 Someone can help me? I tried several things and always don't converge
I am making a confirmatory factor analysis model

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# Model
library(sem)
dados40.cov <- cov(dados40,method="spearman")
model.dados40 <- specify.model()
F1 ->  Item11, lam11, NA
F1 ->  Item31, lam31, NA
F1 ->  Item36, lam36, NA
F1 ->  Item54, lam54, NA
F1 ->  Item63, lam63, NA
F1 ->  Item65, lam55, NA
F1 ->  Item67, lam67, NA
F1 ->  Item69, lam69, NA
F1 ->  Item73, lam73, NA
F1 ->  Item75, lam75, NA
F1 ->  Item76, lam76, NA
F1 ->  Item78, lam78, NA
F1 ->  Item79, lam79, NA
F1 ->  Item80, lam80, NA
F1 ->  Item83, lam83, NA
F2 ->  Item12, lam12, NA
F2 ->  Item32, lam32, NA
F2 ->  Item42, lam42, NA
F2 ->  Item47, lam47, NA
F2 ->  Item64, lam64, NA
F2 ->  Item66, lam66, NA
F2 ->  Item68, lam68, NA
F2 ->  Item74, lam74, NA
F3 ->  Item3, lam3, NA
F3 ->  Item8, lam8, NA
F3 ->  Item18, lam18, NA
F3 ->  Item23, lam23, NA
F3 ->  Item28, lam28, NA
F3 ->  Item33, lam33, NA
F3 ->  Item38, lam38, NA
F3 ->  Item43, lam43, NA
F4 ->  Item9, lam9, NA
F4 ->  Item39, lam39, NA
F5 ->  Item5, lam5, NA
F5 ->  Item10, lam10, NA
F5 ->  Item20, lam20, NA
F5 ->  Item25, lam25, NA
F5 ->  Item30, lam30, NA
F5 ->  Item35, lam35, NA
F5 ->  Item45, lam45, NA
Item3 <-> Item3, e3,   NA
Item5 <-> Item5, e5,   NA
Item8 <-> Item8, e8,   NA
Item9 <-> Item9, e9,   NA
Item10 <-> Item10, e10,   NA
Item11 <-> Item11, e11,   NA
Item12 <-> Item12, e12,   NA
Item18 <-> Item18, e18,   NA
Item20 <-> Item20, e20,   NA
Item23 <-> Item23, e23,   NA
Item25 <-> Item25, e25,   NA
Item28 <-> Item28, e28,   NA
Item30 <-> Item30, e30,   NA
Item31 <-> Item31, e31,   NA
Item32 <-> Item32, e32,   NA
Item33 <-> Item33, e33,   NA
Item35 <-> Item35, e35,   NA
Item36 <-> Item36, e36,   NA
Item38 <-> Item38, e38,   NA
Item39 <-> Item39, e39,   NA
Item42 <-> Item42, e42,   NA
Item43 <-> Item43, e43,   NA
Item45 <-> Item45, e45,   NA
Item47 <-> Item47, e47,   NA
Item54 <-> Item54, e54,   NA
Item63 <-> Item63, e63,   NA
Item64 <-> Item64, e64,   NA
Item65 <-> Item65, e65,   NA
Item66 <-> Item66, e66,   NA
Item67 <-> Item67, e67,   NA
Item68 <-> Item68, e68,   NA
Item69 <-> Item69, e69,   NA
Item73 <-> Item73, e73,   NA
Item74 <-> Item74, e74,   NA
Item75 <-> Item75, e75,   NA
Item76 <-> Item76, e76,   NA
Item78 <-> Item78, e78,   NA
Item79 <-> Item79, e79,   NA
Item80 <-> Item80, e80,   NA
Item83 <-> Item83, e83,   NA
F1 <-> F1, NA,    1
F2 <-> F2, NA,    1
F3 <-> F3, NA,    1
F4 <-> F4, NA,    1
F5 <-> F5, NA,    1
F1 <-> F2, F1F2, NA
F1 <-> F3, F1F3, NA
F1 <-> F4, F1F4, NA
F1 <-> F5, F1F5, NA
F2 <-> F3, F2F3, NA
F2 <-> F4, F2F4, NA
F2 <-> F5, F2F5, NA
F3 <-> F4, F3F4, NA
F3 <-> F5, F3F5, NA
F4 <-> F5, F4F5, NA


###i tryed several correlations, such as hetcor and polychor of polycor
library


hcor <- function(data) hetcor(data, std.err=FALSE)$correlations
hetdados40=hcor(dados40)


dados40.sem <- sem(model.dados40, dados40.cov, nrow(dados40))
Warning message:
In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names =
vars,  :
  Could not compute QR decomposition of Hessian.
Optimization probably did not converge.

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The same happen if i put hetdados40 in the place of dados40.cov
of course hetdados40 has 1 in the diag, but any 0

what should i do? i tryed several things...

all value positive..

#####################################################

> eigen(hetdados40)$values
 [1] 14.7231030  4.3807378  1.6271780  1.4000193  1.0670784  1.0217670
 [7]  0.8792466  0.8103790  0.7397817  0.7279262  0.6909955  0.6589746
[13]  0.6237204  0.6055884  0.5777750  0.5712017  0.5469284  0.5215437
[19]  0.5073809  0.4892339  0.4644124  0.4485545  0.4372404  0.4290573
[25]  0.4270672  0.4071262  0.3947753  0.3763811  0.3680527  0.3560231
[31]  0.3537934  0.3402836  0.3108977  0.3099143  0.2819351  0.2645035
[37]  0.2548654  0.2077900  0.2043732  0.1923942
> eigen(dados40.cov)$values
 [1] 884020.98 337855.95 138823.30 126291.58  87915.21  79207.04  73442.71
 [8]  68388.11  60625.26  58356.54  55934.05  54024.00  50505.10  48680.26
[15]  46836.47  45151.23  43213.65  41465.42  40449.59  37824.73  37622.43
[22]  36344.34  35794.22  33959.29  33552.64  32189.94  31304.44  30594.85
[29]  30077.32  29362.66  26928.12  26526.72  26046.47  24264.50  23213.18
[36]  21503.97  20312.55  18710.97  17093.24  14372.21

#####################################################

first- no missing data
second - there are 40 variables and 1004 subjects, should not be a problem
the number of variables also!

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