Nothing
## ---- message=FALSE, warning=FALSE--------------------------------------------
library(MRFcov)
data("Bird.parasites")
## ----message=F, warning=FALSE, eval = FALSE-----------------------------------
# #Not run
# #install.packages(dplyr)
# data.paras = data.frame(data.paras) %>%
# dplyr::group_by(Capturesession,Genus) %>%
# dplyr::summarise(count = dlyr::n()) %>%
# dplyr::mutate(prop.zos = count / sum(count)) %>%
# dplyr::left_join(data.paras) %>%
# dplyr::ungroup() %>% dplyr::filter(Genus == 'Zosterops') %>%
# dplyr::mutate(scale.prop.zos = as.vector(scale(prop.zos)))
# data.paras <- data.paras[, c(12:15, 23)]
## ----eval=FALSE---------------------------------------------------------------
# help("Bird.parasites")
# View(Bird.parasites)
## -----------------------------------------------------------------------------
MRF_fit <- MRFcov(data = Bird.parasites[, c(1:4)], n_nodes = 4, family = 'binomial')
## -----------------------------------------------------------------------------
plotMRF_hm(MRF_mod = MRF_fit, main = 'MRF (no covariates)',
node_names = c('H. zosteropis', 'H. killangoi',
'Plasmodium', 'Microfilaria'))
## -----------------------------------------------------------------------------
net <- igraph::graph.adjacency(MRF_fit$graph, weighted = T, mode = "undirected")
igraph::plot.igraph(net, layout = igraph::layout.circle,
edge.width = abs(igraph::E(net)$weight),
edge.color = ifelse(igraph::E(net)$weight < 0,
'blue',
'red'))
## -----------------------------------------------------------------------------
MRF_mod <- MRFcov(data = Bird.parasites, n_nodes = 4, family = 'binomial')
## -----------------------------------------------------------------------------
plotMRF_hm(MRF_mod = MRF_mod)
## -----------------------------------------------------------------------------
MRF_mod$key_coefs$Hzosteropis
## -----------------------------------------------------------------------------
fake.dat <- Bird.parasites
fake.dat$Microfilaria <- rbinom(nrow(Bird.parasites), 1, 0.8)
fake.preds <- predict_MRF(data = fake.dat, MRF_mod = MRF_mod)
## -----------------------------------------------------------------------------
H.zos.pred.prev <- sum(fake.preds$Binary_predictions[, 'Hzosteropis']) / nrow(fake.preds$Binary_predictions)
Plas.pred.prev <- sum(fake.preds$Binary_predictions[, 'Plas']) / nrow(fake.preds$Binary_predictions)
Plas.pred.prev
## -----------------------------------------------------------------------------
mod_fits <- cv_MRF_diag_rep(data = Bird.parasites, n_nodes = 4,
n_cores = 1, family = 'binomial', plot = F,
compare_null = T,
n_folds = 10)
# CRF (with covariates) model sensitivity
quantile(mod_fits$mean_sensitivity[mod_fits$model == 'CRF'], probs = c(0.05, 0.95))
# MRF (no covariates) model sensitivity
quantile(mod_fits$mean_sensitivity[mod_fits$model != 'CRF'], probs = c(0.05, 0.95))
## -----------------------------------------------------------------------------
booted_MRF <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4, family = 'binomial', n_bootstraps = 10, n_cores = 1, sample_prop = 0.9)
## -----------------------------------------------------------------------------
booted_MRF$mean_key_coefs$Hzosteropis
## -----------------------------------------------------------------------------
booted_MRF$mean_key_coefs$Hkillangoi
## -----------------------------------------------------------------------------
booted_MRF$mean_key_coefs$Plas
## -----------------------------------------------------------------------------
booted_MRF$mean_key_coefs$Microfilaria
## -----------------------------------------------------------------------------
adj_mats <- predict_MRFnetworks(data = Bird.parasites,
MRF_mod = booted_MRF,
metric = 'eigencentrality',
cutoff = 0.33)
colnames(adj_mats) <- colnames(Bird.parasites[, 1:4])
apply(adj_mats, 2, summary)
## ----eval = FALSE-------------------------------------------------------------
# Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE)
# Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE)
# coords <- data.frame(Latitude = Latitude, Longitude = Longitude)
## ----eval = FALSE-------------------------------------------------------------
# CRFmod_spatial <- MRFcov_spatial(data = Bird.parasites, n_nodes = 4,
# family = 'binomial', coords = coords)
## ----eval = FALSE-------------------------------------------------------------
# CRFmod_spatial$key_coefs$Hzosteropis
## ----eval = FALSE-------------------------------------------------------------
# cv_MRF_diag_rep_spatial(data = Bird.parasites, n_nodes = 4,
# n_cores = 3, family = 'binomial', plot = T, compare_null = T,
# coords = coords)
## ---- message=FALSE, warning=FALSE--------------------------------------------
library(MRFcov)
