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#' @title Edit Snippet
#' @description Snippet for the package BayesianFROC. Copy and paste to the snippet edition tools in your R studio for the conforable usage of the package BayesianFROC. This is under construction. To edit snippet, you can open, by R-stuido, the editor located in Tools > Global options > Code > Edit snippets.
#' @return nothing
#' @export
#' @details
#'
#' if $ are included such as
#'
#' foo$b
#'
#' then in message it should be
#'
#' message("foo\\$a")
#'
#'2020 JUly2
#'
#'
#' @examples
#'
#' snippet_for_BayesianFROC()
#'
snippet_for_BayesianFROC <- function(){
message("
snippet iii
install.packages(${1:\"BayesianFROC\"})
snippet www
devtools::load_all(\".\");
snippet sss
stanModel <- stan_model_of_sbc()
Simulation_Based_Calibration_single_reader_single_modality_via_rstan_sbc(
ite = 233,
M = 11,
epsilon = ${1:0.04},
stanModel = stanModel
)
snippet ddd
devtools::document();?${1:\"BayesianFROC\"}
snippet error_MRMC
a <- error_MRMC(ite = 1111, replication.number = 33, NL=333)
snippet demo-review
demo(demo_for_reviewer_of_my_manuscript,package=\"BayesianFROC\")
snippet methods::as
fitt <- methods::as(fit,\"stanfit\")
snippet drawcur
DrawCurves(fit, Colour = FALSE, new.imaging.device = F)
snippet citation
citation(${1:\"BayesianFROC\"})
snippet download
BayesianFROC::dark_theme()
a<- cranlogs::cran_downloads(
packages = \"BayesianFROC\",
from = \"2019-05-12\",
#from = \"2019-05-03\",
to = Sys.Date())
a
b <- a\\$count;
number.of.downloads <-b;
mean(b)
date<- 1:as.integer(length(b))
today <- b[length(b)-2]
title <- paste(\"today = \",today ,\",\",
b[length(b)-3],\",\",
b[length(b)-4],\",\",
b[length(b)-5],\",\",
b[length(b)-6],\",\",
b[length(b)-7],\",\",
b[length(b)-8],\",\",
b[length(b)-9]
)
plot(date,number.of.downloads ,type=\"l\",col=\"yellow\", main = title)
for (hhh in (0:5)*10) graphics::abline(h=hhh)
graphics::abline(h=b[length(b)-4], col=\"red\",lty =\"dashed\",lwd =\"2\")
graphics::abline(h=b[length(b)-3], col=\"red\",lty =\"dashed\",lwd =\"3\")
graphics::abline(h=b[length(b)-2], col=\"red\",lty =\"solid\",lwd =\"3\")
plot(date, cumsum(b), type=\"l\",col=\"yellow\", main = title)
hist(b,col = \"red\",border=\"yellow\",breaks=77, main = title)
for (aaa in 1:50) {
graphics::abline(h=aaa)
if (aaa%%5==0) {
graphics::abline(h=aaa,lwd =\"2\")
# graphics::abline(v=5,lwd =\"2\")
# # graphics::abline(v=15,lwd =\"1\",lty =\"dashed\")
}
}
graphics::abline(v =b[length(b)-2], untf = FALSE, col=\"red\",lty =\"solid\",lwd =\"4\");
graphics::abline(v =b[length(b)-2]-1, untf = FALSE, col=\"red\",lty =\"solid\",lwd =\"4\")
graphics::abline(v =b[length(b)-3], untf = FALSE, col=\"red\",lty =\"dashed\",lwd =\"2\");
graphics::abline(v =b[length(b)-3]-1, untf = FALSE, col=\"red\",lty =\"dashed\",lwd =\"2\")
graphics::abline(v =b[length(b)-4], untf = FALSE, col=\"red\",lty =\"dotdash\",lwd =\"1\");
graphics::abline(v =b[length(b)-4]-1, untf = FALSE, col=\"red\",lty =\"dotdash\",lwd =\"1\")
# df<-data.frame(b=b)
# g <- ggplot2::ggplot(df, ggplot2::aes(x = b))
# g <- g + ggplot2::geom_histogram(binwidth = 1)
# plot(g)
#x<-runif(1000)/10;for(i in 1:1000) y[i]<- mean(b,trim = x[i]);plot(x,y)
