vankesteren

library(deldir)
library(ggplot2)
library(firatheme)

N <- 40
dat <- matrix(rnorm(N*2), N)
dat <- dat  %*% chol(matrix(c(1, 0, 0, 1.5), 2)) %*% chol(solve(cov(dat)))
dat2 <- deldir(dat[,1], dat[,2])
dat1 <- data.frame(dat)
colnames(dat1) <- c("x1", "y1")

vankesteren

library(sf)
library(httr) 
library(tidyverse) 


url <- parse_url("https://geodata.nationaalgeoregister.nl/bag/wfs/v1_1")

postcode_filter <- function(postcode, gebruiksdoel = "woonfunctie") {
  sprintf(
  "<Filter>

vankesteren

library(tidyverse)
library(sf)
library(osmdata)
library(osmenrich)
library(patchwork)

# conditional IDW functions

conditional_idw <- function(formula, data, p = 1, max.iter = 100, tol = 1e-10) {
  

vankesteren

rtrunc <- function(rdist, min, max) {
  # deal with pipe and also with normal evaluation
  # https://github.com/tidyverse/magrittr/issues/115#issuecomment-173894787
  parents <- lapply(sys.frames(), parent.env)
  is_magrittr_env <- vapply(parents, identical, logical(1), y = environment(`%>%`))
  if (any(is_magrittr_env)) {
    distcall <- get("lhs", sys.frames()[[base::max(which(is_magrittr_env))]])
  } else {
    distcall <- substitute(rdist)
  }

vankesteren

# Gierzwaluw (common swift) analysis using osmenrich
# Last edited 2021-02-09 by @vankesteren
# CC-BY ODISSEI SoDa team

# Packages
# Data
library(tidyverse)
library(sf)
library(osmenrich)

vankesteren

# Meuse universal kriging for a large grid 
# The grid is enriched with osm data
# CC-BY @vankesteren

# load packages ----
library(tidyverse)
library(sf)
library(ggspatial)
library(osmdata)
library(gstat)

vankesteren

# Bayesian test for partial intransitivity using monte-carlo posterior approximation
# (c) Erik-Jan van Kesteren, 2021
# Based on an e-mail discussion with Tony Marley
library(expm)
library(tidyverse)

# Beta prior (1, 1 means uniform)
prior_a <- 1
prior_b <- 1

vankesteren

kmedoids <- function(X, K, max_iter = 100) {
  X <- as.matrix(X)
  N <- nrow(X)
  P <- ncol(X)
  C <- matrix(0, nrow = K, ncol = P)
  clus <- sample(K, N, replace = TRUE)
  converged <- FALSE
  iter <- 0
  while (!converged && iter < max_iter) {
    old_clus <- clus

vankesteren

# gaussian mixture modeling with EM
priorp <- 0.6
m1 <- 0
m2 <- 2
s1 <- 1
s2 <- 0.707

# generate some data with 2 classes
N <- 1000
cl <- rbinom(N, 1, priorp)

vankesteren

# lavPredict from tensorsem parameters
library(lavaan)
library(tensorsem)

mod <- "
  # three-factor model
  visual  =~ x1 + x2 + x3
  textual =~ x4 + x5 + x6
  speed   =~ x7 + x8 + x9
"