R代码：如何使用自己的数据集更正心理包中的“解析错误（文本= x，keep.source = FALSE）”输出

Question

This is a problem related to my last question referred to the omegaSem() function in the psych package (that is already solved because I realized that I was missing a variable assignment and because of that I had an 'object not found' error:

One of the omegaSem function arguments is an object not found

I was trying to use that function following the guide to find McDonald's hierarchical Omega by Dr William Revelle:

http://personality-project.org/r/psych/HowTo/omega.pdf

So now, with the variable error corrected, I'm having a different error that does not occur when I use the same function with the example database (Thurstone) provided in the tutorial that comes with the psych package. I mean, I'm able to use the function succesfully using the Thurstone data (with no other action, I have the expected result) but the function doesn't work when I use my own data.

I searched over other posted questions, and the actions that they perform are not even similar to what I'm trying to do. I have almost two weeks using R, so I'm not able to identify yet how can I extrapolate the solutions for that error message to my procedure (because it seems to be frequent), although I have basic code knowledge. However related questions give no anwer by now.

Additionally, I decided to look over more documentation about the package, and when I was testing other functions, I was able to use the omegaSem() function with another example database, BUT after and only after I did the schmid transformation. So with that, I discovered that when I tried to use the omegaSem() function before the schmid tranformation I had the same error message, but not after that tranformation with this second example database.

This make sense with the actual procedure of the omegaSem() procedure, but I'm suposing that it must be done completely and automatically by the omegaSem() function as it is explained in the guide and I have understood until now, as it follows:

1. omegaSem() applies factor analysis
2. omegaSem() rotate factors obliquely
3. omegaSem() transform data with Schmid Leiman (schmid)

-------necessary steps to print output-------------------

4. omegaSem() print McDonald's hierarchical Omega

So here, another questions appears: - Why the omegaSem() function works with the Thurstone database without any other action and only works for the second example database after performing the schmid transformation? - Why with other databases I dont have the same output applying the omegaSem() function directly? - How is this related to the error message that the compiler shows when I try to apply the function directly to the database?

This is the code that I'm using now: (example of the succesfull omegaSem() done after schmid tranformation not included)

