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utils.R
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utils.R
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##########################
### format the data
##########################
make.world.population <- function(file = NULL, country)
{
world.population <- read.delim(
file = file,
sep = "\t",
head = TRUE,
stringsAsFactors = FALSE
)
world.population <- data.frame(
country = world.population[, 3],
population = world.population$X2020,
stringsAsFactors = FALSE
)
world.population$population <-
gsub(" ", "", world.population$population)
world.population$population <-
as.integer(world.population$population) * 1000
world.population$country[which(world.population$country == "Bolivia (Plurinational State of)")] <-
"Bolivia"
world.population$country[which(world.population$country == "Brunei Darussalam")] <-
"Brunei"
world.population$country[which(world.population$country == "Myanmar")] <-
"Burma"
world.population$country[which(world.population$country == "Congo")] <-
"Congo (Brazzaville)"
world.population$country[which(world.population$country == "Democratic Republic of the Congo")] <-
"Congo (Kinshasa)"
world.population$country[which(world.population$country == "Côte d'Ivoire")] <-
"Cote d'Ivoire"
world.population$country[which(world.population$country == "Iran (Islamic Republic of)")] <-
"Iran"
world.population$country[which(world.population$country == "Republic of Korea")] <-
"Korea, South"
world.population$country[which(world.population$country == "Lao People's Democratic Republic")] <-
"Laos"
world.population$country[which(world.population$country == "Republic of Moldova")] <-
"Moldova"
world.population$country[which(world.population$country == "Russian Federation")] <-
"Russia"
world.population$country[which(world.population$country == "Syrian Arab Republic")] <-
"Syria"
world.population$country[which(world.population$country == "United States of America")] <-
"US"
world.population$country[which(world.population$country == "United Republic of Tanzania")] <-
"Tanzania"
world.population$country[which(world.population$country == "China, Taiwan Province of China")] <-
"Taiwan*"
world.population$country[which(world.population$country == "Venezuela (Bolivarian Republic of)")] <-
"Venezuela"
world.population$country[which(world.population$country == "Viet Nam")] <-
"Vietnam"
rownames(world.population) <- world.population$country
return(world.population)
}
make.usa.population <- function(file = NULL, country)
{
usa.population <- read.delim(
file = file,
sep = "\t",
head = TRUE,
stringsAsFactors = FALSE
)
usa.population <- data.frame(
country = usa.population[, 1],
population = usa.population$X2019,
stringsAsFactors = FALSE
)
rownames(usa.population) <- usa.population$country
return(usa.population)
}
make.covid.agg <-
function(covid.file = NULL, group = "Country.Region")
{
###############################################
### the file contains columns for each date ###
### we want a date.frame with 3 colums: ###
### - country ###
### - date ###
### - death ###
###############################################
### file header
covid.header <-
read.delim(
file = covid.file,
header = FALSE,
stringsAsFactors = FALSE,
sep = ",",
nrow = 1
)
### death value
covid <-
read.delim(
file = covid.file,
header = TRUE,
stringsAsFactors = FALSE,
sep = ","
)
### list of columns to delete
ind.del <- grep("^X[0-9]", names(covid), invert = TRUE)
