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newpoptime.R
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newpoptime.R
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devtools::load_all()
library(splines)
library(visreg)
# support data ------------------------------------------------------------
data("support")
mod_cb <- casebase::fitSmoothHazard(death ~ bs(d.time)*sex,
time = "d.time",
event = "death",
data = support,
ratio = 10)
summary(mod_cb)
hazardPlot(mod_cb, newdata = data.frame(sex = "male"), ci = F)
hazardPlot(mod_cb, newdata = data.frame(sex = "female"), add = T)
absoluteRisk()
visreg(mod_cb,
xvar = "d.time",
by = "sex",
scale = "response",
overlay = T,
# data = mod_cb$data,
plot = T)
visreg(mod_cb,
xvar = "sex",
by = "d.time",
type = "contrast",
scale = "response",
data = mod_cb$data,
plot = T)
mod_cb$data$offset <- 0
visreg(mod_cb,
xvar = "d.time",
by = "sex",
# scale = "linear",
scale = "response",
data = mod_cb$data,
plot = T)
tt$fit
tt$res
# xtrans = exp)
# simdat ------------------------------------------------------------------
devtools::load_all()
library(casebase)
library(splines)
library(visreg)
data("simdat")
mod_cb <- casebase::fitSmoothHazard(status ~ trt + ns(I(log(eventtime)), df = 3) +
trt:ns(I(log(eventtime)),df=1),
time = "eventtime",
data = simdat,
ratio = 10,
family = "glm")
results0 <- hazardPlot(object = mod_cb, newdata = data.frame(trt = 0),
ci.lvl = 0.95, ci = TRUE, lty = 1, line.col = 1, lwd = 2)
head(results0)
hazardPlot(object = mod_cb, newdata = data.frame(trt = 1), ci = TRUE,
ci.lvl = 0.95, add = TRUE, lty = 2, line.col = 2, lwd = 2)
legend("topleft", c("trt=0","trt=1"),lty=1:2,col=1:2,bty="y", lwd = 2)
mod_cb$data$offset <- 0
visreg(mod_cb,
xvar = "eventtime",
by = "trt",
scale = "response",
data = mod_cb$data,
plot = T)
data("bmtcrr")
head(bmtcrr)
str(bmtcrr)
data("support")
?support
popTimeData <- popTime(data = bmtcrr, time = "ftime", event = "Status", exposure = "D")
# popTimeData <- popTime(data = bmtcrr, time = "ftime", event = "Status")
data("ERSPC")
head(ERSPC)
library(splines)
devtools::load_all()
data("support")
mod_cb <- casebase::fitSmoothHazard(death ~ bs(d.time)+sex,
time = "d.time",
event = "death",
data = support,
ratio = 10)
summary(mod_cb)
support$sex
hazardPlot(mod_cb, newdata = data.frame(sex = "male"), ci = F)
hazardPlot(mod_cb, newdata = data.frame(sex = "female"), add = T)
pacman::p_load(effects)
ggeffects::ggpredict(mod_cb)
str(mod_cb)
head(mod_cb$data)
head(mod_cb$originalData)
# load packages and data ---------------------------------------------------
devtools::load_all("~/git_repositories/casebase/")
pacman::p_load(splines)
pacman::p_load(ggeffects)
pacman::p_load(visreg)
data("simdat")
head(simdat)
# Fit the model on simdat from casebase -----------------------------------
mod_cb <- casebase::fitSmoothHazard(status ~ bs(eventtime,degree = 8) * trt,
time = "eventtime",
event = "status",
data = simdat,
ratio = 10)
summary(mod_cb)
plot(mod_cb)
str(mod_cb)
mod_cb
# using casebase hazardPlot ------------------------------------------------
t1_hazardplot <- hazardPlot(object = mod_cb, newdata = data.frame(trt = 1),
ci.lvl = 0.95, ci = TRUE, lty = 1, line.col = 1, lwd = 2)
t0_hazardplot <- hazardPlot(object = mod_cb, newdata = data.frame(trt = 0), ci = TRUE,
ci.lvl = 0.95, add = TRUE, lty = 2, line.col = 2, lwd = 2)
legend("topleft", c("trt=1","trt=0"),lty=1:2,col=1:2,bty="y", lwd = 2)
