Someone can help me? I tried several things and always don't converge
I am making a confirmatory factor analysis model.
# 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)$correlations
hetdados40=hcor(dados40)
dados40.sem <- sem(model.dados40, dados40.cov, nrow(dados40))
Warning message:
In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names =
vars, :
Could not compute QR decomposition of Hessian.
Optimization probably did not converge.
#####################################################
The same happen if i put hetdados40 in the place of dados40.cov
of course hetdados40 has 1 in the diag, but any 0
what should i do? i tryed several things...
all value positive..
#####################################################
> eigen(hetdados40)$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
#####################################################
There are no missing data and 40 variables and 1004 subjects, should not be
a problem the number of variables also!
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