convergence warnings following imputation

[atanasj]

I am imputing longitudinal data (but this question is probably more general than only longitudinal). My specifications are:

> 
+ ## import pred via excel manipulation
+ pred_fu_ <-
+   readxl::read_xlsx(
+     here("data/tmp/mi_working.xlsx"),
+     sheet = "pred_matrix_fu",
+     range = "A1:V22"
+   ) %>%
+   .mice_pred_matrix()
+ pred_fu_
+ 
>                 ID  c sex age session_0 planned_final_0 pb_0 ac_0 bhs_0 tb_0 dep_0 anx_0 si_0 session_1
ID               0  0   0   0         0               0    0    0     0    0     0     0    0         0
c                0  0   0   0         0               0    0    0     0    0     0     0    0         0
sex              0 -2   0   1         0               0    1    0     0    0     0     1    0         0
age              0 -2   0   0         0               0    0    0     0    0     0     0    0         0
session_0        0 -2   0   0         0               0    0    0     0    0     0     0    0         0
planned_final_0  0 -2   0   0         0               0    0    0     0    0     0     0    0         0
pb_0             0 -2   1   0         0               0    0    1     1    1     1     1    1         0
ac_0             0 -2   0   1         0               0    1    0     1    1     1     0    1         0
bhs_0            0 -2   0   0         0               0    1    1     0    1     1     1    1         0
tb_0             0 -2   0   1         0               0    1    1     1    0     1     1    1         0
dep_0            0 -2   0   1         0               0    1    1     1    1     0     1    1         0
anx_0            0 -2   1   0         0               0    1    0     1    1     1     0    1         0
si_0             0 -2   0   0         0               0    1    1     1    1     1     1    0         0
session_1        0 -2   0   1         0               0    0    0     0    0     0     0    0         0
planned_final_1  0 -2   0   0         0               0    0    0     0    0     0     0    0         0
pb_1             0 -2   0   0         0               0    1    0     0    0     0     0    0         0
bhs_1            0 -2   0   0         0               0    0    0     1    0     0     0    0         0
tb_1             0 -2   0   1         0               0    0    0     0    1     0     0    0         0
dep_1            0 -2   0   0         0               0    0    0     0    0     1     0    0         0
anx_1            0 -2   1   0         0               0    0    0     0    0     0     1    0         0
si_1             0 -2   0   1         0               0    0    0     0    0     0     0    1         0
                planned_final_1 pb_1 bhs_1 tb_1 dep_1 anx_1 si_1
ID                            0    0     0    0     0     0    0
c                             0    0     0    0     0     0    0
sex                           0    0     0    0     0     0    0
age                           0    0     0    0     0     0    0
session_0                     0    0     0    0     0     0    0
planned_final_0               0    0     0    0     0     0    0
pb_0                          0    1     0    0     0     0    0
ac_0                          0    0     0    0     0     0    0
bhs_0                         0    0     1    0     0     0    0
tb_0                          0    0     0    1     0     0    0
dep_0                         0    0     0    0     1     0    0
anx_0                         0    0     0    0     0     1    0
si_0                          0    0     0    0     0     0    1
session_1                     0    0     0    0     0     0    0
planned_final_1               0    0     0    0     0     0    0
pb_1                          0    0     0    0     0     0    0
bhs_1                         0    0     0    0     0     0    0
tb_1                          0    0     0    0     0     0    0
dep_1                         0    0     0    0     0     0    0
anx_1                         0    0     0    0     0     0    0
si_1                          0    0     0    0     0     0    0
> 
+ ## init mi model
+ imp_t0t1 <-
+   mice(
+     lpa_t0t1_mi,
+     pred = pred_fu_,
+     meth = "2l.pmm",
+     maxit = 0,
+     m = 10
+   )
+ 
> 

I am getting the following warnings after 100 iterations (i.e., maxit = 100):

Warning messages:
1: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
2: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
3: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
4: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
5: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
6: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
7: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
8: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q
9: In optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q

Should I worry about them? The associated plots are attached below... They seem to mostly converge, but not sure... Any thoughts or help would be greatly appreciated.
sex_ac0.pdf
bhs0_dep0.pdf
anx0_sess1.pdf
pb1_tb1.pdf
dep1_si1.pdf

density_100_2.pdf