# using visreg ------------------------------------------------------------
mod_cb$data$offset <- 0
tt <- visreg(mod_cb,
xvar = "eventtime",
by = "trt",
scale = "linear",
trans = exp,
data = mod_cb$data,
plot = T,
overlay = TRUE)
# get the predicted hazards by trt status and order the
# times so you can plot them
t1 <- tt$fit[which(tt$fit$trt==1),]
t0 <- tt$fit[which(tt$fit$trt==0),]
t1_visreg <- t1[order(t1$eventtime),]
t0_visreg <- t0[order(t1$eventtime),]
# ggpredict ---------------------------------------------
# this stops predictions at eventtimes <0.5
dat1 <- ggpredict(mod_cb, terms = c("eventtime [all]","trt","offset [0]"))
dat1$x %>% range
# this works, but produces slightly different results than visreg or hazardPlot
dat <- ggpredict(mod_cb, terms = c("eventtime [0:5 by=.1]","trt","offset [0]"))
plot(dat)
# get the predicted hazards by trt status. they are already in order
# wih ggpredict
t1_ggpredict <- dat$predicted[which(dat$group==1)]
t0_ggpredict <- dat$predicted[which(dat$group==0)]
# compare hazard ratios for treatment -----------------------------------------
plot(t0_hazardplot$eventtime, t1_hazardplot$predictedhazard / t0_hazardplot$predictedhazard,
type = "l", col = 1, lty = 1, ylab = "hazard ratio for trt=1 vs. trt=0", lwd = 2, ylim = c(0,1.1))
abline(a=1, b=0, col = "grey80")
lines(t0_visreg$eventtime, t1_visreg$visregFit / t0_visreg$visregFit,
type = "l", col = 2, lty = 2, lwd = 2)
lines(unique(dat$x), t1_ggpredict / t0_ggpredict,
type = "l", col = 3, lty = 3, lwd = 2)
legend("bottomright", c("hazardPlot","visreg","ggpredict"),
lty=1:3,col=1:3,bty="y", lwd = 2)
summary(mod_cb)
t1$eventtime == t0$eventtime
plot()
mod_cb$data <- mod_cb$originalData
plot(dat)
mod_cb$data %>% colnames()
mod_cb$originalData %>% colnames()
mod_cb$data <- mod_cb$originalData
pacman::p_load(visreg)
mod_cb$originalData$offset = mod_cb$offset[1]
visreg(mod_cb,
xvar = "d.time",
by = "sex",
scale = "linear",
data = mod_cb$originalData)#,
# xtrans = exp)
dev.off()
modcb$originalData %>% str
modcb$data <- modcb$originalData
modcb$data %>% str
modcb$originalData %>% str
plot(dat, facet = TRUE)
head(modcb$data)
head(modcb$originalData)
library(survival)
data(veteran)
head(veteran)
veteran$status %>% table
modcb <- fitSmoothHazard(status ~ ., data = veteran)
summary(modcb)
data("simdat")
library(splines)
mod_cb <- casebase::fitSmoothHazard(status ~ trt + ns(log(eventtime), df = 3) +
trt:ns(log(eventtime),df=1),
time = "eventtime",
data = simdat,
ratio = 100,
family = "glm")
results0 <- hazardPlot(object = mod_cb, newdata = data.frame(trt = 0),
ci.lvl = 0.95, ci = TRUE, lty = 1, line.col = 1, lwd = 2)
head(results0)
hazardPlot(object = mod_cb, newdata = data.frame(trt = 1), ci = TRUE,
ci.lvl = 0.95, add = TRUE, lty = 2, line.col = 2, lwd = 2)
legend("topleft", c("trt=0","trt=1"),lty=1:2,col=1:2,bty="y", lwd = 2)
summary(modcb)
hazardPlot(modcb, newdata = ERSPC)
?fitSmoothHazard
?hazardPlot
attr(popTimeData, "exposure")
attr(popTimeData, "call")
plot(popTimeData,
add.base.series = T,
comprisk = T,
add.competing.event = T,
ratio = 1,
legend = TRUE,
facet.params = list(ncol=1),
theme.params = list(legend.position = "top"),
casebase.theme = F
)
str(popTimeData)
library(casebase)
library(ggplot2)
ERSPC$DeadOfPrCa <- factor(ERSPC$DeadOfPrCa,
levels = c(0, 1),
labels = c("Censored", "PrCa Death"))
popTimeData <- popTime(data = ERSPC,
time = "Follow.Up.Time",
event = "DeadOfPrCa")
head(popTimeData)
p1 <- ggplot()
