我有一个数据矩阵(900列和5000行)，我想做一个pca .
矩阵在excel中看起来很好(意味着所有的值都是定量的)，但在我在R中阅读我的文件并尝试运行pca代码后，我得到一个错误说"以下变量不是定量的"，我得到一个非定量变量列表。
所以一般来说，有些变量是定量的，有些不是。请看下面的例子。当我检查变量1时，它是正确的和定量的。(文件中随机有一些变量是定量的)当我检查变量2时，它是不正确的和非定量的。(像这样的随机变量在文件中是非定量的)
> data$variable1[1:5]
[1] -0.7617504 -0.9740939 -0.5089303 -0.1032487 -0.1245882
> data$variable2[1:5]
[1] -0.183546332959017 -0.179283451229594 -0.191165669598284 -0.187060515423038
[5] -0.184409474669824
731 Levels: -0.001841783473108 -0.001855956210119 ... -1,97E+05
所以我的问题是，我怎样才能把所有的非定量变量变成定量的??
使文件变短并没有帮助，因为这些值本身就具有定量性。我不知道发生了什么事。这是我原始文件的链接<- https://docs.google.com/file/d/0BzP-YLnUNCdwakc4dnhYdEpudjQ/edit
我也试过下面给出的答案，但它仍然没有帮助。
让我展示一下我到底做了什么，
> data <- read.delim("file.txt", header=T)
> res.pca = PCA(data, quali.sup=1, graph=T)
Error in PCA(data, quali.sup = 1, graph = T) :
The following variables are not quantitative: batch
The following variables are not quantitative: target79
The following variables are not quantitative: target148
The following variables are not quantitative: target151
The following variables are not quantitative: target217
The following variables are not quantitative: target266
The following variables are not quantitative: target515
The following variables are not quantitative: target530
The following variables are not quantitative: target587
The following variables are not quantitative: target620
The following variables are not quantitative: target730
The following variables are not quantitative: target739
The following variables are not quantitative: target801
The following variables are not quantitative: target803
The following variables are not quantitative: target809
The following variables are not quantitative: target819
The following variables are not quantitative: target868
The following variables a
In addition: There were 50 or more warnings (use warnings() to see the first 50)
默认情况下，R将字符串强制转换为因子。这可能导致意想不到的行为。使用:
关闭此默认选项: read.csv(x, stringsAsFactors=F)
或者，您可以使用
强制因子为数字 newVar<-as.numeric(oldVar)
R将变量视为因素，正如Arun所提到的。因此，它生成一个data.frame(实际上是一个列表)。有许多方法可以解决这个问题，其中一种方法是按照以下方式将其转换为数据矩阵:
matrix <- as.numeric(as.matrix(data))
dim(matrix) <- dim(data)
现在你可以在矩阵上运行你的PCA了。
编辑:扩展一下这个例子，查理的建议的第二部分是行不通的。复制下面的会话，看看它是如何工作的;d <- data.frame(
a = factor(runif(2000)),
b = factor(runif(2000)),
c = factor(runif(2000)))
as.numeric(d) #does not work on a list (data frame is a list)
as.numeric(d$a) # does work, because d$a is a vecor, but this is not what you are
# after. R converts the factor levels to numeric instead of the actual value.
(m <- as.numeric(as.matrix(d))) # this does the rigth thing
dim(m) # but m loses the dimensions and is now a vector
dim(m) <- dim(d) # assign the dimensions of d to m
svd(m) # you can do the PCA function of your liking on m
as.numeric(as.character(data$variable2[1:5]))，先用as.character得到因子变量标签的字符串表示，再用as.numeric进行转换