rfa {pairwise} | R Documentation |
Calculation of the rasch residual factor analysis proposed by Wright (1996) and further discussed by Linacre (1998) to detect multidimensionality.
rfa( pers_obj, na_treat = 0, tr = FALSE, use = "complete.obs", res = "stdr", method = "pearson", cor = TRUE )
pers_obj |
an object of class |
na_treat |
value to be assigned to residual cells which have missing data in the original response matrix. default is set to |
tr |
a logical value indicating whether the data (the residual matrix) is transposed prior to calculation. This would perform a person analysis rather than a item analysis. The default is set to item analysis. |
use |
a character string as used in function |
res |
a character string defining which type of (rasch–) residual to analyze when computing covariances or correlations. This must be (exactly) one of the strings "sr" for score residuals , "stdr" for standardised residuals, "srsq" for score residuals squared, or "stdrsq" for standardised residuals squared. The default is set to |
method |
a character string as used in function |
cor |
a logical value indicating whether the calculation should use the correlation matrix or the covariance matrix.The default is set to |
no details in the moment.
An object of class c("rfa","list")
.
Wright, B. D. (1996). Comparing Rasch measurement and factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 3(1), 3–24.
Linacre, J. M. (1998). Detecting multidimensionality: which residual data-type works best? Journal of outcome measurement, 2, 266–283.
###################### ######## data(bfiN) # loading reponse data pers_obj <- pers(pair(bfiN)) result <- rfa(pers_obj) summary(result) plot(result) ####