> library(psych)
> library(ctv, lavaan)
> library(GPArotation)
> my.data <- read.file()
Data from the .csv file D:\Users\Admon\Documents\prueba_export_1563806208742.csv has been loaded.
> describe(my.data)
           vars   n mean   sd median trimmed  mad min max range  skew kurtosis
AUT_10_04     1 195 4.11 0.90      4    4.23 1.48   1   5     4 -0.92     0.33
AUN_07_01     2 195 3.79 1.14      4    3.90 1.48   1   5     4 -0.59    -0.71
AUN_07_02     3 195 3.58 1.08      4    3.65 1.48   1   5     4 -0.39    -0.56
AUN_09_01     4 195 4.15 0.80      4    4.23 1.48   1   5     4 -0.76     0.51
AUN_10_01     5 195 4.25 0.79      4    4.34 1.48   1   5     4 -0.91     0.74
AUT_11_01     6 195 4.43 0.77      5    4.56 0.00   1   5     4 -1.69     3.77
AUT_17_01     7 195 4.46 0.67      5    4.55 0.00   1   5     4 -1.34     2.96
AUT_20_03     8 195 4.44 0.65      5    4.53 0.00   2   5     3 -0.84     0.12
CRE_05_02     9 195 2.47 1.01      2    2.43 1.48   1   5     4  0.35    -0.46
CRE_07_04    10 195 2.42 1.08      2    2.34 1.48   1   5     4  0.51    -0.43
CRE_10_01    11 195 4.41 0.68      5    4.51 0.00   2   5     3 -0.79    -0.12
CRE_16_02    12 195 2.75 1.23      3    2.69 1.48   1   5     4  0.29    -0.96
EFEC_03_07   13 195 4.35 0.69      4    4.45 1.48   1   5     4 -0.95     1.59
EFEC_05      14 195 4.53 0.59      5    4.60 0.00   3   5     2 -0.82    -0.34
EFEC_09_02   15 195 2.19 0.91      2    2.11 1.48   1   5     4  0.57    -0.03
EFEC_16_03   16 195 4.21 0.77      4    4.29 1.48   2   5     3 -0.71    -0.04
EVA_02_01    17 195 4.47 0.61      5    4.54 0.00   3   5     2 -0.70    -0.50
EVA_07_01    18 195 4.38 0.60      4    4.43 1.48   3   5     2 -0.40    -0.70
EVA_12_02    19 195 2.64 1.22      2    2.59 1.48   1   5     4  0.30    -1.00
EVA_15_06    20 195 4.19 0.74      4    4.26 1.48   2   5     3 -0.55    -0.29
FLX_04_01    21 195 4.32 0.69      4    4.41 1.48   2   5     3 -0.71     0.05
FLX_04_05    22 195 4.23 0.74      4    4.32 0.00   1   5     4 -0.99     1.69
FLX_08_02    23 195 2.87 1.19      3    2.86 1.48   1   5     4  0.07    -1.05
FLX_10_03    24 195 4.30 0.71      4    4.39 1.48   2   5     3 -0.84     0.66
IDO_01_06    25 195 3.10 1.26      3    3.13 1.48   1   5     4 -0.19    -1.08
IDO_05_02    26 195 2.89 1.26      3    2.87 1.48   1   5     4 -0.03    -1.16
IDO_09_03    27 195 3.87 0.97      4    3.99 1.48   1   5     4 -0.84     0.47
IDO_17_01    28 195 3.94 0.88      4    4.02 0.00   1   5     4 -0.93     1.23
IE_01_03     29 195 4.01 0.88      4    4.10 1.48   1   5     4 -0.91     0.94
IE_10_03     30 195 4.15 1.00      4    4.34 1.48   1   5     4 -1.31     1.28
IE_13_03     31 195 4.16 0.91      4    4.30 1.48   1   5     4 -1.26     1.74
IE_15_01     32 195 4.26 0.85      4    4.39 1.48   1   5     4 -1.16     1.08
LC_07_03     33 195 4.25 0.72      4    4.34 0.00   1   5     4 -1.07     2.64
LC_08_02     34 195 3.25 1.22      4    3.31 1.48   1   5     4 -0.41    -0.90
LC_11_03     35 195 3.50 1.14      4    3.56 1.48   1   5     4 -0.38    -0.68
LC_11_05     36 195 4.42 0.69      5    4.52 0.00   1   5     4 -1.14     1.97
ME_02_03     37 195 4.11 0.92      4    4.25 1.48   1   5     4 -1.18     1.29
ME_07_06     38 195 3.19 1.28      3    3.24 1.48   1   5     4 -0.28    -1.03
ME_09_01     39 195 4.24 0.77      4    4.34 1.48   1   5     4 -1.12     2.19
