Eduard Szöcs eduardszoecs

## head.html
<base href="{% if root_url != '' %}{{ root_url }}{% else %}/{% endif %}" />

## gist:5bffbd9face790ac4d55
require(plyr)
require(reshape2)
require(MASS)
require(ggplot2)

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

## gist:77b5f22825a772a8915e
### 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

## plt.R
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)

## derivSimulCI.R
`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

## Deriv.R
################################################
## 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))

## r.ipynb

      
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              November 19, 2016 22:40
            
          
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## predict_10000pkgs_eoda.R

# 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")
            )

## sample random walk

# 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)

## api.R
library(tidyverse)

## Something ------------------------------------
	require(plyr)
	require(reshape2)
	require(MASS)
	require(ggplot2)

	### Power
	### Simulate data
	# no of simulated datasets
	nsims <- 100
	# sample sizes
	### 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
	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)
	`derivSimulCI` <- function(mod, n = 200, eps = 1e-7, newdata, term,
	samples = 10000) {
	stopifnot(require("MASS"))
	if(inherits(mod, "gamm"))
	mod <- mod$gam
	m.terms <- attr(terms(mod), "term.labels")
	if(missing(newdata)) {
	newD <- sapply(model.frame(mod)[, m.terms, drop = FALSE],
	function(x) seq(min(x), max(x) - (2*eps), length = n))
	names(newD) <- m.terms
	################################################
	## Functions for derivatives of GAM(M) models ##
	################################################
	Deriv <- function(mod, n = 200, eps = 1e-7, newdata, term) {
	if(inherits(mod, "gamm"))
	mod <- mod$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))

	# 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")
	)

	# 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)
	library(tidyverse)

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