Description Usage Arguments Value Examples
gscals
estimates GSC models alternating least squares.
This leads to estimations of weights for the composites and an overall fit measure.
1 2 |
dat |
(n,p)-matrix, the values of the manifest variables. The columns must be arranged in that way that the components of refl are (absolutely) increasing. |
B |
(q,q) lower triangular matrix describing the interrelations of the latent variables: b_ij = 1 regression coefficient of eta_j in the regression relation in which eta_i is the depend variable b_ij = 0 if eta_i does not depend on eta_j in a direct way (b_ii = 0 !) |
indicatorx |
vector describing with which exogenous composite the X-variables are connected |
indicatory |
vector describing with which endogenous composite the Y-variables are connected |
loadingx |
logical TRUE when there are loadings for the X-variables in the model |
loadingy |
logical TRUE when there are loadings for the Y-variables in the model |
maxiter |
Scalar, maximal number of iterations |
biascor |
Boolean, FALSE if no bias correction is done, TRUE if parametric bootstrap bias correction is done. |
out list with components
Bhat | (q,q) lower triangular matrix with the estimated coefficients of the structural model |
What | (n,q) matrix of weights for constructing the composites |
lambdahat | vector of length p with the loadings or 0 |
iter | number of iterations used |
fehl | maximal difference of parameter estimates for the last and second last iteration |
composit | the data matrix of the composites |
resid | the data matrix of the residuals of the structural model |
S | the covariance matrix of the manifest variables |
ziel | sum of squared residuals for the final sum |
fit | The value of the fit criterion |
R2 | vector with the coefficients of determination for all regression equations of the structural model |
1 2 3 4 5 6 7 8 9 10 | data(mobi250)
ind <- c(1, 1, 1, 4, 4, 4, 2, 2, 2, 3, 3, 5, 5, 5, 6, 6, 6, 7, 1, 1, 4, 4, 4, 4)
o <- order(ind)
indicatorx <- c(1,1,1,1,1)
indicatory <- c(1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5)
dat <- mobi250[,o]
dat <- dat[,-ncol(dat)]
B <- matrix(c(0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,
0,1,1,0,0,0,0,1,1,1,0,0,1,0,0,0,1,0),6,6,byrow=TRUE)
out <- gscals(dat,B,indicatorx,indicatory,loadingx=TRUE,loadingy=TRUE,maxiter=200,biascor=FALSE)
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