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Some Fix in plnnetwork (Issue #129 and #130) #131

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merged 2 commits into from
Sep 10, 2024
Merged

Some Fix in plnnetwork (Issue #129 and #130) #131

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d02fe68
optim in PLNnetwork should not start by final PLN cov, but current es…
jchiquet Sep 10, 2024
5ffdf38
optim in PLNnetwork should not start by final PLN cov, but current es…
jchiquet Sep 10, 2024
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1 change: 1 addition & 0 deletions R/PLNnetworkfit-class.R
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Expand Up @@ -60,6 +60,7 @@ PLNnetworkfit <- R6Class(
args <- list(data = data,
params = list(B = private$B, M = private$M, S = private$S),
config = config)
private$Sigma <- crossprod(private$M)/self$n + diag(colMeans(private$S**2), self$p, self$p)
while (!cond) {
iter <- iter + 1
if (config$trace > 1) cat("", iter)
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5 changes: 4 additions & 1 deletion inst/case_studies/oaks_tree.R
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Expand Up @@ -96,7 +96,10 @@ factoextra::fviz_pca_biplot(
labs(col = "distance (cm)") + scale_color_viridis_c()

## Network inference with sparce covariance estimation
system.time(myPLNnets <- PLNnetwork(Abundance ~ 0 + tree + offset(log(Offset)), data = oaks, control = PLNnetwork_param(min_ratio = 0.05)))

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)
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13 changes: 13 additions & 0 deletions inst/check/objective_zipln.R
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@@ -0,0 +1,13 @@
library(PLNmodels)
data(oaks)

myPLN <- PLN(Abundance ~ 0 + tree + offset(log(Offset)), data = oaks)
myZIPLN <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)) | 0 + tree, data = oaks)

## bricolage car quelques points explosent, sans conséquences sur la convergence finale
obj_PLN <- -myPLN$optim_par$objective
plot(obj_PLN, ## optimisation nlopt de la vraisemblance profilée
ylim = c(obj_PLN[1], # fonction objective sans les constantes
tail(obj_PLN,1)), log = "xy")

plot(myZIPLN$optim_par$objective, type = "l", log = "xy") ## Les itérations du VEM: la vraisemblance estimée
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