sum(b)
jjj<-length(b)
tails <-list( length = jjj , mode = \"vector\")
tail_area <- vector(length = jjj, mode = \"numeric\")
for (iii in 1:jjj) {
tails[[iii]] <- b[b>b[length(b)-iii]]
tail_area[iii] <- length(tails[[iii]]) /length(b)
}
tail_area_rev <- rev(tail_area)
tail_area_rev <- round(tail_area_rev,digits = 3)
tail_area_rev
# hist(tail_area)
plot(date,tail_area_rev ,type=\"l\",col=\"yellow\", main = title)
graphics::abline(h=0.5,lwd =\"2\")
snippet fff.pkg.detach
detach(\"package:BayesianFROC\")
snippet fff.pkg.attached
names(sessionInfo()\\$otherPkgs)
snippet net
curl::has_internet()
snippet sim_MRMC
fit <- Simulation_Based_Calibration_single_reader_single_modality_via_rstan_sbc_MRMCsi()
snippet sim
fit <- Simulation_Based_Calibration_single_reader_single_modality_via_rstan_sbc()
snippet ttt
tttttt( ww=-0.81,www =0.001,
mm=0.65,mmm=0.001,
vv=5.31,vvv=0.001,
zz= 1.55,zzz=0.001 )
snippet pipe
`%>%` <- utils::getFromNamespace(\"%>%\", \"magrittr\")
snippet ins
install.packages(\"${1:package}\")
snippet doc
devtools::document();?
snippet vig
utils::vignette( package = \"${2:survival}\", topic = \"${1:validate}\" )
snippet fitb
${4:fit} <- fit_Bayesian_FROC( ite = ${2:1111}, cha = 1, summary = ${3:F}, Null.Hypothesis = ${3:F}, dataList = ${1:dataList.Chakra.1} )
snippet DrawC
DrawCurves( modalityID = c(${3:1}), readerID = c(${2:1}), ${1:fit} )
snippet rel
BayesianFROC:::release_before()
snippet clear
BayesianFROC:::clearWorkspace()
snippet d
devtools::
snippet fffaaabbb
BayesianFROC:::fffaaabbb()
snippet fff
${4:fit} <- fit_Bayesian_FROC( ite = ${2:1111}, cha = 1, summary = ${3:F}, dataList = ${1:dataList.Chakra.1} )
snippet extract_EAP
extract_EAP_CI(fit,\"${1:l}\",${2:fit@dataList\\$C })
snippet fitb
${4:fit} <- fit_Bayesian_FROC( ite = ${2:1111}, summary = ${3:FALSE}, cha = 1, dataList = ${1:dataList.Chakra.1} )
snippet demo-drawcurves-srsc
demo(demo_drawcurves_srsc,package=\"BayesianFROC\")
snippet demo-stan
demo(demo_stan,package=\"BayesianFROC\")
snippet demo-srsc
demo(demo_srsc,package=\"BayesianFROC\")
snippet demo-MRMC
demo(demo_MRMC,package=\"BayesianFROC\")
snippet datalist
dat <- list(
c=c(3,2,1), #Confidence level
h=c(97,32,31), #Number of hits for each confidence level
f=c(1,14,74), #Number of false alarms for each confidence level
NL=259, #Number of lesions
NI=57, #Number of images
C=3) #Number of confidence level
snippet Draw_a_simulated_data_se
Draw_a_simulated_data_set(
sd = 5, C = 5,
seed.for.drawing.a.prior.sample = 1111,
fun = stats::var,
NI = 259,
NL = 259,
initial.seed.for.drawing.a.data = 1234,
ModifiedPoisson = FALSE,
ite = 1111)
snippet simula
g <- Simulation_Based_Calibration_histogram( NI=1111111, NL=1111111, N=111, ite=11111 )
snippet Dra
Draw.a.prior.sample <- Draw_a_prior_sample()
fit <- Draw_a_simulated_data_set_and_Draw_posterior_samples(Draw.a.prior.sample)
snippet repl
r <-replicate_model_MRMC()
snippet brow
browser()
snippet gets
g <- get_samples_from_Posterior_Predictive_distribution(
StanS4class = fit,
Colour = TRUE,
