Description Usage Arguments Details Value See Also Examples

Uses the distribution of a bootstrapped RAM model's raw parameters to create a bootstrapped estimate of its standardized path coefficients.

*note*: Model must have already been run through `mxBootstrap`

.

1 2 | ```
mxBootstrapStdizeRAMpaths(model, bq= c(.25, .75),
method= c('bcbci','quantile'), returnRaw= FALSE)
``` |

`model` |
An MxModel that uses RAM expectation and has already been run through |

`bq` |
vector of 2 bootstrap quantiles corresponding to the lower and upper limits of the desired confidence interval. |

`method` |
One of 'bcbci' or 'quantile'. |

`returnRaw` |
Whether or not to return the raw bootstrapping results (Defaults to |

`mxBootstrapStdizeRAMpaths`

applies `mxStandardizeRAMpaths`

to each bootstrap replication, thus creating a distribution of standardized estimates for each nonzero path coefficient.

The default `bq`

(bootstrap quantiles) of c(.25, .75) correspond to a 50% CI. This default is chosen as many more
bootstraps are required to accurately estimate more extreme quantiles. For a 95% CI, use `bq=c(.025,.0975)`

.

*nb*: ‘bcbci’ stands for ‘bias-corrected bootstrap confidence interval’

*note 1*: It is possible (though unlikely) that the number of nonzero paths (elements of the `A`

and `S`

RAM matrices) may vary among bootstrap replications. This precludes a simple summary of the standardized paths' bootstrapping results. In this rare case, if `returnRaw=TRUE`

, a raw list of bootstrapping results is returned, with a warning. Otherwise an error is thrown.

*note 2*: `mxBootstrapStdizeRAMpaths`

ignores sub-models. To standardize bootstrapped sub-models, run it on the sub-models directly.

If `returnRaw=FALSE`

(default), it returns a dataframe containing, among other things, the standardized path coefficients as estimated from the real data, their bootstrap SEs, and the lower and upper limits of a bootstrap confidence interval. If `returnRaw=TRUE`

, typically, a matrix containing the raw bootstrap results is returned; this matrix has one column per non-zero path coefficient, and one row for each successfully converged bootstrap replication or, if the number of paths varies between bootstraps, a raw list of results is returned.

`mxBootstrap()`

, `mxStandardizeRAMpaths()`

, `mxBootstrapEval`

, `mxSummary`

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ```
require(OpenMx)
data(myFADataRaw)
manifests = c("x1","x2","x3","x4","x5","x6")
# Build and run 1-factor raw-data CFA
m1 = mxModel("CFA", type="RAM", manifestVars=manifests, latentVars="F1",
# Factor loadings
mxPath("F1", to = manifests, values=1),
# Means and variances of F1 and manifests
mxPath(from="F1", arrows=2, free=FALSE, values=1), # fix var F1 @1
mxPath("one", to= "F1", free= FALSE, values = 0), # fix mean F1 @0
# Freely-estimate means and residual variances of manifests
mxPath(from = manifests, arrows=2, free=TRUE, values=1),
mxPath("one", to= manifests, values = 1),
mxData(myFADataRaw, type="raw")
)
m1 = mxRun(m1)
set.seed(170505) # Desirable for reproducibility
# ==========================
# = 1. Bootstrap the model =
# ==========================
m1_booted = mxBootstrap(m1)
# =================================================
# = 2. Estimate and accumulate a distribution of =
# = standardized values from each bootstrap. =
# =================================================
tmp = mxBootstrapStdizeRAMpaths(m1)
# tmp
# name Estimate SE
# 1 ind60_to_x1 1.0000000 0.00000000
# 2 ind60_to_x2 2.1803678 0.13901100
# 3 ind60_to_x3 1.8185115 0.15219019
# 4 ind60_to_dem60 1.4830002 0.39729395
# ...
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.