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semPLS

bootsempls

Bootstrap a PLS path model

Description

Bootstraps a PLS path model in a sempls object (as returned by the sempls method).

Usage

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 `sempls` as returned by the method `sempls`.
`nboot`	The number of bootstrap replications; the default is `200`.
`start`	A `character` value defining the initialisation of outer weights. If `start="ones"`, then the outer weights for each block are initialised by ones. `start="old"`, the final outer weights taken from the `sempls` object are used for initialisation.
`method`	A `character` value, which can take the values: `"ConstructLevelChanges"` (default): The vector of loadings for each LV in each resample is compared to the corresponding vector of loadings in the original sample. The signs of the weights, and consequently the signs of the loadings, are reversed if the absolute value of the sum differences between estimated loadings obtained from the original sample and the estimated loadings obtained from the resample is bigger than the absolute value of the sum sums of the latter, see Tenenhaus et al (2005). `"IndividualSignChanges"`: not implemented yet. `"Standard"`: No compensation for sign changes of resampled statistics.
`verbose`	A `logical` indicating, whether progress of bootstrap shall be displayed.
`x`	An object of class `bootsempls` and `summary.bootsempls` respectively.
`na.print`	A `character` substituting values not to be printed.
`digits`	Controls the number of digits to print.
`type`	Type of bootstrapped confidence intervals to compute; the default is `"perc"` (percentile); see `boot.ci` for details.
`strata`	An integer vector or factor specifying the strata for multi-sample problems. If the argument is not provided, all data is assumed to come from the same sample. For details, see `boot.ci`.
`level`	Level for confidence intervals; default is `0.95`.
`...`	Arguments to be passed down to other methods.
`data`	The `data` is not used because the `bootsempls` object already contains the required data.
`pattern`	A regular expression passed on to `grep`. It is helpful to easily identify a set of coefficients, e.g. `pattern="beta"` plots the path coefficients only.
`subset`	Index or character vector of coefficients to include. Note, that `subset` overrides `pattern`.
`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 `.Random.seed` when `boot.sempls` was called.
`statistic`	The function used to produce the bootstrap replications; this is always the local function `refit` from `boot.sempls`.
`sim`	Always set to `"ordinary"`; see the documentation for the `boot` function.
`stype`	Always set to `"i"`; see the documentation for the `boot` function.
`call`	The call of the `boot.sem` function.
`tryErrorIndices`	Contains the indices for each resample, which returned `try-error`.
`clcIndices`	When the method `ConstructLevelChanges` is used, it stores the indices of the blocks of MVs for which a sign change occured.
`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 `sempls`.
`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, 159-205.

Examples

## 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)

semPLS

Structural Equation Modeling Using Partial Least Squares

v1.0-10

GPL-2

Authors

Armin Monecke <armin.monecke@stat.uni-muenchen.de>

Initial release

2013-01-21

bootsempls

Description

Usage

Arguments

Details

Value

References

See Also

Examples

semPLS

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