plot.replicated.points = FALSE
)
snippet forget
memoise::forget(fit_Bayesian_FROC)
snippet memo
fit_Bayesian_FROC <- memoise::memoise(fit_Bayesian_FROC)
snippet error_srsc
datasets <-error_srsc(
NLvector = c(100,10000000,1000000000),
ite = 2222
)
snippet lib
library(${1:BayesianFROC})
snippet llllibrary
library(${1:BayesianFROC})
#############################################################20190525
snippet fitba
fit <- fit_Bayesian_FROC( ite = 1111, summary = FALSE, cha=1, dataList = dataList.Chakra.1 )
snippet extract_EAP
extract_EAP_CI(fit,\"${1:l}\",${2:fit@dataList\\$C })
snippet clear
BayesianFROC:::clearWorkspace()
snippet d
devtools::
snippet fff
BayesianFROC:::fffaaabbb()
snippet extract_EAP
extract_EAP_CI(fit,\"${1:l}\",${2:fit@dataList\\$C })
snippet fitb
fit <- fit_Bayesian_FROC( ite = ${2:1111}, summary = ${1:FALSE}, cha = 1, dataList = dataList.Chakra.1 )
snippet fff
fit <- fit_Bayesian_FROC( ite = ${2:1111}, summary = ${1:FALSE}, cha = 1, dataList = dataList.Chakra.1 )
snippet fitba
${2:fit} <- fit_Bayesian_FROC( dataList = ${1:dataList.Chakra.1},
# Substitute your data.
# To run the code, it is sufficient in default arguments for the following variables.
ite = ${3:1111}, # No. of iterations for Monte Carlo (MCMC) Simulation
summary = ${4:FALSE}, # if TRUE, it shows summary of estimates by the print method for stanfit
cha = 1, # No. of chains for MCMC
dig =3 # digit for estimates
)
# The variable dataList should be changed in your data.
snippet demo-drawcurves-srsc
demo(demo_drawcurves_srsc,package=\"BayesianFROC\")
snippet demo-stan
demo(demo_stan,package=\"BayesianFROC\")
snippet demo-srsc
demo(demo_srsc,package=\"BayesianFROC\")
snippet demo-MRMC
demo(demo_MRMC,package=\"BayesianFROC\")
snippet datalist
dat <- list(
c=c(3,2,1), #Confidence level
h=c(97,32,31), #Number of hits for each confidence level
f=c(1,14,74), #Number of false alarms for each confidence level
NL=259, #Number of lesions
NI=57, #Number of images
C=3) #Number of confidence level
snippet Draw_a_simulated_data_se
Draw_a_simulated_data_set(
sd = 5, C = 5,
seed.for.drawing.a.prior.sample = 1111,
fun = stats::var,
NI = 259,
NL = 259,
initial.seed.for.drawing.a.data = 1234,
ModifiedPoisson = FALSE,
ite = 1111)
snippet simula
g <- Simulation_Based_Calibration_histogram( NI=1111111, NL=1111111, N=111, ite=11111 )
snippet Dra
Draw.a.prior.sample <- Draw_a_prior_sample()
fit <- Draw_a_simulated_data_set_and_Draw_posterior_samples(Draw.a.prior.sample)
snippet repl
r <-replicate_model_MRMC()
snippet brow
browser()
snippet pval
p_value_of_the_Bayesian_sense_for_chi_square_goodness_of_fit(
StanS4class=fit,
dig=3,
Colour=TRUE,
plot.replicated.points=FALSE)
snippet gets
g <- get_samples_from_Posterior_Predictive_distribution(
StanS4class = fit,
Colour = TRUE,
plot.replicated.points = FALSE
)
snippet forget
memoise::forget(fit_Bayesian_FROC)
snippet memo
fit_Bayesian_FROC <- memoise::memoise(fit_Bayesian_FROC)
snippet error_srsc
datasets <-error_srsc(
NLvector = c(100,10000000,1000000000),
ite = 2222
)
snippet lib
library(${1:BayesianFROC})
")
}
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