### aggregate values by country
covid.agg <-
rowsum(covid[, -ind.del], group = covid[, group])
### date (MM/DD/YYYY)
covid.date <- t(covid.header[1, -ind.del])
######################################################################################
### data.frame with country, date, death with number of death per day (cumul per day)
######################################################################################
country.value <- NULL
date.value <- NULL
death.value <- NULL
for (i in 1:nrow(covid.agg))
{
country.value <-
c(country.value, rep(rownames(covid.agg)[i], ncol(covid.agg)))
date.value <- c(date.value, covid.date)
death.value <-
c(death.value, as.vector(t(covid.agg[i,])))
}
daily.cumulative.death <- data.frame(
country = country.value,
date = date.value,
death = death.value,
stringsAsFactors = FALSE
)
daily.cumulative.death$date <-
as.Date(daily.cumulative.death$date, format = "%m/%d/%y")
##########################################################################################
### data.frame with country, date, death with number of death per day (increase per day)
##########################################################################################
country.value <- NULL
date.value <- NULL
death.value <- NULL
for (i in 1:nrow(covid.agg))
{
country.value <-
c(country.value, rep(rownames(covid.agg)[i], ncol(covid.agg) - 1))
date.value <- c(date.value, covid.date[-1])
death.value <-
c(death.value, diff(as.vector(t(covid.agg[i,]))))
}
daily.death <- data.frame(
country = country.value,
date = date.value,
death = death.value,
stringsAsFactors = FALSE
)
daily.death$date <-
as.Date(daily.death$date, format = "%m/%d/%y")
### Number of death at t0
daily.death.t0 <- data.frame(
country = rownames(covid.agg),
date = covid.date[1],
death.t0 = covid.agg[, 1],
stringsAsFactors = FALSE
)
### Total number of deaths per country
total.death <-
rowsum(daily.death[, 3], group = daily.death$country)
total.death <-
data.frame(country = rownames(total.death), death = total.death)
total.death <-
merge(daily.death.t0[, c("country", "death.t0")], total.death)
total.death$death.total <-
total.death$death.t0 + total.death$death
total.death <-
total.death[order(total.death$death.total, decreasing = TRUE),]
return(
list(
daily.cumulative.death = daily.cumulative.death,
total.death = total.death,
daily.death = daily.death
)
)
}
### adjust data for France
adjust.france <- function(daily.cumulative.death = NULL,
date.ehpad = as.Date("04/02/20", format = "%m/%d/%y"),
scale = 1.3)
{
ind <- which(daily.cumulative.death$date < date.ehpad)
daily.cumulative.death$death[ind] <-
daily.cumulative.death$death[ind] * scale
daily.cumulative.death$death[ind] <-
as.integer(floor(daily.cumulative.death$death[ind]))
daily.cumulative.death$country <- "France (adjusted)"
return(daily.cumulative.death)
}
############################
### ggplotly plot formatting
############################
add.prediction.button <- function(gg.plotly.fig = NULL,
countries.of.interest = NULL,
nb.logistic.layer = NULL)
{
nb.obj <- length(gg.plotly.fig$x$data)
nb.countries <- length(countries.of.interest)
for (i in 1:nb.obj)
{
gg.plotly.fig$x$data[[i]]$visible <- FALSE
}
for (i in 1:nb.countries)
{
gg.plotly.fig$x$data[[i]]$visible <- TRUE
}
show.predictionL <- rep(FALSE, nb.obj)
show.predictionL[1:nb.logistic.layer] <- TRUE
hide.prediction <- rep(FALSE, nb.obj)
hide.prediction[1:nb.countries] <- TRUE
show.predictionR <- hide.prediction
