我的数据存储在名为'Gap3'的数据框中，结构如下：
> summary(Gap3)
region time rC2R g
Beijing : 19 1995 : 31 Min. :0.000 Min. : 3.80
Tianjin : 19 1996 : 31 1st Qu.:2.404 1st Qu.: 9.70
Hebei : 19 1997 : 31 Median :2.819 Median :11.50
Shanxi : 19 1998 : 31 Mean :2.898 Mean :11.47
InnerMongolia: 19 1999 : 31 3rd Qu.:3.240 3rd Qu.:12.90
Liaoning : 19 2000 : 31 Max. :5.605 Max. :23.80
(Other) :475 (Other):403 NA's :2
CCover FDI FDS HC
Min. :0.0348 Min. :0.00000 Min. :0.732 Min. : 2.599
1st Qu.:0.2402 1st Qu.:0.01100 1st Qu.:1.884 1st Qu.: 7.040
Median :0.2945 Median :0.02240 Median :2.303 Median : 7.852
Mean :0.3130 Mean :0.03247 Mean :2.438 Mean : 7.805
3rd Qu.:0.3726 3rd Qu.:0.04180 3rd Qu.:2.733 3rd Qu.: 8.546
Max. :0.7852 Max. :0.44940 Max. :7.303 Max. :12.028
NA's :2 NA's :8 NA's :2
I IE MedCResCover MedCWoCover
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.3332 1st Qu.:0.0792 1st Qu.:0.0536 1st Qu.:0.0518
Median :0.4389 Median :0.1231 Median :0.2201 Median :0.2159
Mean :0.4879 Mean :0.3029 Mean :0.3159 Mean :0.2103
3rd Qu.:0.6251 3rd Qu.:0.3269 3rd Qu.:0.5560 3rd Qu.:0.3000
Max. :1.1126 Max. :2.0513 Max. :1.9183 Max. :0.9507
NA's :3
pGDP rCityRate RCover rFkOut
Min. : 1826 Min. :0.1590 Min. :0.00160 Min. :0.0492
1st Qu.: 6226 1st Qu.:0.3300 1st Qu.:0.04312 1st Qu.:0.1060
Median : 12437 Median :0.4210 Median :0.08665 Median :0.1456
Mean : 19034 Mean :0.4447 Mean :0.21798 Mean :0.1829
3rd Qu.: 26133 3rd Qu.:0.5280 3rd Qu.:0.27025 3rd Qu.:0.2071
Max. :100105 Max. :0.8960 Max. :1.05240 Max. :1.2914
NA's :153 NA's :1
TCover UnCover
Min. :0.0000 Min. :0.0000
1st Qu.:0.0997 1st Qu.:0.1536
Median :0.1591 Median :0.1880
Mean :0.2273 Mean :0.2028
3rd Qu.:0.3090 3rd Qu.:0.2376
缺失值的数量为：
> sum(is.na(Gap3))
[1] 171
维度为：
> dim(Gap3)
[1] 589 18
我目前的目的是决定我应该使用哪种效果，修复效果或随机效果。我的模型功能和我所做的如下所示：
> form1
rC2R ~ TCover + MedCResCover + UnCover + pGDP + I(pGDP^2) + g +
FDS + FDI + IE + I + rFkOut + HC + rCityRate
>gap.fe1 <- plm(form1, data=Gap3,model="within")
>gap.rd1 <- plm(form1, data=Gap3,model="random")
>phtest(gap.fe1,gap.rd1)
Error in solve.default(dvcov) :
system is computationally singular: reciprocal condition number = 1.117e-22
然后我将小于1的变量乘以100，然后再次执行该过程，但奇点出现了。在我的第三次尝试中，我删除了pGDP和I(pGDP^2)，这次它成功完成了。
phtest(gap.fe3,gap.rd3)
Hausman Test
data: form3
chisq = 94.967, df = 11, p-value = 1.762e-15
alternative hypothesis: one model is inconsistent
有人可以告诉我原因，为什么pGDP和I(pGDP^2)引起了奇点？