# doesnt work
nogo <- list(data = popTimeData[event == 1], mapping = aes(x = time, y = yc, colour = `event status`), colour = "green")
p1 + do.call("geom_point", nogo)
# works
works <- list(data = popTimeData[event == 1], mapping = aes(x = time, y = yc, colour = `event status`))
p1 + do.call("geom_point", works) + scale_color_manual(values = c("PrCa Death" = "blue"))
dim(popTimeData)
sampleCaseBase(popTimeData)
sampleData <- ERSPC %>%
arrange(desc(Follow.Up.Time)) %>%
mutate(id = row_number()) %>%
sampleCaseBase(time = "Follow.Up.Time",
event = "DeadOfPrCa",
comprisk = FALSE,
ratio = 1)
plot(popTimeData) + geom_point(aes(x = Follow.Up.Time, y = id),
data = filter(sampleData,
DeadOfPrCa == 0),
colour = "black",
size = 0.5, inherit.aes = FALSE)
huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron))
h <- ggplot(huron, aes(year))
h + geom_ribbon(aes(ymin=0, ymax=level))
popTimeData %>% head
huron %>% head
library(casebase)
library(ggplot2)
devtools::load_all()
ERSPC$ScrArm <- factor(ERSPC$ScrArm,
levels = c(0, 1),
labels = c("Control group", "Screening group"))
ERSPC$DeadOfPrCa <- factor(ERSPC$DeadOfPrCa,
levels = c(0, 1),
labels = c("Censored", "PrCa Death"))
popTimeData <- popTime(data = ERSPC,
time = "Follow.Up.Time",
event = "DeadOfPrCa")
plot(popTimeData)
devtools::load_all()
cols <- c("Case series" = "#D55E00", "Competing event" = "#009E73", "Base series" = "#0072B2")
plot(popTimeData,
casebase.theme = TRUE,
add.case.series = TRUE,
ratio = 1,
add.base.series = TRUE,
legend = TRUE,
base.params = list(show.legend = TRUE))#,
legend.params = list(name = element_blank(),
breaks = c("Case series", "Competing event", "Base series"),
values = cols))
# case.params = list(mapping = aes(x = time, y = yc, colour = "Relapse")),
# base.params = list(mapping = aes(x = time, y = ycoord, colour = "controls")),
# legend.params = list(breaks = c("Relapse", "controls"), values = c("Relapse" = "green","controls" = "red")))
popTimeData <- popTime(data = bmtcrr, time = "ftime", event = "Status")
cols <- c("Case series" = "black", "Competing event" = "#009E73", "Base series" = "#0072B2")
p1 <- plot(popTimeData,
casebase.theme = TRUE,
add.case.series = TRUE,
ratio = 1,
add.base.series = TRUE,
legend = TRUE,
comprisk = TRUE,
add.competing.event = FALSE,
legend.params = list(name = element_blank(),
breaks = c("Case series", "Competing event", "Base series"),
values = cols),
theme.params = list(legend.position = "none"))
dev.off()
p1
p1 + labs(caption = c("hellow worls"), title = "Poptime", x = "nine")
# case.params = list(mapping = aes(x = time, y = yc, colour = "Relapse")),
# base.params = list(mapping = aes(x = time, y = ycoord, colour = "controls")),
# legend.params = list(breaks = c("Relapse", "controls"), values = c("Relapse" = "green","controls" = "red")))
h <- ggplot(popTimeData)
devtools::load_all()
# library(casebase)
popTimeData <- popTime(data = bmtcrr, time = "ftime", event = "Status")
plot(popTimeData,
add.case.series = TRUE,
add.base.series = TRUE,
add.competing.event = FALSE,
comprisk = TRUE,
# case.params = list(show.legend = TRUE),
ratio = 1,
legend = T,
casebase.theme = TRUE,
theme.params = list(legend.position = "bottom"))
bt <- data.table::as.data.table(bmtcrr)
popTimeData <- popTime(data = bt, time = "ftime", event = "Status")
head(popTimeData)
rlang::last_error()
?popTime
head(popTimeData)
popTimeData$comprisk.event <- 0
popTimeData$comprisk.event[popTimeData$event == 1] <- 2
popTimeData$comprisk.event[popTimeData$event == 2] <- 1
