View source: R/alpha_div_test.R
glmutli_pq | R Documentation |
See glmulti::glmulti()
for more information.
glmutli_pq(
physeq,
formula,
fitfunction = "lm",
hill_scales = c(0, 1, 2),
aic_step = 2,
confsetsize = 100,
plotty = FALSE,
level = 1,
method = "h",
crit = "aicc",
...
)
physeq |
(required): a |
formula |
(required) a formula for |
fitfunction |
(default "lm") |
hill_scales |
(a vector of integer) The list of q values to compute the hill number H^q. If Null, no hill number are computed. Default value compute the Hill number 0 (Species richness), the Hill number 1 (exponential of Shannon Index) and the Hill number 2 (inverse of Simpson Index). |
aic_step |
The value between AIC scores to cut for. |
confsetsize |
The number of models to be looked for, i.e. the size of the returned confidence set. |
plotty |
(logical) Whether to plot the progress of the IC profile when running. |
level |
If 1, only main effects (terms of order 1) are used to build the candidate set. If 2, pairwise interactions are also used (higher order interactions are currently ignored) |
method |
The method to be used to explore the candidate set of models. If "h" (default) an exhaustive screening is undertaken. If "g" the genetic algorithm is employed (recommended for large candidate sets). If "l", a very fast exhaustive branch-and-bound algorithm is used. Package leaps must then be loaded, and this can only be applied to linear models with covariates and no interactions. If "d", a simple summary of the candidate set is printed, including the number of candidate models. |
crit |
The Information Criterion to be used. Default is the small-sample corrected AIC (aicc). This should be a function that accepts a fitted model as first argument. Other provided functions are the classic AIC, the Bayes IC (bic), and QAIC/QAICc (qaic and qaicc). |
... |
Other arguments passed on to |
This function is mainly a wrapper of the work of others.
Please make a reference to glmulti::glmulti()
if you
use this function.
A data.frame summarizing the glmulti results with columns
-estimates -unconditional_interval -nb_model" -importance -alpha
glmulti::glmulti()
if (requireNamespace("glmulti")) {
res_glmulti <-
glmutli_pq(data_fungi, "Hill_0 ~ Hill_1 + Abundance + Time + Height", level = 1)
res_glmulti
res_glmulti_interaction <-
glmutli_pq(data_fungi, "Hill_0 ~ Abundance + Time + Height", level = 2)
res_glmulti
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.