Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://cran.rstudio.com/web/packages/Rcpp/../RcppDynProg/../Rcpp/../stpm/readme/README.html below:

README

• Continuous (one- and multiple-dimensions)
• Discrete (one- and multiple-dimensions)

• Continuous (one- and multiple-dimensions)
• Discrete (one- and multiple-dimensions)
• Time-dependant coefficients (one-dimensional optimisation)

Note: for Windows, please install Rtools: https://cran.r-project.org/bin/windows/Rtools/ Note: compilation under Windows may fail if you run Windows on a virtual machine! Then:

install.packages("devtools")
library(devtools)
install_github("izhbannikov/spm")

library(stpm)
## Test discrete
data <- simdata_discr(N=1000)
pars <- spm_discrete(data)
pars

library(stpm)
# Setting up the model
model.par <- list()
model.par$a <- matrix(c(-0.05, 1e-3, 2e-3, -0.05), nrow=2, ncol=2, byrow=TRUE)
model.par$f1 <- matrix(c(90, 35), nrow=1, ncol=2)
model.par$Q <- matrix(c(1e-8, 1e-9, 1e-9, 1e-8), nrow=2, ncol=2, byrow=TRUE)
model.par$f <- matrix(c(80, 27), nrow=1, ncol=2)
model.par$b <- matrix(c(6, 2), nrow=2, ncol=2)
model.par$mu0 <- 1e-5
model.par$theta <- 0.11
# Projection
# Discrete-time model
data.proj.discrete <- spm_projection(model.par, N=100, ystart=c(80, 27), tstart=c(30, 60))
plot(data.proj.discrete$stat$srv.prob, xlim = c(30, 115))
# Continuous-time model
data.proj.continuous <- spm_projection(model.par, N=100, ystart=c(80, 27), model="continuous", gomp=TRUE)
plot(data.proj.continuous$stat$srv.prob, xlim = c(30, 115))

library(stpm)
model.par <- list(at = "-0.05", f1t = "80", Qt = "2e-8", ft= "80", bt = "5", mu0t = "1e-5*exp(0.11*t)")
data.proj.time_dependent <- spm_projection(model.par, N=100, ystart=80, model="time-dependent")
plot(data.proj.time_dependent$stat$srv.prob, xlim = c(30,105))

library(stpm)
data <- prepare_data(x=system.file("data","longdat.csv",package="stpm"))
head(data[[1]])
head(data[[2]])

library(stpm)
dat <- sim_pobs(N=500)
head(dat)

library(stpm)
#Reading the data:
data <- sim_pobs(N=100)
head(data)
#Parameters estimation:
pars <- spm_pobs(x=data)
pars

library(stpm)
dat <- simdata_cont(N=50)
head(dat)

library(stpm)
data <- simdata_discr(N=100)
head(data)

library(stpm)
dat <- simdata_time_dep(N=100)
head(dat)

library(stpm)
data <- simdata_cont(N=50)
pars <- spm_continuous(dat=data,a=-0.05, f1=80, Q=2e-8, f=80, b=5, mu0=2e-5)
pars

library(stpm)
data <- simdata_discr(N=10)
pars <- spm_discrete(data)
pars

library(stpm)
data.continuous <- simdata_cont(N=1000)
data.discrete <- simdata_discr(N=1000)
data <- list(data.continuous, data.discrete)
p.discr.model <- spm(data)
p.discr.model
p.cont.model <- spm(data, model="continuous")
p.cont.model
p.td.model <- spm(data, model="time-dependent",formulas=list(at="aa*t+bb", f1t="f1", Qt="Q", ft="f", bt="b", mu0t="mu0"), start=list(a=-0.001, bb=0.05, f1=80, Q=2e-8, f=80, b=5, mu0=1e-3))
p.td.model

The SPM offers longitudinal data imputation with results that are better than from other imputation tools since it preserves data structure, i.e. relation between Y(t) and mu(Y(t),t). Below there are two examples of multiple data imputation with function spm.impute(…).

library(stpm)

#######################################################
############## One dimensional case ###################
#######################################################

# Data preparation (short format)#
data <- simdata_discr(N=1000, dt = 2, format="short")

miss.id <- sample(x=dim(data)[1], size=round(dim(data)[1]/4)) # ~25% missing data
incomplete.data <- data
incomplete.data[miss.id,4] <- NA
# End of data preparation #

##### Multiple imputation with SPM #####
imp.data <- spm.impute(x=incomplete.data, id=1, case="xi", t1=3, covariates="y1", minp=1, theta_range=seq(0.075, 0.09, by=0.001))$imputed

##### Look at the incomplete data with missings #####
head(incomplete.data)

##### Look at the imputed data #####
head(imp.data)


#########################################################
################ Two-dimensional case ###################
#########################################################

# Parameters for data simulation #
a <- matrix(c(-0.05, 0.01, 0.01, -0.05), nrow=2)
f1 <- matrix(c(90, 30), nrow=1, byrow=FALSE)
Q <- matrix(c(1e-7, 1e-8, 1e-8, 1e-7), nrow=2)
f0 <- matrix(c(80, 25), nrow=1, byrow=FALSE)
b <- matrix(c(5, 3), nrow=2, byrow=TRUE)
mu0 <- 1e-04
theta <- 0.07
ystart <- matrix(c(80, 25), nrow=2, byrow=TRUE)

# Data preparation #
data <- simdata_discr(N=1000, a=a, f1=f1, Q=Q, f=f0, b=b, ystart=ystart, mu0 = mu0, theta=theta, dt=2, format="short")

# Delete some observations in order to have approx. 25% missing data
incomplete.data <- data
miss.id <- sample(x=dim(data)[1], size=round(dim(data)[1]/4)) 
incomplete.data <- data
incomplete.data[miss.id,4] <- NA
miss.id <- sample(x=dim(data)[1], size=round(dim(data)[1]/4)) 
incomplete.data[miss.id,5] <- NA
# End of data preparation #

##### Multiple imputation with SPM #####
imp.data <- spm.impute(x=incomplete.data, id=1, case="xi", t1=3, covariates=c("y1", "y2"), minp=1, theta_range=seq(0.060, 0.07, by=0.001))$imputed

##### Look at the incomplete data with missings #####
head(incomplete.data)

##### Look at the imputed data #####
head(imp.data)

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4