缺失值NA的影响

最近学习logistic回归模型时，其中一步构造回归设计矩阵(mode.matrix)时遇到麻烦，现总结重点：

1.解释变量的数据类型是连续型还是离散型，离散型的需要弄清楚各个水类的个数，防止出现NA水平，改变了数据的长度。

## read data and create relevant variablescredit <- read.csv("C:/Users/Administrator/Desktop/DataMining/germancredit.csv")dim(credit)str(credit)'data.frame':   1000 obs. of  22 variables: $ Default        : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 1 1 1 2 ... $ checkingstatus1: Factor w/ 4 levels "A11","A12","A13",..: 1 2 4 1 1 4 4 2 4 2 ... $ duration       : int  6 48 12 42 24 36 24 36 12 30 ... $ history        : Factor w/ 3 levels "good","poor",..: 3 2 3 2 2 2 2 2 2 3 ... $ purpose        : Factor w/ 10 levels "A40","A41","A410",..: 5 5 8 4 1 8 4 2 5 1 ... $ amount         : int  1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ... $ savings        : Factor w/ 5 levels "A61","A62","A63",..: 5 1 1 1 1 5 3 1 4 1 ... $ employ         : Factor w/ 5 levels "A71","A72","A73",..: 5 3 4 4 3 3 5 3 4 1 ... $ installment    : int  4 2 2 2 3 2 3 2 2 4 ... $ status         : Factor w/ 4 levels "A91","A92","A93",..: 3 2 3 3 3 3 3 3 1 4 ... $ others         : Factor w/ 3 levels "A101","A102",..: 1 1 1 3 1 1 1 1 1 1 ... $ residence      : int  4 2 3 4 4 4 4 2 4 2 ... $ property       : Factor w/ 4 levels "A121","A122",..: 1 1 1 2 4 4 2 3 1 3 ... $ age            : int  67 22 49 45 53 35 53 35 61 28 ... $ otherplans     : Factor w/ 3 levels "A141","A142",..: 3 3 3 3 3 3 3 3 3 3 ... $ housing        : Factor w/ 3 levels "A151","A152",..: 2 2 2 3 3 3 2 1 2 2 ... $ cards          : int  2 1 1 1 2 1 1 1 1 2 ... $ job            : Factor w/ 4 levels "A171","A172",..: 3 3 2 3 3 2 3 4 2 4 ... $ liable         : int  1 1 2 2 2 2 1 1 1 1 ... $ tele           : Factor w/ 2 levels "A191","A192": 2 1 1 1 1 2 1 2 1 1 ... $ foreign        : Factor w/ 2 levels "foreign","german": 1 1 1 1 1 1 1 1 1 1 ... $ rent           : Factor w/ 2 levels "FALSE","TRUE": 1 1 1 1 1 1 1 2 1 1 ...

可以看出各个变量的类型。

credit$Default <- factor(credit$Default)  #整数类型变成因子类型## re-level the credit history and a few other variableslevels(credit$history) = c("good","good","poor","poor","terrible")levels(credit$foreign) <- c("foreign","german")credit$rent <- factor(credit$housing=="A151")table(credit$rent)credit$purpose <- factor(credit$purpose,                        levels=c("A40","A41","A42","A43","A44",                                 "A45","A46","A47","A48","A49","A410"))  #重新指定水平为11类

这里重新指定因子的类型，原本我认为不需要，因子read.csv在读入字符串变量时已经将其转变为因子类型，但是变量purpose较特殊，必须指定。

原因在于purpose变量，读入的原始数据中purpose变量共有10个水平，如果此时不重新指定purose变量的水平，

credit$purpose <- factor(credit$purpose,levels=c("A40","A41","A42","A43","A44",                       "A45","A46","A47","A48","A49","A410"))  #重新指定水平为11类

那么直接levels（credit$purpose）就会把原始的第八个水平指定为空，造成purpose变量的取值中有50NA，在计算回归设计矩阵时就会出现下标出界的错误。