Please use 'reply to all' for responses to R-help reponses.
What do you get with your original data for
psych::omega(my.data)$model$lavaan
? Any entries like "F3=~"?
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Thu, Aug 29, 2019 at 12:05 PM Danilo Esteban Rodriguez Zapata <
danilo_rodrig...@cun.edu.co> wrote:
> Dear William,
>
> Thank you for your answer, I would like to add some information that I
> just obtained looking in different sites and forums. Someone there ask me
> to create a fake data file, so I did that from my original data file. What
> I did was open the .csv file with notepad and replace all the 4 for 5 and
> the 2 for 1, then I saved the file again with no other changes. I also
> searched for the "~" in the file and I found nothing. Now with that file I
> did the omegaSem() function and it worked succesfully, so the weird thing
> here is that the omegaSem() function works with the fake data file, wich is
> exactly the same as the original file, but recoding some answers as I said.
>
> It seems to be an issue with the file. When I replace, lets say, the 5 for
> 6 and make the omegaSem() again, it works. Then I replace back again the 6
> for 5 in all the data and the function doesn't works anymore.
>
> El jue., 29 ago. 2019 a las 12:33, William Dunlap (<wdun...@tibco.com>)
> escribió:
>
>> > 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 <reve...@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@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]]
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>> 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.
>>>
>>
>
> --
> Danilo E. Rodríguez Zapata
> Analista en Psicometría
> CEBIAC
>
[[alternative HTML version deleted]]
______________________________________________
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