find_k | R Documentation |
This function is specifically for determining k in the context of factor analysis using change in RMSEA as the criterion for identifying the optimal factor model.
find_k( variables, n, p, m = NULL, max.k = 10, min.n = 200, rmsea0 = 0.05, rmseaA = 0.08, ... )
variables |
a |
n |
integer; number of observations. Ignored if |
p |
integer; number of variables to factor analyze. Ignored if |
m |
integer; maximum number of factors expected to be extracted from |
max.k |
integer; maximum number of folds. Default is 10. |
min.n |
integer; minimum sample size per fold. Default is 200 based on simulations from Curran et al. (2003). |
rmsea0 |
numeric; RMSEA under the null hypothesis. |
rmseaA |
numeric; RMSEA under the alternative hypothesis. |
... |
other arguments passed to |
named vector with the number of folds, sample size suggested by the power analysis, and the actual sample size used for determining k.
Curran, P. J., Bollen, K. A., Chen, F., Paxton, P., & Kirby, J. B. (2003). Finite sampling properties of the point estimates and confidence intervals of the RMSEA. Sociological Methods & Research, 32(2), 208-252. doi: 10.1177/0049124103256130
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. doi: 10.1037/1082-989X.1.2.130
find_k(n = 900, p = 20, m = 3) # adjust precision find_k(n = 900, p = 20, m = 3, rmsea0 = .03, rmseaA = .10)
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