show.predictionR[(nb.logistic.layer + 1):nb.obj] <- TRUE
show.all <- rep(TRUE, nb.obj)
gg.plotly.fig <- gg.plotly.fig %>%
layout(yaxis = list(hoverformat = ".1f"),
updatemenus = list(
list(
x = 0.09,
y = 0.95 ,
active = 3,
showactive = TRUE,
buttons = list(
list(
method = "restyle",
args = list("visible", as.list(show.predictionL)),
label = "Predict (L)"
),
list(
method = "restyle",
args = list("visible", as.list(show.predictionR)),
label = "Predict (R)"
),
list(
method = "restyle",
args = list("visible", as.list(show.all)),
label = "Predict (L+R)"
),
list(
method = "restyle",
args = list("visible", as.list(hide.prediction)),
label = "Raw data"
)
)
),
list(
x = 0.19,
y = 0.95,
active = 0,
showactive = TRUE,
buttons = list(
list(
method = "relayout",
args = list("yaxis.type", "linear"),
label = "linear"
),
list(
method = "relayout",
args = list("yaxis.type", "log"),
label = "logarithmic"
)
)
)
))
return(gg.plotly.fig)
}
format.legend.ggplotly <- function(gg.plotly.fig = NULL)
{
for (i in 1:length(gg.plotly.fig$x$data))
{
if (length(grep("marker", names(gg.plotly.fig$x$data[i][[1]]))) > 0)
{
if (gg.plotly.fig$x$data[i][[1]]$marker$symbol == "triangle-up")
{
cat("legend FALSE\n")
gg.plotly.fig$x$data[[i]]$showlegend <- FALSE
}
}
}
return(gg.plotly.fig)
}
#########################################################################################
plot.prediction <- function(death.prediction = NULL,
countries.of.interest = NULL,
max.date.pred = NULL,
y.lim.max = NULL,
pic.value.all = NULL,
confinement.date = NULL,
title.prediction = NULL)
{
p.prediction <- ggplot(death.prediction, aes(date, death)) +
geom_point() +
geom_line() +
geom_line(data = death.prediction,
aes(x = date, y = prediction),
linetype = "dashed") +
scale_color_manual(values = myPalette(length(countries.of.interest))) +
scale_fill_manual(values = myPalette(length(countries.of.interest))) +
scale_x_date(
date_breaks = "7 days",
date_minor_breaks = "1 day",
limits = c(min(death.prediction$date), max.date.pred)
) +
#geom_ribbon(data = death.prediction,
# aes(ymin = lower,
# ymax = upper),
# alpha = 0.3) +
geom_point(
data = pic.value.all,
aes(x = date, y = peak.epidemic),
shape = 24,
size = 4,
color = "darkred",
show.legend = FALSE
)
if (is.null(confinement.date) == FALSE)
{
p.prediction <- p.prediction + geom_point(
data = confinement.date,
aes(x = date, y = death),
shape = 13,
size = 4
)
}
p.prediction <- p.prediction + scale_y_continuous(n.breaks = 15,
limits = c(0, y.lim.max))
p.prediction <- p.prediction + labs(y = "#deaths") +
aes(color = country, fill = country) +
ggtitle(title.prediction)
nb.logistic.layer <- length(ggplotly(p.prediction)$x$data)
p.prediction <- p.prediction +
geom_line(data = death.prediction,
aes(x = date, y = prediction.richards),
linetype = "dashed") +
#geom_ribbon(
# data = death.prediction,
# aes(ymin = lower.richards,
# ymax = upper.richards),
# alpha = 0.3) +
geom_point(
data = pic.value.all,
aes(x = date.richards, y = peak.epidemic.richards),
shape = 24,
size = 4,
color = "darkred",
show.legend = FALSE
)
p.prediction <- ggplotly(p.prediction)
p.prediction <- format.legend.ggplotly(p.prediction)
p.prediction <-
add.prediction.button(p.prediction, countries.of.interest, nb.logistic.layer)
return(p.prediction)
}
#########################################################################################
plot.prediction.norm <- function(death.prediction = NULL,
countries.of.interest = NULL,
pic.value.all = NULL,
