Description Usage Arguments Value Examples
plspath
estimates pls path models using the classical approach formulated in Lohmueller.
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 b_ij= 0 if eta_i does not depend on eta_j in a direct way (b_ii = 0 !) |
indicatorx |
(p1,1) vector indicating with which exogenous composite the x-indicators are related. |
indicatory |
(p2,1) vector indicating with which endogenous composite the y-indicators are related. The components of the indicators must be increasing. |
modex |
equals "A" or "B" , the mode for this block of indicators |
modey |
equals "A" or "B" , the mode for this block of indicators |
maxiter |
Scalar, maximal number of iterations |
stdev |
Boolean Should the standard deviations of the estimates be computed by bootstrap? |
out list wih components
Bhat | (q,q) lower triangular matrix with the estimated coefficients of the structural model |
eta | (n,q)-matrix, the scores of the latent variables |
w | vector of length p of weights for constructing the latent variables |
lambdahat | vector of length p with the loadings |
resa | (n,?) matrix of residuals from outer model |
resi | (n,?) matrix of residuals from inner model |
R2 | vector with the coefficients of determination for all regression equations of the structural model |
iter | number of iterations used |
ret | scalar, return code: |
0 normal convergence | |
1 limit of iterations attained, probably without convergence | |
sdev.beta | (q,q) matrix, the standard deviations of path coefficients (when stdev = TRUE) |
sdev.lambda | vector, the standard deviations of loadings (when stdev = TRUE) |
1 2 3 4 5 6 7 8 9 10 11 | data(mobi250)
refl <- 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(refl)
dat <- mobi250[,o]
dat <- dat[,-ncol(dat)]
refl <- refl[o][-length(refl)]
indicatorx <- refl[1:5]
indicatory <- refl[-c(1:5)] - 1
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 <- plspath(dat,B,indicatorx,indicatory,modex="A",modey="A")
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