ME_09_06     40 195 3.23 1.33      4    3.29 1.48   1   5     4 -0.31    -1.14
NEG_01_03    41 195 4.18 0.76      4    4.27 0.00   1   5     4 -1.28     3.33
NEG_05_04    42 195 4.27 0.69      4    4.35 0.00   1   5     4 -0.87     1.75
NEG_07_03    43 195 4.32 0.73      4    4.43 1.48   1   5     4 -1.05     1.55
NEG_08_01    44 195 3.95 0.88      4    4.02 1.48   1   5     4 -0.67     0.29
OP_03_05     45 195 4.32 0.66      4    4.39 0.00   1   5     4 -0.99     2.54
OP_12_01     46 195 4.16 0.80      4    4.25 1.48   1   5     4 -1.02     1.57
OP_14_01     47 195 4.27 0.78      4    4.38 1.48   1   5     4 -1.15     1.67
OP_14_02     48 195 4.36 0.68      4    4.44 1.48   1   5     4 -1.07     2.35
ORL_01_03    49 195 4.36 0.77      4    4.49 1.48   1   5     4 -1.31     2.08
ORL_03_01    50 195 4.41 0.69      4    4.50 1.48   1   5     4 -1.28     2.77
ORL_03_05    51 195 4.36 0.74      4    4.48 1.48   2   5     3 -1.13     1.28
ORL_10_05    52 195 4.40 0.68      4    4.48 1.48   1   5     4 -1.18     2.57
PER_08_02    53 195 3.23 1.29      4    3.29 1.48   1   5     4 -0.26    -1.17
PER_16_01    54 195 4.29 0.70      4    4.38 1.48   2   5     3 -0.74     0.27
PER_19_06    55 195 3.19 1.25      3    3.24 1.48   1   5     4 -0.20    -1.06
PER_22_06    56 195 4.21 0.73      4    4.29 0.00   1   5     4 -0.89     1.46
PLA_01_03    57 195 4.23 0.68      4    4.31 0.00   2   5     3 -0.81     1.18
PLA_05_01    58 195 4.06 0.77      4    4.13 0.00   1   5     4 -0.89     1.29
PLA_07_02    59 195 2.94 1.19      3    2.94 1.48   1   5     4  0.00    -1.02
PLA_10_01    60 195 4.03 0.76      4    4.08 0.00   1   5     4 -0.68     0.87
PLA_12_02    61 195 2.67 1.11      2    2.62 1.48   1   5     4  0.41    -0.61
PLA_18_01    62 195 4.01 0.85      4    4.09 1.48   1   5     4 -0.82     0.78
PR_06_02     63 195 3.02 1.27      3    3.02 1.48   1   5     4 -0.01    -1.13
PR_15_03     64 195 3.55 1.07      4    3.62 1.48   1   5     4 -0.46    -0.22
PR_25_01     65 195 2.36 1.04      2    2.27 1.48   1   5     4  0.73     0.06
PR_25_06     66 195 2.95 1.17      3    2.94 1.48   1   5     4  0.04    -0.86
REL_09_05    67 195 3.81 0.95      4    3.89 1.48   1   5     4 -0.51    -0.31
REL_14_03    68 195 3.99 0.88      4    4.08 1.48   1   5     4 -0.75     0.39
REL_14_06    69 195 2.93 1.26      3    2.92 1.48   1   5     4  0.06    -1.11
REL_16_04    70 195 3.16 1.27      3    3.20 1.48   1   5     4 -0.13    -1.11
RS_02_03     71 195 4.14 0.75      4    4.22 0.00   1   5     4 -0.82     1.14
RS_07_05     72 195 4.29 0.67      4    4.38 0.00   2   5     3 -0.72     0.59
RS_08_05     73 195 4.04 0.88      4    4.13 1.48   1   5     4 -0.97     1.26
RS_13_03     74 195 4.19 0.69      4    4.25 0.00   2   5     3 -0.46    -0.17
TF_03_01     75 195 4.01 0.82      4    4.06 1.48   1   5     4 -0.63     0.32
TF_04_01     76 195 4.09 0.76      4    4.15 0.00   1   5     4 -0.70     0.76
TF_10_03     77 195 4.11 0.85      4    4.21 1.48   1   5     4 -0.96     0.99
TF_12_01     78 195 4.11 0.85      4    4.21 1.48   1   5     4 -1.10     1.66
TRE_09_05    79 195 4.29 0.79      4    4.39 1.48   1   5     4 -1.12     1.74
TRE_09_06    80 195 4.33 0.69      4    4.42 1.48   1   5     4 -1.10     2.36
TRE_26_04    81 195 2.97 1.20      3    2.96 1.48   1   5     4  0.08    -1.01
TRE_26_05    82 195 3.99 0.84      4    4.03 1.48   1   5     4 -0.41    -0.37

