使用CatNet包的贝叶斯网络：处理丢失的数据 [英] bayesian networks with the catnet package: handling missing data

问题描述

我对这个社区、r和一般的编程都是新手。(提前感谢您的耐心！)我正在做一个涉及贝叶斯网络的项目。

海峡问题。以下代码是在此站点上发布的，以回答名为"bnlearn Package R中的NA/NaN值"的问题

rm(list=ls())

### generate random data (not simply independent binomials)
set.seed(123)
n.obs <- 10
a1 <- rbinom(n.obs,1,.3)
a2 <- runif(n.obs)
a3 <- floor(-3*log(.25+3*a2/4))
a3[a3>=2] <- NA
a2 <- floor(2*a2)
my.data <- data.frame(a1,a2,a3 )
### discretize data into proper categories
my.data <- cnDiscretize(my.data,numCategories=2)

my.data
##    a1 a2 a3
## 1   1  2  1
## 2   2  1  2
## 3   1  2  1
## 4   2  2  2
## 5   2  1 NA
## 6   1  2  1
## 7   1  1 NA
## 8   2  1 NA
## 9   1  1 NA
## 10  1  2  1

## say we want a2 conditional on a1,a3

## first generate a network with a1,a3 ->a2
cnet <- cnNew(
      nodes = c("a1", "a2", "a3"),
      cats = list(c("1","2"), c("1","2"), c("1","2")),
      parents = list(NULL, c(1,3), NULL)
      )


## set the empirical probabilities from data=my.data
cnet2 <- cnSetProb(cnet,data=my.data)

## to get the conditional probability table
cnProb(cnet2,which='a2')

##$a2
##         a1        a3         0         1
## A 0.0000000 0.0000000 0.0000000 1.0000000
## B 0.0000000 1.0000000 0.5712826 0.4287174
## A 1.0000000 0.0000000 0.0000000 1.0000000
## B 1.0000000 1.0000000 0.5685786 0.4314214

但是，当我复制、粘贴并运行代码时，我得到了不同的结果(见下文)。

rm(list=ls())

### generate random data (not simply independent binomials)
set.seed(123)
n.obs <- 10
a1 <- rbinom(n.obs,1,.3)
a2 <- runif(n.obs)
a3 <- floor(-3*log(.25+3*a2/4))
a3[a3>=2] <- NA
a2 <- floor(2*a2)
my.data <- data.frame(a1,a2,a3 )
### discretize data into proper categories
my.data <- cnDiscretize(my.data,numCategories=2)

my.data
##   a1 a2 a3
## 1   1  2  1
## 2   2  1  2
## 3   1  2  1
## 4   2  2  2
## 5   2  1 NA
## 6   1  2  1
## 7   1  1 NA
## 8   2  1 NA
## 9   1  1 NA
## 10  1  2  1

## say we want a2 conditional on a1,a3 
## first generate a network with a1,a3 ->a2
cnet <- cnNew(
    nodes = c("a1", "a2", "a3"),
    cats = list(c("1","2"), c("1","2"), c("1","2")),
    parents = list(NULL, c(1,3), NULL)
    )


## set the empirical probabilities from data=my.data
cnet2 <- cnSetProb(cnet,data=my.data)

## to get the conditional probability table
cnProb(cnet2,which='a2')
## $a2
##   a1  a3   1   2
## A 1.0 1.0 0.0 1.0
## B 1.0 2.0 0.5 0.5
## A 2.0 1.0 0.5 0.5
## B 2.0 2.0 0.5 0.5

谁能解释一下为什么我的结果不同？我问这个问题是因为我想了解CatNet是如何处理丢失的数据的。

最佳，

约翰

推荐答案

顶部/底部代码相同--它们应该输出相同的结果。我检查了catnet函数中使用相同函数的其他包--这可能是您的问题。在使用非基函数时，最好使用::表示法。

rm(list=ls())
library(catnet)

### generate random data (not simply independent binomials)
set.seed(123)
n.obs <- 10
a1 <- rbinom(n.obs,1,.3)
a2 <- runif(n.obs)
a3 <- floor(-3*log(.25+3*a2/4))
a3[a3>=2] <- NA
a2 <- floor(2*a2)
my.data <- data.frame(a1,a2,a3 )
### discretize data into proper categories
my.data <- catnet::cnDiscretize(my.data,numCategories=2)

my.data
##    a1 a2 a3
## 1   1  2  1
## 2   2  1  2
## 3   1  2  1
## 4   2  2  2
## 5   2  1 NA
## 6   1  2  1
## 7   1  1 NA
## 8   2  1 NA
## 9   1  1 NA
## 10  1  2  1

## say we want a2 conditional on a1,a3

## first generate a network with a1,a3 ->a2
cnet <- catnet::cnNew(
  nodes = c("a1", "a2", "a3"),
  cats = list(c("1","2"), c("1","2"), c("1","2")),
  parents = list(NULL, c(1,3), NULL)
)


## set the empirical probabilities from data=my.data
cnet2 <- catnet::cnSetProb(cnet,data=my.data)

## to get the conditional probability table
catnet::cnProb(cnet2,which='a2')

# $a2
# a1  a3   1   2
# A 1.0 1.0 0.0 1.0
# B 1.0 2.0 0.5 0.5
# A 2.0 1.0 0.5 0.5
# B 2.0 2.0 0.5 0.5

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