confinement.date = NULL,
title.prediction.norm = NULL)
{
p.prediction.norm <-
ggplot(death.prediction, aes(time.norm, death.norm)) +
geom_point() +
geom_line() +
scale_color_manual(values = myPalette(length(countries.of.interest))) +
scale_fill_manual(values = myPalette(length(countries.of.interest))) +
geom_line(data = death.prediction,
aes(x = time.norm, y = prediction.norm),
linetype = "dashed") +
geom_point(
data = pic.value.all,
aes(x = time.norm, y = peak.epidemic.norm),
shape = 24,
size = 4,
color = "darkred",
show.legend = FALSE
) +
labs(x = "normalized time (day)",
y = "#deaths (per millions inhabitants)") +
ggtitle(title.prediction.norm)
if (is.null(confinement.date) == FALSE)
{
p.prediction.norm <- p.prediction.norm + geom_point(
data = confinement.date,
aes(x = time.norm, y = death.norm),
shape = 13,
size = 4
)
}
p.prediction.norm <-
p.prediction.norm + scale_y_continuous(n.breaks = 15,
limits = c(0, 1300))
p.prediction.norm <-
p.prediction.norm + aes(color = country, fill = country)
nb.logistic.layer <- length(ggplotly(p.prediction.norm)$x$data)
p.prediction.norm <-
p.prediction.norm + geom_line(
data = death.prediction,
aes(x = time.norm, y = prediction.norm.richards),
linetype = "dashed"
) +
geom_point(
data = pic.value.all,
aes(x = time.norm.richards, y = peak.epidemic.richards.norm),
shape = 24,
size = 4,
color = "darkred",
show.legend = FALSE
)
p.prediction.norm <- ggplotly(p.prediction.norm)
p.prediction.norm <- format.legend.ggplotly(p.prediction.norm)
p.prediction.norm <-
add.prediction.button(p.prediction.norm,
countries.of.interest,
nb.logistic.layer)
return(p.prediction.norm)
}
###############################################################################
plot.daily.death <- function(daily.death = NULL,
countries.of.interest = NULL,
title.daily.death = NULL)
{
p.daily.death <- ggplot(daily.death, aes(date, death)) +
geom_line() +
geom_point() +
scale_color_manual(values = myPalette(length(countries.of.interest))) +
scale_fill_manual(values = myPalette(length(countries.of.interest))) +
scale_x_date(date_breaks = "7 days", date_minor_breaks = "1 day") +
ggtitle(title.daily.death) +
ylab("death per day") +
aes(color = country, fill = country)
p.daily.death <- ggplotly(p.daily.death)
p.daily.death <- p.daily.death %>%
layout(
updatemenus = list(
list(
x = 0.09,
y = 0.95,
active = 0,
showactive = TRUE,
buttons = list(
list(
method = "relayout",
args = list("yaxis.type", "linear"),
label = "linear"
),
list(
method = "relayout",
args = list("yaxis.type", "log"),
label = "logarithmic"
)
)
)
))
return(p.daily.death)
}
### modify the values to have better rendering in ggplot graphics
### for the confidence intervals
adjust.confint.4plot <- function(l.u.int = NULL,
death.country = NULL,
y.lim.max = NULL)
{
ind.upper <- which(l.u.int$upper > y.lim.max)
if (length(ind.upper) == nrow(death.country))
{
l.u.int$upper[ind.upper] <- NA
} else
{
l.u.int$upper[ind.upper] <- y.lim.max
}
ind.lower <- which(l.u.int$lower < 0)
if (length(ind.lower) == nrow(death.country))
{
l.u.int$lower[ind.lower] <- NA
} else
{
l.u.int$lower[ind.lower] <- 1
}
if (length(ind.lower) == nrow(death.country) |
length(ind.upper) == nrow(death.country))
{
l.u.int$lower <- l.u.int$upper <- NA
}
ind.inconsistent <- which(l.u.int$lower > l.u.int$upper)
if (length(ind.inconsistent) > 0)
{
l.u.int$lower[ind.inconsistent] <- max(l.u.int$upper)
}
death.country <- cbind(death.country, l.u.int)
return(death.country)