Until now, I have charged the libraries, import the my own database and did some simple descriptive statistics.

> r9 <- my.data
> omega(r9)
Omega 
Call: omega(m = r9)
Alpha:                 0.95 
G.6:                   0.98 
Omega Hierarchical:    0.85 
Omega H asymptotic:    0.89 
Omega Total            0.96 

Schmid Leiman Factor loadings greater than  0.2 
                g   F1*   F2*   F3*   h2   u2   p2
AUT_10_04    0.43              0.30 0.27 0.73 0.68
AUN_07_01                           0.05 0.95 0.53
AUN_07_02                           0.06 0.94 0.26
AUN_09_01    0.38              0.30 0.24 0.76 0.59
AUN_10_01    0.35              0.55 0.44 0.56 0.29
AUT_11_01    0.42              0.30 0.27 0.73 0.66
AUT_17_01    0.32              0.40 0.28 0.72 0.37
AUT_20_03    0.41              0.25 0.24 0.76 0.73
CRE_05_02-   0.24       -0.53       0.34 0.66 0.17
CRE_07_04-   0.37       -0.51       0.39 0.61 0.35
CRE_10_01    0.46              0.48 0.46 0.54 0.47
CRE_16_02-              -0.70       0.48 0.52 0.01
EFEC_03_07   0.46              0.31 0.31 0.69 0.68
EFEC_05      0.43              0.32 0.29 0.71 0.64
EFEC_09_02-  0.29       -0.46       0.29 0.71 0.28
EFEC_16_03   0.49              0.26 0.31 0.69 0.77
EVA_02_01    0.55              0.21 0.36 0.64 0.85
EVA_07_01    0.57                   0.37 0.63 0.89
EVA_12_02-              -0.61       0.39 0.61 0.06
EVA_15_06    0.50              0.37 0.39 0.61 0.65
FLX_04_01    0.57              0.30 0.42 0.58 0.78
FLX_04_05    0.52              0.26 0.34 0.66 0.80
FLX_08_02-              -0.78       0.60 0.40 0.00
FLX_10_03    0.39              0.29 0.24 0.76 0.63
IDO_01_06-              -0.80       0.64 0.36 0.00
IDO_05_02-              -0.78       0.62 0.38 0.00
IDO_09_03    0.41              0.49 0.42 0.58 0.40
IDO_17_01    0.51              0.51 0.54 0.46 0.49
IE_01_03     0.44              0.60 0.56 0.44 0.35
IE_10_03     0.41              0.53 0.44 0.56 0.37
IE_13_03     0.39              0.48 0.38 0.62 0.40
IE_15_01     0.39              0.40 0.31 0.69 0.49
LC_07_03     0.50                   0.27 0.73 0.91
LC_08_02                 0.83       0.69 0.31 0.00
LC_11_03     0.25                   0.10 0.90 0.60
LC_11_05     0.45        0.24       0.27 0.73 0.75
ME_02_03     0.55                   0.31 0.69 0.99
ME_07_06                 0.85       0.75 0.25 0.02
ME_09_01     0.64                   0.45 0.55 0.93
ME_09_06                 0.81       0.69 0.31 0.02
NEG_01_03    0.58              0.20 0.38 0.62 0.88
NEG_05_04    0.70                   0.50 0.50 0.98
NEG_07_03    0.64                   0.43 0.57 0.96
NEG_08_01    0.43              0.25 0.25 0.75 0.74
OP_03_05     0.62                   0.40 0.60 0.98
OP_12_01     0.67                   0.46 0.54 0.98
OP_14_01     0.60                   0.38 0.62 0.95
OP_14_02     0.66                   0.47 0.53 0.93
ORL_01_03    0.67                   0.47 0.53 0.96
ORL_03_01    0.66                   0.48 0.52 0.91
ORL_03_05    0.64                   0.46 0.54 0.90
ORL_10_05    0.66                   0.49 0.51 0.89
PER_08_02    0.21        0.84       0.75 0.25 0.06
PER_16_01    0.68              0.21 0.50 0.50 0.91
PER_19_06    0.20        0.73       0.58 0.42 0.07
PER_22_06    0.53                   0.30 0.70 0.94
PLA_01_03    0.57                   0.36 0.64 0.89
PLA_05_01    0.61                   0.42 0.58 0.89
PLA_07_02                0.75       0.61 0.39 0.04
PLA_10_01    0.56                   0.36 0.64 0.88
PLA_12_02                0.61       0.37 0.63 0.00
PLA_18_01    0.63                   0.47 0.53 0.85
PR_06_02                 0.77       0.62 0.38 0.03
PR_15_03     0.31       -0.39  0.24 0.31 0.69 0.31
PR_25_01-               -0.56       0.32 0.68 0.00
PR_25_06                 0.74       0.55 0.45 0.01
REL_09_05    0.41       -0.23  0.38 0.37 0.63 0.45
REL_14_03    0.41       -0.21  0.29 0.30 0.70 0.56
REL_14_06                0.66  0.21 0.48 0.52 0.04
REL_16_04                0.78       0.63 0.37 0.03
RS_02_03     0.57                   0.36 0.64 0.90
RS_07_05     0.68                   0.47 0.53 0.99
RS_08_05     0.44                   0.20 0.80 0.95
RS_13_03     0.67                   0.46 0.54 0.97
TF_03_01     0.66                   0.44 0.56 0.98
TF_04_01     0.74                   0.56 0.44 0.98
TF_10_03     0.70                   0.50 0.50 0.98
TF_12_01     0.61                   0.40 0.60 0.92
TRE_09_05    0.70              0.23 0.55 0.45 0.89
TRE_09_06    0.62                   0.41 0.59 0.93
TRE_26_04-              -0.68       0.47 0.53 0.00
TRE_26_05    0.55       -0.21       0.34 0.66 0.88

