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source("install_and_load_INLA.R")
#parameters
kappa = 1
phi_1 = c(1, 1.25) # "Local" version: (sigma, phi) First value is not used
WALS <- read_csv("data/complexity_data_WALS.csv") %>%
dplyr::select("Name" = lang, roundComp, logpop2, "ISO_639" = silCode) %>%
dplyr::mutate(ISO_639 = str_to_lower(ISO_639))
min_val <- min(WALS$roundComp)
max_val <- max(WALS$roundComp)
# Perform the rescaling
WALS$roundComp <- (WALS$roundComp - min_val) / (max_val - min_val)
pop_file_fn <-
"data_wrangling/ethnologue_pop_SM_morph_compl_reanalysis.tsv"
L1 <-
read_tsv(pop_file_fn, show_col_types = F) %>% dplyr::select(ISO_639, L1_log10_scaled)
glottolog_df <-
read_tsv("data_wrangling/glottolog_cldf_wide_df.tsv", col_types = cols()) %>%
dplyr::select(
Glottocode,
Language_ID,
"ISO_639" = ISO639P3code,
Language_level_ID,
level,
Family_ID,
Longitude,
Latitude
) %>%
mutate(Language_level_ID = if_else(is.na(Language_level_ID), Glottocode, Language_level_ID)) %>%
mutate(Family_ID = ifelse(is.na(Family_ID), Language_level_ID, Family_ID)) %>%
dplyr::select(
Glottocode,
Language_ID,
ISO_639,
Language_level_ID,
level,
Family_ID,
Longitude,
Latitude
)
WALS_df <- WALS %>%
inner_join(L1,
by = c("ISO_639")) %>%
inner_join(glottolog_df, by = "ISO_639") %>%
filter(!is.na(Latitude), !is.na(Longitude)) %>%
dplyr::select(Language_ID = Glottocode,
Name,
roundComp,
ISO_639,
L1_log10_scaled,
Longitude,
Latitude)
tree <- read.tree(file.path("data_wrangling/wrangled.tree"))
#dropping tips not in Grambank
WALS_df <- WALS_df[WALS_df$Language_ID %in% tree$tip.label,]
tree <- keep.tip(tree, WALS_df$Language_ID)
x <-
assert_that(all(tree$tip.label %in% WALS_df$Language_ID), msg = "The data and phylogeny taxa do not match")
## Building standardized phylogenetic precision matrix
tree_scaled <- tree
tree_vcv = vcv.phylo(tree_scaled)
typical_phylogenetic_variance = exp(mean(log(diag(tree_vcv))))
#We opt for a sparse phylogenetic precision matrix (i.e. it is quantified using all nodes and tips), since sparse matrices make the analysis in INLA less time-intensive
tree_scaled$edge.length <-
tree_scaled$edge.length / typical_phylogenetic_variance
phylo_prec_mat <- MCMCglmm::inverseA(tree_scaled,
nodes = "ALL",
scale = FALSE)$Ainv
WALS_df = WALS_df[order(match(WALS_df$Language_ID, rownames(phylo_prec_mat))), ]
#"local" set of parameters
## Create spatial covariance matrix using the matern covariance function
spatial_covar_mat_1 = varcov.spatial(WALS_df[, c("Longitude", "Latitude")],
cov.pars = phi_1, kappa = kappa)$varcov
# Calculate and standardize by the typical variance
typical_variance_spatial_1 = exp(mean(log(diag(
spatial_covar_mat_1
))))
spatial_cov_std_1 = spatial_covar_mat_1 / typical_variance_spatial_1
spatial_prec_mat_1 = solve(spatial_cov_std_1)
dimnames(spatial_prec_mat_1) = list(WALS_df$Language_ID, WALS_df$Language_ID)
## Since we are using a sparse phylogenetic matrix, we are matching taxa to rows in the matrix
phy_id = match(tree$tip.label, rownames(phylo_prec_mat))
WALS_df$phy_id = phy_id
## Other effects are in the same order they appear in the dataset
WALS_df$sp_id = 1:nrow(spatial_prec_mat_1)
formula <- as.formula(
paste(
"roundComp ~",
"L1_log10_scaled +",
"f(phy_id, model = 'generic0', Cmatrix = phylo_prec_mat,
constr = TRUE, hyper = pcprior_hyper) +
f(sp_id, model = 'generic0', Cmatrix = spatial_prec_mat_1,
constr = TRUE, hyper = pcprior_hyper)"
)
)
result <- inla(
formula,
family = "gaussian",
data = WALS_df,
control.compute = list(waic = TRUE)
)
summary(result)
save(result, file = "output_models/model_WALS_controlled.RData")
social_effects_controlled <-
c("morphological complexity ~ L1 + phylogenetic effect + spatial effect",
round(
c(
result$summary.fixed[2,]$`0.025quant`,
result$summary.fixed[2,]$`0.5quant`,
result$summary.fixed[2,]$`0.975quant`,
nrow(WALS_df)
),
2
), "default (~10%)")
save(social_effects_controlled, file = "output_models/social_effects_controlled.RData")
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