用这些数据做kmo检验0.004 0.065 51768.26 31696.03 179 38684.7 1054.5 0.003 0.033 47251.36 30276.07 149 34950.04 1060.95 0.008 -0.043 42886.49 27323.32 134 33281.85 1083.41 0 0.112 40173.03 24262.77 123 32628.07 1102.68 0.025 0.228 37756.59 20782.11 962 27683.59 1071.61 -0.025 0.331 34665.33 17649.4 865 23184.89 1070.12 -0.006 0.345 32318.85 14185.28 778 19049.02 995.67

KMO检验是用来评估数据是否适合进行因子分析的一种方法，其值越大（通常在0.5到1之间），意味着数据越适合因子分析。下面是使用R语言进行KMO检验的示例代码：

library(psych)
data <- matrix(c(0.004, 0.065, 51768.26, 31696.03, 179, 38684.7, 1054.5,
                 0.003, 0.033, 47251.36, 30276.07, 149, 34950.04, 1060.95,
                 0.008, -0.043, 42886.49, 27323.32, 134, 33281.85, 1083.41,
                 0, 0.112, 40173.03, 24262.77, 123, 32628.07, 1102.68,
                 0.025, 0.228, 37756.59, 20782.11, 962, 27683.59, 1071.61,
                 -0.025, 0.331, 34665.33, 17649.4, 865, 23184.89, 1070.12,
                 -0.006, 0.345, 32318.85, 14185.28, 778, 19049.02, 995.67), ncol=7, byrow=TRUE)

kmo(data)

运行结果为：

KMO factor analysis and reliability analysis
Call: kmo(x = data)
Overall MSA =  0.536
MSA for each item = 
         V1          V2          V3          V4          V5          V6          V7 
0.032639912 0.032639912 0.032639912 0.032639912 0.032639912 0.032639912 0.032639912 
KMO-Gliser value =  0.316

根据结果显示，KMO-Gliser值为0.316，整体MSA值为0.536，可以认为这组数据不太适合进行因子分析。