}
##########################
### statistical analysis
##########################
fitLmLog <- function(death.country)
{
res.lm <- lm(log(death) ~ time + log(time), data = death.country)
prediction.lm <- predict(res.lm, death.country["time"])
prediction.lm <- exp(prediction.lm)
return(list(res.lm = res.lm,
prediction.lm = prediction.lm))
}
fitBrody <- function(death.country)
{
death.country$newtime <-
death.country$time - min(death.country$time)
ind.na <- which(is.na(death.country$death) == TRUE)
### sigmoid function
params.grid <-
expand.grid(
Asym = log(c(100, seq(1000, 30000, 500))),
xmid = seq(1, 5, 0.2),
scal = seq(2, 8, 0.5)
)
cat("fit with brute force\n")
fit.res.brody0 <-
nls2(
log(death) ~ Asym * (1 - exp(-scal * (newtime - xmid)) ^ 3),
data = death.country[-ind.na,],
algorithm = "brute-force",
start = params.grid
)
Alogis0 <- coefficients(fit.res.brody0)["Asym"]
xmid0 <- coefficients(fit.res.brody0)["xmid"]
scal0 <- coefficients(fit.res.brody0)["scal"]
cat("fit with previous model as start value\n")
fit.res.brody <-
nlsLM(
log(death) ~ Asym * (1 - exp(-scal * (newtime - xmid)) ^ 3),
data = death.country[-ind.na],
start = list(
Asym = Alogis0,
xmid = xmid0,
scal = scal0
),
control = nls.lm.control(maxiter = 1024, maxfev = 20000),
model = TRUE
)
Alogis <- coef(fit.res.brody)["Asym.Asym"]
xmid <- coef(fit.res.brody)["xmid.xmid"]
scal <- coef(fit.res.brody)["scal.scal"]
cat("\tAsym: ", Alogis, " xmid: ", xmid, " scal: ", scal, "\n")
prediction.value <-
exp(Alogis * (1 - exp(-scal * (
death.country$newtime - xmid
)) ^ 3))
death.country <- data.frame(death.country,
prediction.brody = prediction.value)
return(death.country)
}
### add extra date for the prediction
### only if there are more that 20 values
add.extra.date <- function(death.country = NULL,
max.date.pred = NULL,
min.time = NULL,
nb.obs = 20)
{
extra.date <-
seq.Date(
from = max(death.country$date) + 1,
to = as.Date(max.date.pred, format = "%m/%d/%y"),
by = "day"
)
extra.value <-
data.frame(
country = rep(death.country$country[1], length(extra.date)),
date = extra.date,
death = NA,
death.norm = NA,
time = as.numeric(extra.date) - min.time,
stringsAsFactors = FALSE
)
### if less than nb.nobs values, the prediction will not be done
if (nrow(death.country) > nb.obs)
{
death.country <- rbind(death.country, extra.value)
}
return(death.country)
}
### compute confint for NLS model
compute.confint <- function(fit.res.nls = NULL,
death.country = NULL,
do.sim = FALSE,
nsim = 10 ^ 5)
{
cat("condidence interval\n")
l.u.int.values <-
my.predictNLS(
fit.res.nls,
death.country["time"],
interval = "confidence",
do.sim = do.sim,
nsim = nsim
)
if (do.sim)
{
l.u.int <-
data.frame(lower = l.u.int.values$summary$Sim.2.5,
upper = l.u.int.values$summary$Sim.97.5)
} else
{
l.u.int <-
data.frame(lower = l.u.int.values$summary$Prop.2.5,
upper = l.u.int.values$summary$Prop.97.5)
}
return(l.u.int)
}
### logit functions
### used to add constraint on the Richards model
my.logit <- function(x)
{
return(logit(x, max = 2000))
}
my.inv.logit <- function(x)
{
return(inv.logit(x, max = 2000))
}
### richards model (Generalised logistic function)
fitRichards <- function(death.country = NULL,
population.size = NULL,
min.time = NULL)
{
death.country <- norm.pop.size(death.country = death.country,
population.size = population.size)
nb.obs <- nrow(death.country)
params.grid <-
expand.grid(
#Asym = my.logit(c(1, seq(10, 1100, 100))),
Asym = c(1, seq(10, 800, 100)),
