正如评论员所建议的，任何形式的子集都可以。这里是一个tidyverse方法，你可以在plm回归中过滤。

library(plm)
library(tidyverse)
library(stargazer)

model_plm_all <- plm(pcap ~ unemp + water + util + pc + gsp, 
                 data=Produc, 
                 effect = "individual", model = "within", 
                 index=c("state", "year"))

model_plm_region5 <- plm(pcap ~ unemp + water + util + pc + gsp, 
                 data=Produc %>%filter(region == 5), 
                 effect = "individual", model = "within", 
                 index=c("state", "year"))

model_plm_region6 <- plm(pcap ~ unemp + water + util + pc + gsp, 
                         data=Produc %>%filter(region == 6), 
                         effect = "individual", model = "within", 
                         index=c("state", "year"))

stargazer(model_plm_all, model_plm_region5, model_plm_region6, type="text",
          column.labels = c("All", "Region 5", "Region 6"), model.numbers = FALSE)

==========================================================================================
                                          Dependent variable:                             
             -----------------------------------------------------------------------------
                                                 pcap                                     
                        All                      Region 5                 Region 6        
------------------------------------------------------------------------------------------
unemp                92.486***                  94.759***                   4.667         
                      (11.648)                   (23.912)                 (27.650)        
                                                                                          
water                 0.759***                   1.110***                  0.637**        
                      (0.040)                    (0.155)                   (0.284)        
                                                                                          
util                  1.316***                   1.193***                 0.730***        
                      (0.017)                    (0.066)                   (0.149)        
                                                                                          
pc                     0.006                     0.039**                  0.127***        
                      (0.004)                    (0.017)                   (0.030)        
                                                                                          
gsp                    0.006                      0.001                    -0.022         
                      (0.004)                    (0.015)                   (0.029)        
                                                                                          
------------------------------------------------------------------------------------------
Observations            816                        136                       68           
R2                     0.970                      0.984                     0.960         
Adjusted R2            0.968                      0.982                     0.954         
F Statistic  4,899.365*** (df = 5; 763) 1,511.193*** (df = 5; 123) 280.529*** (df = 5; 59)
==========================================================================================
Note:                                                          *p<0.1; **p<0.05; ***p<0.01