Bootstrap a PLS path model
Description
Bootstraps a PLS path model in a sempls
object (as returned by
the sempls
method).
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  bootsempls(object, nboot=200, start=c("ones", "old"), method=
c("ConstructLevelChanges", "IndividualSignChanges",
"Standard"), verbose=TRUE, strata, ...)
## S3 method for class 'bootsempls'
print(x, digits=3, ...)
## S3 method for class 'bootsempls'
summary(object, type=c("perc", "bca", "norm", "basic", "none"),
level=0.95, ...)
## S3 method for class 'summary.bootsempls'
print(x, na.print, digits = 3, ...)
## S3 method for class 'bootsempls'
densityplot(x, data, pattern="beta", subset=NULL, ...)
## S3 method for class 'bootsempls'
parallelplot(x, data, pattern="beta", subset=NULL, reflinesAt,
col=c("grey", "darkred", "darkred", "black"), lty=c("solid",
"solid","dashed", "dotted"), ...)

Arguments
object 
An object of class 
nboot 
The number of bootstrap replications; the default is 
start 
A

method 
A

verbose 
A 
x 
An object of class 
na.print 
A 
digits 
Controls the number of digits to print. 
type 
Type of bootstrapped confidence intervals to compute; the
default is 
strata 
An integer vector or factor specifying the strata for
multisample problems. If the argument is not provided, all data is
assumed to come from the same sample. For details, see

level 
Level for confidence intervals; default is 
... 
Arguments to be passed down to other methods. 
data 
The 
pattern 
A regular expression passed on to

subset 
Index or character vector of coefficients to
include. Note, that 
reflinesAt 
A vector of values at which to plot reference lines into the parallel cooordinates. 
col 
Colors for bootstrap statistics, sample statistic, lower and upper bootstrap confidence levels and reference lines. 
lty 
Line type for bootstrap statistics, sample statistic, lower and upper bootstrap confidence levels and reference lines. 
Details
boot.sempls
implements the nonparametric bootstrap, assuming an
independent random sample. Convergence failures in the bootstrap resamples
are discarded (and a warning printed); 10 consecutive convergence failures
result in an error. You can use the boot
function
in the boot package for more complex sampling schemes and additional options.
Value
boot.sempls
returns an object of class bootsempls
, which inherits
from class boot
, supported by the boot
package. The returned
object contains the following components:
t0 
The estimated parameters in the model fit to the original data set. 
t 
a matrix containing the bootstrapped estimates, one bootstrap replication per row. 
data 
The data frame containing the data to which the model was fit. 
seed 
The value of 
statistic 
The function used to produce the bootstrap replications;
this is always the local function 
sim 
Always set to 
stype 
Always set to 
call 
The call of the 
tryErrorIndices 
Contains the indices for each resample, which
returned 
clcIndices 
When the method 
bootIndices 
A matrix containing the indices of the converged bootstrap samples as rows. 
outer_weights 
A martrix containing, as rows, the outer weights for each bootsrap sample. 
fitted_model 
The fitted sempls model returned from 
strata 
The strata used. This is the vector passed to boot, if it was supplied or a vector of ones if there were no strata. 
References
Tenenhaus, M., V. E. Vinzi, Y.M. Chatelin, and C. Lauro (2005) PLS path modeling. Computational Statistics & Data Analysis 48, 159205.
See Also
boot
, boot.sem
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  ## Not run:
data(ECSImobi)
ecsi < sempls(model=ECSImobi, data=mobi)
### Bootstrapping
set.seed(123)
ecsiBoot < bootsempls(ecsi, nboot=200, start="ones", verbose=TRUE)
summary(ecsiBoot, type="perc", level=0.95)
## inspectation of bootstrap samples
parallelplot(ecsiBoot, subset=1:ncol(ecsiBoot$t), reflinesAt=0)
# only inspecting the path coefficients
parallelplot(ecsiBoot, pattern="beta", reflinesAt=c(0,1))
densityplot(ecsiBoot, pattern="beta")
# only inspecting the outer loadings
parallelplot(ecsiBoot, pattern="lam")
# only inspecting the outer loadings for Loyalty
parallelplot(ecsiBoot, pattern="lam7", type="perc", level=0.90,
main="Loyalty\n 200 bootstrapped outer loadings")
## End(Not run)
