Someone can help me? I tried several things and always don't converge # 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 40 variables and 1004 subjects, should not be a problem the number of variables also! -- View this message in context: http://r.789695.n4.nabble.com/sem-problem-did-not-converge-tp3305200p3305200.html Sent from the R help mailing list archive at Nabble.com.
Someone can help me? I tried several things and always don't converge I am making a confirmatory factor analysis. # 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 40 variables and 1004 subjects, should not be a problem the number of variables also! [[alternative HTML version deleted]]
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! [[alternative HTML version deleted]]
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 ##################################################### first- no missing data second - there are 40 variables and 1004 subjects, should not be a problem the number of variables also! [[alternative HTML version deleted]]
You have a fairly large and complex model there. This sort of model (almost) always causes problems. I would try fitting one factor at a time. That might help you to narrow down the problem. If one factor doesn't converge, the whole model won't converge. You might also consider joining the structural equation modeling list - semnet. This isn't really a sem (the package) or R problem, it's a more general SEM (the approach) problem. Jeremy Chen, F., Bollen, K., Paxton, P., Curran, P.J., & Kirby, J. (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological Methods and Research, 29, 468-508. On 14 February 2011 07:34, Felipe Bhering <felipelbhering at gmail.com> wrote:> > Someone can help me? I tried several things and always don't converge > > # 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 40 variables and 1004 subjects, should not be a problem the number > of variables also! > -- > View this message in context: http://r.789695.n4.nabble.com/sem-problem-did-not-converge-tp3305200p3305200.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >-- Jeremy Miles Psychology Research Methods Wiki: www.researchmethodsinpsychology.com