Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/sem.missing.paths.R

Identifies missing paths from a piecewise SEM, fits models, extracts path p-values and returns in a `data.frame`

.

1 2 3 |

`modelList` |
a |

`data` |
a |

`conditional` |
whether conditional variables should be shown in the independence claim (unless the formula is fewer than 30 characters). Default is |

`corr.errors` |
a vector of variables with correlated errors (separated by "~~"). |

`add.vars` |
a vector of additional variables whose independence claims should be evaluated, but which do not appear in the model list. |

`grouping.vars` |
an optional variable that represents the levels of data aggregation for a multi-level dataset. |

`grouping.fun` |
a function defining how variables are aggregated in |

`adjust.p` |
whether p-values degrees of freedom should be adjusted (see below). Default is |

`basis.set` |
provide an optional basis set. |

`model.control` |
a |

`.progressBar` |
enable optional text progress bar. Default is |

This function takes a model list (and optional basis set) and evaluates all conditional independence claims by constructing regressions, returning the claims, the variables upon which they are conditional, and associated p-values in a `data.frame`

.

Returns a `data.frame`

where the first column is the independence claim (with the first variable being the variable of interest, followed by the conditional variables, unless truncated), and the second through sixth columns the model estimates corresponding to the response variable in the independence claim.

Independence claims are constructed based on how the variables are treated as in the model list. For example, if the indepedence claim includes a binary variable that is fit to a binomial distribution using an identity link, the function will evaluate the any claims using the same parameters.

Similarly, for linear mixed effects models construted in `lme4`

or `nlme`

, varying slopes and intercepts are treated as in the model list. For example, if a variable is modeled with both a random slope and intercept in any model in the model list, that variable will be modeled with a random slope and intercept when evaluating all independence claims in which it appears. If slopes and intercepts vary for multiple variables, they will appear as such, even if they are conditional.

For models of class `lmerMod`

, denominator degrees of freedom and resulting P-values are calculated using the Kenward-Rogers approximation from the `pbkrtest`

package.

For linear mixed effects models, p-values can be adjusted to accommodate the full model degrees of freedom using the argument `p.adjust = TRUE`

. For more information, see Shipley 2013.

Jon Lefcheck

Shipley, Bill. "Confirmatory path analysis in a generalized multilevel context." Ecology 90.2 (2009): 363-368.

Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a d-separation test." Ecology 94.3 (2013): 560-564.

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 43 44 45 46 47 48 49 50 51 52 53 54 | ```
# Load example data
data(shipley2009)
# Reduce dataset for example
shipley2009.reduced = shipley2009[1:200, ]
# Load model packages
library(lme4)
library(nlme)
# Create list of models
shipley2009.reduced.modlist = list(
lme(DD ~ lat, random = ~1|site/tree, na.action = na.omit,
data = shipley2009.reduced),
lme(Date ~ DD, random = ~1|site/tree, na.action = na.omit,
data = shipley2009.reduced),
lme(Growth ~ Date, random = ~1|site/tree, na.action = na.omit,
data = shipley2009.reduced),
glmer(Live ~ Growth+(1|site)+(1|tree),
family=binomial(link = "logit"), data = shipley2009.reduced)
)
# Evaluate independence claims
sem.missing.paths(shipley2009.reduced.modlist, shipley2009.reduced)
## Not run:
# Repeat with full dataset as in Shipley (2009)
# Create list of models
shipley2009.modlist = list(
lme(DD ~ lat, random = ~1|site/tree, na.action = na.omit,
data = shipley2009),
lme(Date ~ DD, random = ~1|site/tree, na.action = na.omit,
data = shipley2009),
lme(Growth ~ Date, random = ~1|site/tree, na.action = na.omit,
data = shipley2009),
glmer(Live ~ Growth+(1|site)+(1|tree),
family=binomial(link = "logit"), data = shipley2009)
)
# Evaluate independence claims
sem.missing.paths(shipley2009.modlist, shipley2009)
## End(Not run)
``` |

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.