[R] R code: How to correct "Error in parse(text = x, keep.source = FALSE)" output in psych package using own dataset

William Dunlap wdun|@p @end|ng |rom t|bco@com
Thu Aug 29 19:32:50 CEST 2019


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

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.  E.g.,
> fakeData <- data.frame(A=1/(1:40), B=1/(2:41), C=1/(3:42), D=1/(4:43),
E=1/(5:44))
> cor(fakeData)
          A         B         C         D         E
A 1.0000000 0.9782320 0.9481293 0.9215071 0.8988962
B 0.9782320 1.0000000 0.9932037 0.9811287 0.9684658
C 0.9481293 0.9932037 1.0000000 0.9969157 0.9906838
D 0.9215071 0.9811287 0.9969157 1.0000000 0.9983014
E 0.8988962 0.9684658 0.9906838 0.9983014 1.0000000
> psych::omegaSem(fakeData)
Loading required namespace: lavaan
Loading required namespace: GPArotation
In factor.stats, I could not find the RMSEA upper bound . Sorry about that
Error in parse(text = x, keep.source = FALSE) :
  <text>:2:0: unexpected end of input
1: ~
   ^
In addition: Warning message:
In cov2cor(t(w) %*% r %*% w) :
  diag(.) had 0 or NA entries; non-finite result is doubtful
> psych::omega(fakeData)$model$lavaan
In factor.stats, I could not find the RMSEA upper bound . Sorry about that
[1] g =~ +A+B+C+D+E       F1=~  + B + C + D + E F2=~  + A
[4] F3=~
Warning message:
In cov2cor(t(w) %*% r %*% w) :
  diag(.) had 0 or NA entries; non-finite result is doubtful

You can get a result if you use nfactors=n where n is the number of the
good F<n> entries in psych::omega()$model$lavaan:
> psych::omegaSem(fakeData, nfactors=2)
...

Measures of factor score adequacy
                                                   g    F1*      F2*
Correlation of scores with factors             11.35  12.42    84.45
Multiple R square of scores with factors      128.93 154.32  7131.98
Minimum correlation of factor score estimates 256.86 307.64 14262.96
...
Does that work with your data?

This is a problem that the maintainer of psych,
>   maintainer("psych")
[1] "William Revelle <revelle using northwestern.edu>"
would like to know about.






Bill Dunlap
TIBCO Software
wdunlap tibco.com


On Thu, Aug 29, 2019 at 9:03 AM Danilo Esteban Rodriguez Zapata via R-help <
r-help using r-project.org> wrote:

> 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:
>
>
> https://stackoverflow.com/questions/57661750/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
>
> --
> Danilo E. Rodríguez Zapata
> Analista en Psicometría
> CEBIAC
>
>         [[alternative HTML version deleted]]
>
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