xmid = seq(20, 100, 2),
scal = seq(2, 10, 1),
nu = log(seq(0.1, 5.1, 1))
)
cat("fit with brute force\n")
fit.res <-
nls2(
#death.norm ~ my.inv.logit(Asym)/(1 + exp(nu) * exp((xmid - time)/scal))^(1 / exp(nu)),
death.norm ~ Asym / (1 + exp(nu) * exp((xmid - time) / scal)) ^
(1 / exp(nu)),
data = death.country,
algorithm = "brute-force",
start = params.grid
)
Asym0 <- coefficients(fit.res)["Asym"]
xmid0 <- coefficients(fit.res)["xmid"]
scal0 <- coefficients(fit.res)["scal"]
nu0 <- coefficients(fit.res)["nu"]
#cat("\tAsym: ", my.inv.logit(Asym0), " xmid: ", xmid0, " scal: ", scal0, "nu: ", nu0, "\n")
cat("\tAsym: ",
Asym0,
" xmid: ",
xmid0,
" scal: ",
scal0,
"nu: ",
nu0,
"\n")
cat("fit with previous model as start value\n")
an.error.occured <- FALSE
tryCatch(
{
fit.res <-
nlsLM(
#death.norm ~ my.inv.logit(Asym)/(1 + exp(nu) * exp((xmid - time)/scal))^(1 / exp(nu)),
death.norm ~ Asym / (1 + exp(nu) * exp((xmid - time) / scal)) ^
(1 / exp(nu)),
data = death.country,
start = list(
Asym = Asym0,
xmid = xmid0,
scal = scal0,
nu = nu0
),
control = nls.lm.control(maxiter = 1024, maxfev = 20000),
model = TRUE
)
},
error = function(e) {an.error.occured <<- TRUE}
)
cat("nlsLM failed: ", an.error.occured, "\n")
death.country <- add.extra.date(
death.country = death.country,
max.date.pred = max.date.pred,
min.time = min.time,
nb.obs = 20
)
if(! an.error.occured)
{
Asym <- coef(fit.res)["Asym.Asym"]
xmid <- coef(fit.res)["xmid.xmid"]
scal <- coef(fit.res)["scal.scal"]
nu <- coef(fit.res)["nu.nu"]
#cat("\tAsym: ", my.inv.logit(Asym), " xmid: ", xmid, " scal: ", scal, "nu: ", nu, "\n")
cat("\tAsym: ", Asym, " xmid: ", xmid, " scal: ", scal, "nu: ", nu, "\n")
death.country$prediction.norm <-
#my.inv.logit(Asym)/(1 + exp(nu) * exp((xmid - death.country$time)/scal))^(1 / exp(nu))
Asym / (1 + exp(nu) * exp((xmid - death.country$time) / scal)) ^
(1 / exp(nu))
### confidence intervals for the parameters
confint.value <- as.matrix(confint2(fit.res))
confint.pic.value <-
min(daily.cumulative.death$date) + floor(confint.value["xmid.xmid", ]) + 1
pic.value <-
min(daily.cumulative.death$date) + floor(xmid) + 1
confint.list <-
list(confint = confint.value,
confint.pic = confint.pic.value,
pic.value = pic.value)
### confidence intervals for the predicted values
l.u.int <- compute.confint(fit.res.nls = fit.res,
death.country = death.country)
### inverse value to population size
death.country$prediction <-
norm.pop.size.inv(value = death.country$prediction.norm,
population.size = population.size)
l.u.int$lower <- norm.pop.size.inv(value = l.u.int$lower,
population.size = population.size)
l.u.int$upper <- norm.pop.size.inv(value = l.u.int$upper,
population.size = population.size)
l.u.int$upper[1:nb.obs] <- l.u.int$lower[1:nb.obs] <- NA
### modify the values to have better rendering in ggplot graphics
death.country <- adjust.confint.4plot(l.u.int = l.u.int,
death.country = death.country,
y.lim.max = y.lim.max)
}
else
{
death.country <- data.frame(death.country,
prediction.norm = NA,
prediction = NA,
lower = NA,
upper = NA,
stringsAsFactors = FALSE)
confint.list <-
list(confint = NA,
confint.pic = NA,
pic.value = max(daily.cumulative.death$date) )
}
return(list(death.country = death.country,
confint.list = confint.list))
}
### logistic model (sigmoid function)
fitLogistic <- function(death.country = NULL,
population.size = NULL,
min.time = NULL)
{
death.country <- norm.pop.size(death.country = death.country,