data("Bird.parasites")
## ----message=F, warning=FALSE, eval = FALSE-----------------------------------
# #Not run
# #install.packages(dplyr)
# data.paras = data.frame(data.paras) %>%
# dplyr::group_by(Capturesession,Genus) %>%
# dplyr::summarise(count = dlyr::n()) %>%
# dplyr::mutate(prop.zos = count / sum(count)) %>%
# dplyr::left_join(data.paras) %>%
# dplyr::ungroup() %>% dplyr::filter(Genus == 'Zosterops') %>%
# dplyr::mutate(scale.prop.zos = as.vector(scale(prop.zos)))
# data.paras <- data.paras[, c(12:15, 23)]
## ----eval=FALSE---------------------------------------------------------------
# help("Bird.parasites")
# View(Bird.parasites)
## -----------------------------------------------------------------------------
MRF_fit <- MRFcov(data = Bird.parasites[, c(1:4)], n_nodes = 4, family = 'binomial')
## -----------------------------------------------------------------------------
plotMRF_hm(MRF_mod = MRF_fit, main = 'MRF (no covariates)',
node_names = c('H. zosteropis', 'H. killangoi',
'Plasmodium', 'Microfilaria'))
## -----------------------------------------------------------------------------
net <- igraph::graph.adjacency(MRF_fit$graph, weighted = T, mode = "undirected")
igraph::plot.igraph(net, layout = igraph::layout.circle,
edge.width = abs(igraph::E(net)$weight),
edge.color = ifelse(igraph::E(net)$weight < 0,
'blue',
'red'))
## -----------------------------------------------------------------------------
MRF_mod <- MRFcov(data = Bird.parasites, n_nodes = 4, family = 'binomial')
## -----------------------------------------------------------------------------
plotMRF_hm(MRF_mod = MRF_mod)
## -----------------------------------------------------------------------------
MRF_mod$key_coefs$Hzosteropis
## -----------------------------------------------------------------------------
fake.dat <- Bird.parasites
fake.dat$Microfilaria <- rbinom(nrow(Bird.parasites), 1, 0.8)
fake.preds <- predict_MRF(data = fake.dat, MRF_mod = MRF_mod)
## -----------------------------------------------------------------------------
H.zos.pred.prev <- sum(fake.preds$Binary_predictions[, 'Hzosteropis']) / nrow(fake.preds$Binary_predictions)
Plas.pred.prev <- sum(fake.preds$Binary_predictions[, 'Plas']) / nrow(fake.preds$Binary_predictions)
Plas.pred.prev
## -----------------------------------------------------------------------------
mod_fits <- cv_MRF_diag_rep(data = Bird.parasites, n_nodes = 4,
n_cores = 1, family = 'binomial', plot = F,
compare_null = T,
n_folds = 10)
# CRF (with covariates) model sensitivity
quantile(mod_fits$mean_sensitivity[mod_fits$model == 'CRF'], probs = c(0.05, 0.95))
# MRF (no covariates) model sensitivity
quantile(mod_fits$mean_sensitivity[mod_fits$model != 'CRF'], probs = c(0.05, 0.95))
## -----------------------------------------------------------------------------
booted_MRF <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4, family = 'binomial', n_bootstraps = 10, n_cores = 1, sample_prop = 0.9)
## -----------------------------------------------------------------------------
booted_MRF$mean_key_coefs$Hzosteropis
## -----------------------------------------------------------------------------
booted_MRF$mean_key_coefs$Hkillangoi
## -----------------------------------------------------------------------------
booted_MRF$mean_key_coefs$Plas
## -----------------------------------------------------------------------------
booted_MRF$mean_key_coefs$Microfilaria
## -----------------------------------------------------------------------------
adj_mats <- predict_MRFnetworks(data = Bird.parasites,
MRF_mod = booted_MRF,
metric = 'eigencentrality',
cutoff = 0.33)
colnames(adj_mats) <- colnames(Bird.parasites[, 1:4])
apply(adj_mats, 2, summary)
## ----eval = FALSE-------------------------------------------------------------
# Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE)
# Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE)
# coords <- data.frame(Latitude = Latitude, Longitude = Longitude)
## ----eval = FALSE-------------------------------------------------------------
# CRFmod_spatial <- MRFcov_spatial(data = Bird.parasites, n_nodes = 4,
# family = 'binomial', coords = coords)
## ----eval = FALSE-------------------------------------------------------------
# CRFmod_spatial$key_coefs$Hzosteropis
## ----eval = FALSE-------------------------------------------------------------
# cv_MRF_diag_rep_spatial(data = Bird.parasites, n_nodes = 4,
# n_cores = 3, family = 'binomial', plot = T, compare_null = T,
# coords = coords)
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