Comments (4)
It looks like the minimization that is done as part of factor model fitting does not converge for your specific dataset. Can you replicate the issue using R?
from factor_analyzer.
It looks like the minimization that is done as part of factor model fitting does not converge for your specific dataset. Can you replicate the issue using R?
Is there a setting that I can change in /Users/*/opt/anaconda3/lib/python3.7/site-packages/factor_analyzer/confirmatory_factor_analyzer.py
to work around that minimization?
Also, by "using R," do you mean the Lavaan pakage?
from factor_analyzer.
No, there’s no such setting because the minimization is a core part of the model fitting.
Yes, R + lavaan
is a reasonable counterpart.
from factor_analyzer.
No, there’s no such setting because the minimization is a core part of the model fitting.
Yes, R +
lavaan
is a reasonable counterpart.
I used lavaan and got somewhat more understandable errors:
Error in lav_samplestats_icov(COV = cov[[g]], ridge = ridge, x.idx = x.idx[[g]], :
lavaan ERROR: sample covariance matrix is not positive-definite
In addition: Warning messages:
1: In lav_data_full(data = data, group = group, cluster = cluster, :
lavaan WARNING: some observed variances are (at least) a factor 1000 times larger than others; use varTable(fit) to investigate
2: In lav_data_full(data = data, group = group, cluster = cluster, :
lavaan WARNING: small number of observations (nobs < nvar)
nobs = 33 nvar = 119
Update: I cut down to 2 indicators per factor and 4 factors in total. This avoided the (nobs < nvar) error but showed another error that might corroborate with python's factor_analyzer:
Warning messages:
1: In lav_data_full(data = data, group = group, cluster = cluster, :
lavaan WARNING: some observed variances are (at least) a factor 1000 times larger than others; use varTable(fit) to investigate
2: In lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
lavaan WARNING: the optimizer warns that a solution has NOT been found!
from factor_analyzer.
Related Issues (20)
- calculate_bartlett_sphericity() crashes with dataframe but not with numpy array HOT 1
- calculate_kmo() differs from psych.KMO() in R HOT 1
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- how to get Proportion Explained, RMSR and chi-squared? HOT 1
- Current release on conda HOT 1
- Mistake in correlation-function HOT 2
- Only 3 factors appear in factor loading matrix, but "n_factors=5" as input. Likely my error but I cannot find it. HOT 1
- Add pre-commit checks and apply them to all existing code
- random initial values for rotation matrix in GPA rotations HOT 2
- Regression method to calculate factor scores HOT 1
- Optimization error
- Comparison with SPSS HOT 2
- Switch to using `nose2` for tests instead of `nose` HOT 1
- Add support for Python 3.11 HOT 1
- Remove pre-commit from install dependencies, add it to "dev" extra HOT 1
- get_factor_variance() returns ndarray which is not ordered by variance.
- SciPy sum function is deprecated causing Factor-Analyzer to fail HOT 4
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- `UnboundLocalError` with principal-lapack method types and any oblique rotations HOT 1
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