Nothing
## -----------------------------------------------------------------------------
library(bdlp)
source(system.file("dangl2014.R", package = "bdlp"))
## -----------------------------------------------------------------------------
dangl2014(info=T)
## -----------------------------------------------------------------------------
library(MASS)
meta <- dangl2014(setnr=1)
meta
## -----------------------------------------------------------------------------
library(MASS)
data <- generateData(meta)
head(data)
## -----------------------------------------------------------------------------
meta <- dangl2014(1, seedinfo = list(120, "4.0.3", c("Mersenne-Twister", "Inversion")))
data <- generateData(meta)
head(data)
## ---- fig.width=5, fig.height=5, fig.align='center'---------------------------
meta <- dangl2014(setnr=1)
plotMetadata(meta)
## ----eval=FALSE, message=FALSE, warning=FALSE, include=TRUE, results="hide"----
# generateDatabase(name = system.file("dangl2014.R", package = "bdlp"), setnr = 1, draws = 50)
## -----------------------------------------------------------------------------
dangl2014 <- function(setnr = NULL,
seedinfo = list(100,
paste(R.version$major, R.version$minor, sep = "."),
RNGkind()),
info = FALSE,
metaseedinfo = list(100,
paste(R.version$major, R.version$minor, sep = "."),
RNGkind())){
inf <- data.frame(n = c(50, 40), k = c(2,2), shape = c("spherical", "spherical"))
ref <- "Dangl R. (2014) A small simulation study. Journal of Simple Datasets 10(2), 1-10"
if(info == T) return(list(summary = inf, reference = ref))
if(is.null(metaseedinfo)) metaseedinfo <- seedinfo
set.seed(metaseedinfo[[1]])
RNGversion(metaseedinfo[[2]])
RNGkind(metaseedinfo[[3]][1], metaseedinfo[[3]][2])
if(setnr == 1) {
return(new("metadata.metric",
clusters = list(c1 = list(n = 25, mu = c(4,5), Sigma=diag(1,2)),
c2 = list(n = 25, mu = c(-1,-2), Sigma=diag(1,2))),
genfunc = MASS::mvrnorm, seedinfo = seedinfo))
}
if(setnr == 2){
return(new("metadata.metric",
clusters = list(c1 = list(n = 20, mu = c(0,2), Sigma=diag(1,2)),
c2 = list(n = 20, mu = c(-1,-2), Sigma=diag(1,2))),
genfunc = MASS::mvrnorm, seedinfo = seedinfo))
}
}
## ----eval=FALSE, message=FALSE, warning=FALSE, include=TRUE, results="hide"----
# require(MASS)
# m1 <- initializeObject(type = "metric", genfunc = mvrnorm, k = 2)
# m1@clusters$cl1 <- list(n = 25, mu = c(4,5), Sigma = diag(1,2))
# m1@clusters$cl2 <- list(n = 25, mu = c(-1,-2), Sigma = diag(1,2))
#
# m2 <- initializeObject(type = "metric", genfunc = mvrnorm, k = 2)
# m2@clusters$cl1 <- list(n = 44, mu = c(1,2), Sigma = diag(1,2))
# m2@clusters$cl2 <- list(n = 66, mu = c(-5,-6), Sigma = diag(1,2))
#
# saveSetup(name="miller2012.R", author="John Miller", mail="john.miller@edu.com",
# inst="Example University", cit="Simple Data, pp. 23-24", objects=list(m1, m2),
# table=data.frame(n = c(50, 110), k = c(2,2), shape = c("spherical", "spherical")))
#
# generateDatabase(name = "miller2012.R", setnr = 1, draws = 20)
## -----------------------------------------------------------------------------
Fun1 <- function(x){x^2}
Fun2 <- function(x){sqrt(x)}
Fun3 <- function(x){sin(2*pi*x)}
functions <- list(Fun1 = Fun1, Fun2 = Fun2, Fun3 = Fun3)
interval <- c(0,1)
gridPoints <- 30
sd <- 0.2
n <- 100
minTimePoints <- 5
maxTimePoints <- 10
regular <- FALSE
grid <- sampleGrid(n, minTimePoints, maxTimePoints, gridPoints, regular)
meta <- new("metadata.functional", functions = functions,
gridMatrix = grid,
sd=sd,
sd_distribution="rnorm",
interval = interval,
resolution=gridPoints,
total_n = n,
minTimePoints = minTimePoints,
maxTimePoints = maxTimePoints,
regular=F)
data <- generateData(meta)
head(data)
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