I'm testing several panel models based on the same data set, which is large with 265,904 observations.

Let's say I'm comparing models (1), (2), and (3) in Table 4.

 Table_4_1 <- plm(DV ~ X1 + X3 + X4 + X5 + X6, index="firm_id", model="within", data=Survivors_upto_2019) 
Table_4_2 <- plm(DV ~ X2 + X3 + X4 + X5 + X6, index="firm_id", model="within", data=Survivors_upto_2019) 
Table_4_3 <- plm(DV ~ X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, index="firm_id", model="within", data=Survivors_upto_2019)

 summary(Table_4_1) 
summary(Table_4_2) 
summary(Table_4_3)

Table_4_1 and Table_4_2 are not nested, but Table_4_2 and Table_4_3 are nested. Therefore, I used the following commands,

 jtest(Table_4_1,Table_4_2) 
pFtest(Table_4_2, Table_4_3) 

For test J, I got the following results.

 Estimate Std. Error t value Pr(>|t|) 
M1 + fitted(M2) 0.95029 0.0034222 277.683 < 2.2e-16 *** 
M2 + fitted(M1) 0.35485 0.0075335 47.103 < 2.2e-16 ***

For pFtest, I got an error.

 Warning message: 
In pf(stat, df1, df2, lower.tail = FALSE) : NaNs produced

My questions are:

1. How do I interpret the results of the J statistic?

2. For a nested panel comparison, how should I solve pFtest?