problem with translation from lavaan to MCMC syntax
littlehifive opened this issue · 3 comments
Hi Ed, @bgoodri suggested that I come to you again for this question:
See the simulated dataset (test.csv) and a minimal example below for a pretty complex model that we are trying. We got this error message when trying to run the chunk below. The lavaan model syntax seems to be okay since it runs with lavaan::sem().
blavaan NOTE: ordinal models are new, please report bugs!
https://github.com/ecmerkle/blavaan/issues
[1] "Error in if (lamsign[l2, 1] == 1) l2 <- lamsign[l2, 2] : \n argument is of length zero\n"
attr(,"class")
[1] "try-error"
attr(,"condition")
<simpleError in if (lamsign[l2, 1] == 1) l2 <- lamsign[l2, 2]: argument is of length zero>
Error in blavaan(model_all_1, data = test, ordered = c("PSRA1
[test.csv](https://github.com/ecmerkle/blavaan/files/8751283/test.csv)
_1", "PSRA2_1", :
blavaan ERROR: problem with translation from lavaan to MCMC syntax.
model_all_1 <- '
# CFA
PSRA_1 =~ fl1*PSRA1_1 + fl2*PSRA2_1 + fl3*PSRA3_1 + fl4*PSRA4_1 + fl5*PSRA5_1 + fl6*PSRA6_1 + fl7*PSRA7_1 + fl8*PSRA8_1 + fl9*PSRA9_1 + fl10*PSRA10_1 + fl11*PSRA11_1 + fl12*PSRA12_1 + fl13*PSRA13_1
PSRA_2 =~ fl1*PSRA1_2 + fl2*PSRA2_2 + fl3*PSRA3_2 + fl4*PSRA4_2 + fl5*PSRA5_2 + fl6*PSRA6_2 + fl7*PSRA7_2 + fl8*PSRA8_2 + fl9*PSRA9_2 + fl10*PSRA10_2 + fl11*PSRA11_2 + fl12*PSRA12_2 + fl13*PSRA13_2
PSRA_3 =~ fl1*PSRA1_3 + fl2*PSRA2_3 + fl3*PSRA3_3 + fl4*PSRA4_3 + fl5*PSRA5_3 + fl6*PSRA6_3 + fl7*PSRA7_3 + fl8*PSRA8_3 + fl9*PSRA9_3 + fl10*PSRA10_3 + fl11*PSRA11_3 + fl12*PSRA12_3 + fl13*PSRA13_3
# thresholds
PSRA1_1|a1*t1 + a2*t2 + a3*t3
PSRA1_2|a1*t1 + a2*t2 + a3*t3
PSRA1_3|a1*t1 + a2*t2 + a3*t3
PSRA2_1|b1*t1 + b2*t2 + b3*t3
PSRA2_2|b1*t1 + b2*t2 + b3*t3
PSRA2_3|b1*t1 + b2*t2 + b3*t3
PSRA3_1|c1*t1 + c2*t2 + c3*t3
PSRA3_2|c1*t1 + c2*t2 + c3*t3
PSRA3_3|c1*t1 + c2*t2 + c3*t3
PSRA4_1|d1*t1 + d2*t2 + d3*t3
PSRA4_2|d1*t1 + d2*t2 + d3*t3
PSRA4_3|d1*t1 + d2*t2 + d3*t3
PSRA5_1|e1*t1 + e2*t2 + e3*t3
PSRA5_2|e1*t1 + e2*t2 + e3*t3
PSRA5_3|e1*t1 + e2*t2 + e3*t3
PSRA6_1|f1*t1 + f2*t2 + f3*t3
PSRA6_2|f1*t1 + f2*t2 + f3*t3
PSRA6_3|f1*t1 + f2*t2 + f3*t3
PSRA7_1|g1*t1 + g2*t2 + g3*t3
PSRA7_2|g1*t1 + g2*t2 + g3*t3
PSRA7_3|g1*t1 + g2*t2 + g3*t3
PSRA8_1|h1*t1 + h2*t2 + h3*t3
PSRA8_2|h1*t1 + h2*t2 + h3*t3
PSRA8_3|h1*t1 + h2*t2 + h3*t3
PSRA9_1|i1*t1 + i2*t2 + i3*t3
PSRA9_2|i1*t1 + i2*t2 + i3*t3
PSRA9_3|i1*t1 + i2*t2 + i3*t3
PSRA10_1|j1*t1 + j2*t2 + j3*t3
PSRA10_2|j1*t1 + j2*t2 + j3*t3
PSRA10_3|j1*t1 + j2*t2 + j3*t3