With eigenvalues of:
    g   F1*   F2*   F3* 
18.06  0.04 11.47  4.32 

general/max  1.57   max/min =   267.1
mean percent general =  0.58    with sd =  0.36 and cv of  0.63 
Explained Common Variance of the general factor =  0.53 

The degrees of freedom are 3078  and the fit is  34.62 
The number of observations was  195  with Chi Square =  5671.12  with prob <  2.8e-157
The root mean square of the residuals is  0.06 
The df corrected root mean square of the residuals is  0.06
RMSEA index =  0.078  and the 10 % confidence intervals are  0.063 NA
BIC =  -10559.18

Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 3239  and the fit is  51.52 
The number of observations was  195  with Chi Square =  8509.84  with prob <  0
The root mean square of the residuals is  0.16 
The df corrected root mean square of the residuals is  0.16 

RMSEA index =  0.104  and the 10 % confidence intervals are  0.089 NA
BIC =  -8569.4 

Measures of factor score adequacy             
                                                 g   F1*  F2*  F3*
Correlation of scores with factors            0.98  0.07 0.98 0.91
Multiple R square of scores with factors      0.95  0.00 0.97 0.83
Minimum correlation of factor score estimates 0.91 -0.99 0.94 0.66

 Total, General and Subset omega for each subset
                                                 g F1*  F2*  F3*
Omega total for total scores and subscales    0.96  NA 0.83 0.95
Omega general for total scores and subscales  0.85  NA 0.82 0.76
Omega group for total scores and subscales    0.09  NA 0.01 0.19

Now, until here, I apply the basic (non hierarchical) omega() function to my own database

> omegaSem(r9,n.obs=198)
Error in parse(text = x, keep.source = FALSE) : 
  <text>:2:0: unexpected end of input
1: ~ 

The previous is the error message that appears after trying to use the omegaSem() function directly with my own database.