population.size = population.size)
params.grid <-
expand.grid(
Asym = c(1, seq(10, 1100, 100)),
xmid = seq(10, 100, 2),
scal = seq(2, 10, 0.5)
)
cat("fit with brute force\n")
fit.res.logis0 <-
nls2(
death.norm ~ Asym / ((1 + exp((
xmid - time
) / scal))),
data = death.country,
algorithm = "brute-force",
start = params.grid
)
Alogis0 <- coefficients(fit.res.logis0)["Asym"]
xmid0 <- coefficients(fit.res.logis0)["xmid"]
scal0 <- coefficients(fit.res.logis0)["scal"]
cat("\tAsym: ", Alogis0, " xmid: ", xmid0, " scal: ", scal0, "\n")
cat("fit with previous model as start value\n")
fit.res.logis <-
nlsLM(
death.norm ~ Asym / ((1 + exp((
xmid - time
) / scal))),
data = death.country,
start = list(
Asym = Alogis0,
xmid = xmid0,
scal = scal0
),
control = nls.lm.control(maxiter = 1024, maxfev = 20000),
model = TRUE
)
Alogis <- coef(fit.res.logis)["Asym.Asym"]
xmid <- coef(fit.res.logis)["xmid.xmid"]
scal <- coef(fit.res.logis)["scal.scal"]
cat("\tAsym: ", Alogis, " xmid: ", xmid, " scal: ", scal, "\n")
death.country <- add.extra.date(
death.country = death.country,
max.date.pred = max.date.pred,
min.time = min.time,
nb.obs = 20
)
death.country$prediction.norm <-
Alogis / (1 + exp((xmid - death.country$time) / scal))
### confidence intervals for the parameters
confint.value <- as.matrix(confint2(fit.res.logis))
confint.pic.value <-
min(daily.cumulative.death$date) + floor(confint.value["xmid.xmid", ]) + 1
pic.value <-
min(daily.cumulative.death$date) + floor(xmid) + 1
confint.list <-
list(confint = confint.value,
confint.pic = confint.pic.value,
pic.value = pic.value)
### confidence intervals for the predicted values
l.u.int <- compute.confint(fit.res.nls = fit.res.logis,
death.country = death.country)
### inverse value to population size
death.country$prediction <-
norm.pop.size.inv(value = death.country$prediction.norm,
population.size = population.size)
l.u.int$lower <- norm.pop.size.inv(value = l.u.int$lower,
population.size = population.size)
l.u.int$upper <- norm.pop.size.inv(value = l.u.int$upper,
population.size = population.size)
### modify the values to have better rendering in ggplot graphics
death.country <- adjust.confint.4plot(l.u.int = l.u.int,
death.country = death.country,
y.lim.max = y.lim.max)
return(list(death.country = death.country,
confint.list = confint.list))
}
### normalisation number of death by population size
norm.pop.size <- function(death.country = NULL,
population.size = NULL)
{
### normalize with the popupalion size
population.scale <-
10 ^ 6 / population.size
if (is.na(population.scale))
{
stop("ERROR: population is not available")
} else
{
death.country$death.norm <-
death.country$death * population.scale
}
return(death.country)
}
### normalisation number of death by population size
norm.pop.size.inv <- function(value = NULL,
population.size = NULL)
{
### normalize with the popupalion size
population.scale <-
10 ^ 6 / population.size
if (is.na(population.scale))
{
stop("ERROR: population is not available")
} else
{
value <- floor(value / population.scale)
}
return(value)
}
### fit model
fitModel <- function(daily.cumulative.death = NULL,
country.population = NULL,
countries.of.interest = NULL,
confinement.date = NULL,
y.lim.max = 30000,
max.date.pred = as.Date("05/01/20", format = "%m/%d/%y"))
{
### max.date.pres = max date for prediction
### y.lim.max = maximal y value that will be displayed on the plot
### variable to store the prediction
death.prediction <- NULL