PSRA11_1|k1*t1 + k2*t2 + k3*t3
PSRA11_2|k1*t1 + k2*t2 + k3*t3
PSRA11_3|k1*t1 + k2*t2 + k3*t3
PSRA12_1|l1*t1 + l2*t2 + l3*t3
PSRA12_2|l1*t1 + l2*t2 + l3*t3
PSRA12_3|l1*t1 + l2*t2 + l3*t3
PSRA13_1|m1*t1 + m2*t2 + m3*t3
PSRA13_2|m1*t1 + m2*t2 + m3*t3
PSRA13_3|m1*t1 + m2*t2 + m3*t3
# CFA
# measurement model
EGRA_1 =~ VOC_1 + LTRC_1 + GRPH_1 + INV_1 + OPR_1 + RDCP_1
GRPH_1 ~~ LTRC_1
RDCP_1 ~~ VOC_1
RDCP_1 ~~ OPR_1
# measurement model
EGRA_2 =~ VOC_2 + LTRC_2 + GRPH_2 + INV_2 + OPR_2 + RDCP_2
GRPH_2 ~~ LTRC_2
RDCP_2 ~~ VOC_2
RDCP_2 ~~ OPR_2
# measurement model
EGRA_3 =~ VOC_3 + LTRC_3 + GRPH_3 + INV_3 + OPR_3 + RDCP_3
GRPH_3 ~~ LTRC_3
RDCP_3 ~~ VOC_3
RDCP_3 ~~ OPR_3
# cross-lagged effects
# region
IC_2 ~ IC_1
IC_3 ~ IC_1 + IC_2
WM_2 ~ WM_1
WM_3 ~ WM_1 + WM_2
PSRA_2 ~ PSRA_1 + IC_1
PSRA_3 ~ PSRA_1 + PSRA_2 + IC_2
EGRA_2 ~ EGRA_1 + PSRA_1 + IC_1
EGRA_3 ~ EGRA_1 + EGRA_2 + PSRA_2 + IC_2
# fully correlated at each wave
EGRA_1 ~~ PSRA_1 + IC_1 + WM_1
PSRA_1 ~~ IC_1 + WM_1
IC_1 ~~ WM_1
EGRA_2 ~~ PSRA_2 + IC_2 + WM_2
PSRA_2 ~~ IC_2 + WM_2
IC_2 ~~ WM_2
EGRA_3 ~~ PSRA_3 + IC_3 + WM_3
PSRA_3 ~~ IC_3 + WM_3
IC_3 ~~ WM_3
'
mydp <- dpriors(tau = "normal(0, 0.5)",
lambda = "normal(0, 2)",
beta = "normal(0.5,0.2)",
rho = "beta(1,1)",
nu = "normal(0, 5)")
fit_all_1 <- bsem(model_all_1, data = test,
ordered = c("PSRA1_1", "PSRA2_1", "PSRA3_1",
"PSRA4_1", "PSRA5_1", "PSRA6_1",
"PSRA7_1", "PSRA8_1", "PSRA10_1",
"PSRA11_1", "PSRA12_1", "PSRA13_1",
"PSRA1_2", "PSRA2_2", "PSRA3_2",
"PSRA4_2", "PSRA5_2", "PSRA6_2",
"PSRA7_2", "PSRA8_2", "PSRA10_2",
"PSRA11_2", "PSRA12_2", "PSRA13_2",
"PSRA1_3", "PSRA2_3", "PSRA3_3",
"PSRA4_3", "PSRA5_3", "PSRA6_3",
"PSRA7_3", "PSRA8_3", "PSRA10_3",
"PSRA11_3", "PSRA12_3", "PSRA13_3"
),
dp = mydp,
std.lv=TRUE,
seed = 1234)
Thanks for the report. I need to look at it more, but I am almost sure it is std.lv=TRUE causing the problem. If you can use std.lv=FALSE, that might be the easiest solution.
Some more detail: for std.lv=TRUE, we use the "flip the signs in generated quantities" approach described here. But if we flip the sign of a loading, then we also have to flip the signs of other parameters involving the corresponding latent variable. This can get complicated to code and to test, and I'm guessing there is a bug in there. I have sometimes thought that it would be easier to enforce the sign constraint with truncated priors, instead of this sign flipping (though truncated priors might cause problems for the sampler).
Thanks! The model runs when std.lv = F.