Now, following, I present the expected output of omegaSem() applied directly using the Thurstone database. It's similar to the output of the basic omega() function but it has certain distinctions:

> r9 <- Thurstone
> omegaSem(r9,n.obs=500)

Call: omegaSem(m = r9, n.obs = 500)
Omega 
Call: omega(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip, 
    digits = digits, title = title, sl = sl, labels = labels, 
    plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option)
Alpha:                 0.89 
G.6:                   0.91 
Omega Hierarchical:    0.74 
Omega H asymptotic:    0.79 
Omega Total            0.93 

Schmid Leiman Factor loadings greater than  0.2 
                     g   F1*   F2*   F3*   h2   u2   p2
Sentences         0.71  0.56             0.82 0.18 0.61
Vocabulary        0.73  0.55             0.84 0.16 0.63
Sent.Completion   0.68  0.52             0.74 0.26 0.63
First.Letters     0.65        0.56       0.73 0.27 0.57
Four.Letter.Words 0.62        0.49       0.63 0.37 0.61
Suffixes          0.56        0.41       0.50 0.50 0.63
Letter.Series     0.59              0.62 0.73 0.27 0.48
Pedigrees         0.58  0.24        0.34 0.51 0.49 0.66
Letter.Group      0.54              0.46 0.52 0.48 0.56

With eigenvalues of:
   g  F1*  F2*  F3* 
3.58 0.96 0.74 0.72 

general/max  3.73   max/min =   1.34
mean percent general =  0.6    with sd =  0.05 and cv of  0.09 
Explained Common Variance of the general factor =  0.6 

The degrees of freedom are 12  and the fit is  0.01 
The number of observations was  500  with Chi Square =  7.12  with prob <  0.85
The root mean square of the residuals is  0.01 
The df corrected root mean square of the residuals is  0.01
RMSEA index =  0  and the 10 % confidence intervals are  0 0.026
BIC =  -67.45

Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 27  and the fit is  1.48 
The number of observations was  500  with Chi Square =  730.93  with prob <  1.3e-136
The root mean square of the residuals is  0.14 
The df corrected root mean square of the residuals is  0.16 

RMSEA index =  0.23  and the 10 % confidence intervals are  0.214 0.243
BIC =  563.14 

Measures of factor score adequacy             
                                                 g  F1*  F2*  F3*
Correlation of scores with factors            0.86 0.73 0.72 0.75
Multiple R square of scores with factors      0.74 0.54 0.51 0.57
Minimum correlation of factor score estimates 0.49 0.07 0.03 0.13

 Total, General and Subset omega for each subset
                                                 g  F1*  F2*  F3*
Omega total for total scores and subscales    0.93 0.92 0.83 0.79
Omega general for total scores and subscales  0.74 0.58 0.50 0.47
Omega group for total scores and subscales    0.16 0.34 0.32 0.32

 The following analyses were done using the  lavaan  package 

 Omega Hierarchical from a confirmatory model using sem =  0.79
 Omega Total  from a confirmatory model using sem =  0.93 
With loadings of 
                     g  F1*  F2*  F3*   h2   u2   p2
Sentences         0.77 0.49           0.83 0.17 0.71
Vocabulary        0.79 0.45           0.83 0.17 0.75
Sent.Completion   0.75 0.40           0.73 0.27 0.77
First.Letters     0.61      0.61      0.75 0.25 0.50
Four.Letter.Words 0.60      0.51      0.61 0.39 0.59
Suffixes          0.57      0.39      0.48 0.52 0.68
Letter.Series     0.57           0.73 0.85 0.15 0.38
Pedigrees         0.66           0.25 0.50 0.50 0.87
Letter.Group      0.53           0.41 0.45 0.55 0.62

With eigenvalues of:
   g  F1*  F2*  F3* 
3.87 0.60 0.79 0.76 

The degrees of freedom of the confimatory model are  18  and the fit is  57.11391  with p =  5.936744e-06
general/max  4.92   max/min =   1.3
mean percent general =  0.65    with sd =  0.15 and cv of  0.23 
Explained Common Variance of the general factor =  0.64 