table(popTimeData$comprisk.event, popTimeData$event)
str(popTimeData)
popTime(data = popTimeData, time = "time", event = "comprisk.event")
# Ribbon ------------------------------------------------------------------
h + geom_ribbon(aes(x = time, ymin=0, ymax=ycoord)) +
theme_minimal()
pdf("geom_ribbon.pdf")
h + geom_ribbon(aes(x = time, ymin=0, ymax=ycoord)) +
theme_minimal()
dev.off()
# Segment -----------------------------------------------------------------
h + geom_segment(aes(x = 0, xend = time, y = ycoord, yend = ycoord)) +
theme_minimal()
pdf("geom_segment.pdf")
h + geom_segment(aes(x = 0, xend = time, y = ycoord, yend = ycoord)) +
theme_minimal()
dev.off()
# File size in kB ---------------------------------------------------------
file.info("geom_ribbon.pdf")$size/1000
file.info("geom_segment.pdf")$size/1000
x_var <- "displ"
aes(x_var)
aes_(quote(displ), quote(ty), quote(uu))
#> Aesthetic mapping:
#> * `x` -> `displ`
aes_(as.name(x_var))
#> Aesthetic mapping:
#> * `x` -> `displ`
aes_(parse(text = x_var)[[1]])
#> Aesthetic mapping:
#> * `x` -> `displ`
f <- function(x_var) {
aes_(substitute(x_var))
}
f(displ)
#> Aesthetic mapping:
#> * `x` -> `displ`
library(ggplot2)
mtcars$wt2 <- mtcars$wt*0.9
mtcars$mpg2 <- mtcars$mpg*0.9
mp <- list(size = 3, color = "red", pch = 10, aes(x = wt2, y = mpg2))
add <- FALSE
tt <- list(
if (add)
do.call("geom_point",mp)
)
p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point() + tt
library(magrittr)
do.call("geom_point",mp)
cols <- c("LINE1"="#f04546","LINE2"="#3591d1","BAR"="#62c76b")
a <-c("S1","S2","S3","S4","S5","S6","S7","S8","S9") #names
b <-c(0.23,0.26,0.55,0.56,0.36,0.23,0.18,0.06,0.04) #mean t0
c <-c(0.64,0.6,0.81,1.4,0.89,0.55,0.48,0.22,0.09) #mean t1
d <-c(0.20,0.23,0.52,0.53,0.33,0.20,0.15,0.04,0.03) #SD low t0
e <-c(0.26,0.29,0.58,.59,0.39,0.26,0.21,0.08,0.05) #SD high t0
f <-c(0.67,0.63,0.86,1.44,0.93,0.59,0.51,0.25,0.10) #SD high t1
g <-c(0.61,0.57,0.78,1.36,0.85,0.53,0.45,0.19,0.08) #SD low t1
h <-c(0.41,0.34,0.26,0.84,0.53,0.32,0.30,0.16,0.05) #absolute change
data <- data.frame(a,b,c,d,e,f,g,h)
ggplot(data=data,aes(x=a)) +
geom_bar(stat="identity", aes(y=h, fill = "BAR"),colour="#333333")+ #green
geom_line(aes(y=b,group=1, colour="LINE1"),size=1.0) + #red
geom_point(aes(y=b, colour="LINE1"),size=3) + #red
geom_errorbar(aes(ymin=d, ymax=e, colour="LINE1"), width=0.1, size=.8) +
geom_line(aes(y=c,group=1,colour="LINE2"),size=1.0) + #blue
geom_point(aes(y=c,colour="LINE2"),size=3) + #blue
geom_errorbar(aes(ymin=f, ymax=g,colour="LINE2"), width=0.1, size=.8) +
scale_colour_manual(name="Error Bars",values=cols) + scale_fill_manual(name="Bar",values=cols) +
ylab("Symptom severity") + xlab("PHQ-9 symptoms") +
ylim(0,1.6) +
theme_bw() +
theme(axis.title.x = element_text(size = 15, vjust=-.2)) +
theme(axis.title.y = element_text(size = 15, vjust=0.3))
set.seed(7)
df <- data.frame(
x = 1:20,
y.bar = rpois(20, lambda = 5),
y.line = rpois(20, lambda = 10)
)
ggplot(data = df,
mapping = aes(x = x)) +
# specify fill for bar / color for line inside aes(); you can use
# whatever label you wish to appear in the legend
geom_col(aes(y = y.bar, fill = "bar.label")) +
geom_line(aes(y = y.line, color = "line.label")) +
xlab("Month of year") +
scale_y_continuous(name = "Daily classifications per Spotter") +
# the labels must match what you specified above
scale_fill_manual(name = "", values = c("bar.label" = "grey")) +
scale_color_manual(name = "", values = c("line.label" = "black")) +
theme_bw()