eduardszoecs

X.1,AGS,Gemeinde,Land,bvlkg_2019,cx,cy,20200310,20200311,20200312,20200313,20200314,20200315,20200316
2,7131000,Ahrweiler,7,129727,7.07017,50.49524,0,0,0,2,2,3,8
3,7132000,Altenkirchen (Westerwald),7,128705,7.839916,50.79483,0,0,0,0,1,1,2
27,7331000,Alzey-Worms,7,129244,8.109496,49.78882,0,0,0,2,2,2,8
28,7332000,Bad Dürkheim,7,132660,8.155013,49.48546,4,4,6,7,8,14,17
4,7133000,Bad Kreuznach,7,158080,7.657812,49.81823,0,0,0,0,0,13,13
13,7231000,Bernkastel-Wittlich,7,112262,7.015531,49.93051,0,0,3,3,4,4,5
5,7134000,Birkenfeld,7,80720,7.281271,49.7158,0,0,0,0,0,0,0
6,7135000,Cochem-Zell,7,61587,7.178459,50.14493,0,0,0,2,3,6,23
29,7333000,Donnersbergkreis,7,75101,7.864106,49.64187,0,0,0,2,2,2,2

eduardszoecs

library(tidyverse)

## Something ------------------------------------ 

eduardszoecs

# plain sample
foo1 <- function(x, n){
  s <- sample(c(-1, 1), n, replace = TRUE)
  min(which(cumsum(s) == 0))
}

# addaptive sample length
foo2 <- function(nmax, step = 10000, start = 0) {
  s <- sample(c(-1, 1), step, replace = TRUE)

eduardszoecs

# get data ----------------------------------------------------------------
library(Ecdat)
data("CRANpackages")
df <- rbind(CRANpackages, 
            data.frame(Version = c("3.2", "3.3") ,
                       Date = as.Date(c("2016-04-27", "2016-12-22")), 
                       Packages = c(8329, 9687),
                       Source = c("Andrie de Vries", "Eduard Szöcs")
            )

eduardszoecs

eduardszoecs

################################################
## Functions for derivatives of GAM(M) models ##
################################################
Deriv <- function(mod, n = 200, eps = 1e-7, newdata, term) {
    if(inherits(mod, "gamm"))
        mod <- mod$gam
    m.terms <- attr(terms(mod), "term.labels")
    if(missing(newdata)) {
        newD <- sapply(model.frame(mod)[, m.terms, drop = FALSE],
                       function(x) seq(min(x), max(x), length = n))

eduardszoecs

`derivSimulCI` <- function(mod, n = 200, eps = 1e-7, newdata, term,
                           samples = 10000) {
    stopifnot(require("MASS"))
    if(inherits(mod, "gamm"))
        mod <- mod$gam
    m.terms <- attr(terms(mod), "term.labels")
    if(missing(newdata)) {
        newD <- sapply(model.frame(mod)[, m.terms, drop = FALSE],
                       function(x) seq(min(x), max(x) - (2*eps), length = n))
        names(newD) <- m.terms

eduardszoecs

plt <- function(df, mod){
  # predict only for range in site
  sites <- levels(df$site)
  value_0p <- c(sapply(sites, function(y)  seq(min(df$value_0[df$site == y]), 
                                               max(df$value_0[df$site == y]), 
                                               length.out = 333)))
  # use the mean for init
  pdat <- data.frame(value_0 = value_0p, 
                     site = rep(sites, each = 333), 
                     init = mean(df$init)

eduardszoecs

### Type1 Error
require(plyr)
require(reshape2)
require(MASS)
require(ggplot2)
nsims <- 100
# sample sizes
N <- c(3,6,9,12)
ctrl <- 2^(1:10)
# both as grid

eduardszoecs

require(plyr)
require(reshape2)
require(MASS)
require(ggplot2)

### Power
### Simulate data
# no of simulated datasets
nsims <- 100
# sample sizes