Measures of factor score adequacy             
                                                 g   F1*  F2*  F3*
Correlation of scores with factors            0.90  0.68 0.80 0.85
Multiple R square of scores with factors      0.81  0.46 0.64 0.73
Minimum correlation of factor score estimates 0.62 -0.08 0.27 0.45

 Total, General and Subset omega for each subset
                                                 g  F1*  F2*  F3*
Omega total for total scores and subscales    0.93 0.92 0.82 0.80
Omega general for total scores and subscales  0.79 0.69 0.48 0.50
Omega group for total scores and subscales    0.14 0.23 0.35 0.31

To get the standard sem fit statistics, ask for summary on the fitted object>

I'm expecting to have the same output applying the function directly. My expectation is to make sure if its mandatory to make the schmid transformation before the omegaSem(). I'm supposing that not, because its not supposed to work like that as it says in the guide. Maybe this can be solved correcting the error message:

> r9 <- my.data
> omegaSem(r9,n.obs=198)
Error in parse(text = x, keep.source = FALSE) : 
  <text>:2:0: unexpected end of input
1: ~ 
   ^

Hope I've been clear enough. Feel free to ask any other information that you might need.

Thank you so much for giving me any guidance to reach the answer of this issue. I higly appreciate any help.

Regards,

Danilo

--------------------------EDIT--------------------------------------------------

Now I was asking for help on slack and someone there help me. We find that when we created a fake data file just replacing some of the original data file, the function works! Just replacing, for example the 4 for the 5 and save it back again. The thing is that when I use the original database i have the same error message

Answer 1

Well, I was cross.posting this question in several sites, I forgot to mention that. The thing is that It seems that there is a bug with the package. Following, I'm citing an answer that I received by mail

"This error probably comes from calling factor("~") and psych::omegaSem(data) will do that if all the columns in data are very highly correlated with one another. In that case omega(data, nfactor=n) will not be able to find n factors in the data but it returns "~" in place of the factors that it could not find. Eg,

> psych::omega(my.data)$model$lavaan [1] g =~ +AUT_10_04+AUN_07_01+AUN_07_02+AUN_09_01+AUN_10_01+AUT_11_01+AUT_17_01+AUT_20_03+CRE_05_02+CRE_07_04+CRE_10_01+CRE_16_02+EFEC_03_07+EFEC_05+EFEC_09_02+EFEC_16_03+EVA_02_01+EVA_07_01+EVA_12_02+EVA_15_06+FLX_04_01+FLX_04_05+FLX_08_02+FLX_10_03+IDO_01_06+IDO_05_02+IDO_09_03+IDO_17_01+IE_01_03+IE_10_03+IE_13_03+IE_15_01+LC_07_03+LC_08_02+LC_11_03+LC_11_05+ME_02_03+ME_07_06+ME_09_01+ME_09_06+NEG_01_03+NEG_05_04+NEG_07_03+NEG_08_01+OP_03_05+OP_12_01+OP_14_01+OP_14_02+ORL_01_03+ORL_03_01+ORL_03_05+ORL_10_05+PER_08_02+PER_16_01+PER_19_06+PER_22_06+PLA_01_03+PLA_05_01+PLA_07_02+PLA_10_01+PLA_12_02+PLA_18_01+PR_06_02+PR_15_03+PR_25_01+PR_25_06+REL_09_05+REL_14_03+REL_14_06+REL_16_04+RS_02_03+RS_07_05+RS_08_05+RS_13_03+TF_03_01+TF_04_01+TF_10_03+TF_12_01+TRE_09_05+TRE_09_06+TRE_26_04+TRE_26_05 [2] F1=~

Element #2 of that output, the empty fomula " F1=~ ", triggers the bug in omegaSem.

omegaSem needs to ignore such entries in omega's output. psych's author should be able to fix things up."