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这个问题可能过于特定于包，但我会重视我predict在数据集上使用该函数时可能出现的问题。

我正在使用的程序如下：

require(penalized)
# neg contains negative data
# pos contains positive data

现在，下面的过程旨在构建可比较的（在正面和负面案例方面平衡）训练和验证数据集。 

# 50% negative training set
negSamp <- neg %>% sample_frac(0.5) %>% as.data.frame()
# Negative validation set
negCompl <- neg[setdiff(row.names(neg),row.names(negSamp)),]
# 50% positive training set 
posSamp <- pos %>% sample_frac(0.5) %>% as.data.frame()
# Positive validation set
posCompl <- pos[setdiff(row.names(pos),row.names(posSamp)),]
# Combine sets
validat <- rbind(negSamp,posSamp)
training <- rbind(negCompl,posCompl)

好的，所以我们现在有两个可比较的集合。

[1] FALSE  TRUE
> dim(training)
[1] 1061  381
> dim(validat)
[1] 1060  381
> identical(names(training),names(validat))
[1] TRUE

我将模型拟合到训练集没有问题（我在这里尝试使用一系列 Lambda1 值）。但是，将模型拟合到验证数据集失败了，只是奇怪的错误描述。

> fit <- penalized(VoiceTremor,training[-1],data=training,lambda1=40,standardize=TRUE)
# nonzero coefficients: 13
> fit2 <- predict(fit, penalized=validat[-1], data=validat)
Error in .local(object, ...) : 
  row counts of "penalized", "unpenalized" and/or "data" do not match

只是为了确保这不是由于数据集中的某些 NA：

> identical(validat,na.omit(validat))
[1] TRUE

奇怪的是，我可能会生成一些与正确数据集相当的新数据：

data.frame(VoiceTremor="NVT",matrix(rnorm(380000),nrow=1000,ncol=380) ) -> neg
data.frame(VoiceTremor="VT",matrix(rnorm(380000),nrow=1000,ncol=380) ) -> pos
> dim(pos)
[1] 1000  381
> dim(neg)
[1] 1000  381

and run the procedure above, and then the second fit works! How come? What could be wrong with my second (not training) data set?

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1 回答 1

0

Ok,

I found the solution to this problem. The problem was in my finding of complementary data sets. 

neg[setdiff(row.names(neg),row.names(negSamp)),]

does not do the right thing, but

neg %>% 
rownames_to_column() %>% 
filter(! rowname %in% row.names(negSamp)) %>% 
column_to_rownames() %>% data.frame()

does. With this change, along with using data.frame instead of as.data.frame then it all works.

于 2016-08-18T08:25:22.990 回答