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,
...
)named vector with the number of folds (k), sample size suggested by the power analysis (power.n), and the actual sample size used for determining k (actual.n).
a data.frame (or convertible to a data.frame) with variables to factor analyze in columns and observations in rows.
The power analysis assumes all observations have complete data. Use n argument or remove rows manually to account for missingness.
integer; number of observations. Ignored if variables is provided.
integer; number of variables to factor analyze. Ignored ifvariables is provided.
integer; maximum number of factors expected to be extracted from variables. Default is p / 4 (i.e., 4 variables per factor).
integer; maximum number of folds. Default is 10. NULL indicates no maximum.
integer; minimum sample size per fold. Default is 200 based on simulations from Curran et al. (2003).
numeric; RMSEA under the null hypothesis.
numeric; RMSEA under the alternative hypothesis.
other arguments passed to findRMSEAsamplesize.
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. tools:::Rd_expr_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. tools:::Rd_expr_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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