnorm(theta[i,j,t])    
    theta[i,j,t] <- alpha.lam[i] + blam1*radiation[i,j] + blam2*cwd[i,j] + blam3*swd[i,j]
}}}
Anyway, don't worry about the details of the code above, it's just thrown together as an example from other analyses. My main question is how to do this type of analysis when I don't have a balanced design with equal numbers of iButtons per transect? Any help would be greatly appreciated. I'm clearly new to R and WinBUGS and don't have much previous computer coding experience.

Thanks!

oh and here is what the data look like in long (stacked) format:
    > Data[1:15, 1:4]
   iButton julian block       aveT
1        1      1     1 -4.5000000
2        1      2     1 -5.7500000
3        1      3     1 -3.5833333
4        1      4     1 -4.6666667
5        1      5     1 -2.5833333
6        1      6     1 -3.0833333
7        1      7     1 -1.5833333
8        1      8     1 -8.3333333
9        1      9     1 -5.0000000
10       1     10     1 -2.4166667
11       1     11     1 -1.7500000
12       1     12     1 -3.2500000
13       1     13     1 -3.4166667
14       1     14     1 -2.0833333
15       1     15     1 -1.7500000

解决方案
Can you try using a list instead?

This allows a variable length for each item in the list where each index would correspond to the transect.

So something like this:
theta <- list()

for(i in unique(Data$block)) {
  ibuttons <- unique(Data$iButton[Data$block==i])
  days <- unique(Data$julian[Data$block==i])
  theta[[i]] <- matrix(NA, length(ibuttons), length(days)) # Empty matrix with NA's
    for(j in 1:length(ibuttons)) {
      for(t in 1:length(days)) {
        theta[[i]][j,t] <- fn(i, ibuttons[j], days[t])
      }
    }
  }


                        
这篇关于尝试在R中创建并循环通过不平衡数据的矩阵的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！