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Zipln formula #136

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Merged
merged 2 commits into from
Sep 11, 2024
Merged

Zipln formula #136

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1e3b8e5
added example for oaks with ZIPLN
jchiquet Sep 10, 2024
ba5cf32
fix for #126
jchiquet Sep 10, 2024
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4 changes: 2 additions & 2 deletions R/utils-zipln.R
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Original file line number Diff line number Diff line change
Expand Up @@ -27,15 +27,15 @@ extract_model_zi <- function(call, envir) {
call_args <- c(as.list(call_args), list(xlev = attr(call$formula, "xlevels"), na.action = NULL))

## Extract terms for ZI and PLN components
terms <- .extract_terms_zi(as.formula(call$formula, env = envir))
terms <- .extract_terms_zi(as.formula(eval(call$formula, env = envir)))
## eval the call in the parent environment with adjustement due to ZI terms
call_args$formula <- terms$formula
frame <- do.call(stats::model.frame, call_args, envir = envir)

## Save level for predict function
xlevels <- list(PLN = .getXlevels(terms$PLN, frame))
if (!is.null(terms$ZI)) xlevels$ZI = .getXlevels(terms$ZI, frame)
attr(call$formula, "xlevels") <- xlevels
if (!is.null(xlevels$PLN)) attr(call$formula, "xlevels") <- xlevels

## Create the set of matrices to fit the PLN model
X <- model.matrix(terms$PLN, frame, xlev = xlevels$PLN)
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6 changes: 4 additions & 2 deletions inst/case_studies/oaks_tree.R
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Expand Up @@ -97,14 +97,16 @@ factoextra::fviz_pca_biplot(

## Network inference with sparce covariance estimation

system.time(myZIPLNnets <- ZIPLNnetwork(Abundance ~ 0 + tree + offset(log(Offset)), zi = "single", data = oaks, control = ZIPLNnetwork_param(min_ratio = 0.1)))

system.time(myPLNnets <- PLNnetwork(Abundance ~ 0 + tree + offset(log(Offset)), data = oaks, control = PLNnetwork_param(min_ratio = 0.1)))
plot(myPLNnets)
plot(getBestModel(myPLNnets, "EBIC"))
# stability_selection(myPLNnets)
# plot(getBestModel(myPLNnets, "StARS", stability = .975))

system.time(myZIPLNnets <- ZIPLNnetwork(Abundance ~ 0 + tree + offset(log(Offset)), zi = "single", data = oaks, control = ZIPLNnetwork_param(min_ratio = 0.1)))
plot(myZIPLNnets)
plot(getBestModel(myZIPLNnets, "EBIC"))

## Mixture model to recover tree structure
system.time(my_mixtures <- PLNmixture(Abundance ~ 1 + offset(log(Offset)), data = oaks, clusters = 1:5))

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