Related
Consider the following simple example:
# E. Musk in Grunheide
set.seed(22032022)
# generate random numbers
randomNumbers <- rnorm(n = 1000, mean = 10, sd = 10)
# empirical sd
sd(randomNumbers)
#> [1] 10.34369
# histogram
hist(randomNumbers, probability = TRUE, main = "", breaks = 50)
# just for illusatration purpose
###
# empirical density
lines(density(randomNumbers), col = 'black', lwd = 2)
# theortical density
curve(dnorm(x, mean = 10, sd = 10), col = "blue", lwd = 2, add = TRUE)
###
Created on 2022-03-22 by the reprex package (v2.0.1)
Question:
Is there a nice way to illustrate the empirical standard deviation (sd) in the histogram by colour?
E.g. representing the inner bars by a different color, or indicating the range of the sd by an interval, i.e., [mean +/- sd], on the x-axis?
Note, if ggplot2 provides an easy solution, suggesting this would be also much appreciated.
This is similar ggplot solution to Benson's answer, except we precompute the histogram and use geom_col, so that we don't get any of the unwelcome stacking at the sd boundary:
# E. Musk in Grunheide
set.seed(22032022)
# generate random numbers
randomNumbers <- rnorm(n=1000, mean=10, sd=10)
h <- hist(randomNumbers, breaks = 50, plot = FALSE)
lower <- mean(randomNumbers) - sd(randomNumbers)
upper <- mean(randomNumbers) + sd(randomNumbers)
df <- data.frame(x = h$mids, y = h$density,
fill = h$mids > lower & h$mids < upper)
library(ggplot2)
ggplot(df) +
geom_col(aes(x, y, fill = fill), width = 1, color = 'black') +
geom_density(data = data.frame(x = randomNumbers),
aes(x = x, color = 'Actual density'),
key_glyph = 'path') +
geom_function(fun = function(x) {
dnorm(x, mean = mean(randomNumbers), sd = sd(randomNumbers)) },
aes(color = 'theoretical density')) +
scale_fill_manual(values = c(`TRUE` = '#FF374A', 'FALSE' = 'gray'),
name = 'within 1 SD') +
scale_color_manual(values = c('black', 'blue'), name = 'Density lines') +
labs(x = 'Value of random number', y = 'Density') +
theme_minimal()
Here is a ggplot solution. First calculate mean and sd, and save the values in different vectors. Then use an ifelse statement to categorise the values into "Within range" and "Outside range", fill them with different colours.
Blue line represents the normal distribution stated in your question, and black line represents the density graph of the histogram we're plotting.
library(ggplot2)
set.seed(22032022)
# generate random numbers
randomNumbers <- rnorm(n=1000, mean=10, sd=10)
randomNumbers_mean <- mean(randomNumbers)
randomNumbers_sd <- sd(randomNumbers)
ggplot(data.frame(randomNumbers = randomNumbers), aes(randomNumbers)) +
geom_histogram(aes(
fill = ifelse(
randomNumbers > randomNumbers_mean + randomNumbers_sd |
randomNumbers < randomNumbers_mean - randomNumbers_sd,
"Outside range",
"Within range"
)
),
binwidth = 1, col = "gray") +
geom_density(aes(y = ..count..)) +
stat_function(fun = function(x) dnorm(x, mean = 10, sd = 10) * 1000,
color = "blue") +
labs(fill = "Data")
Created on 2022-03-22 by the reprex package (v2.0.1)
data.frame(rand = randomNumbers,
cut = {
sd <- sd(randomNumbers)
mn <- mean(randomNumbers)
cut(randomNumbers, c(-Inf, mn -sd, mn +sd, Inf))
}) |>
ggplot(aes(x = rand, fill = cut ) ) +
geom_histogram()
I have a large dataset of gene expression from ~10,000 patient samples (TCGA), and I'm plotting a predicted expression value (x) and the actual observed value (y) of a certain gene signature. For my downstream analysis, I need to draw a precise line through the plot and calculate different parameters in samples above/below the line.
No matter how I draw a line through the data (geom_smooth(method = 'lm', 'glm', 'gam', or 'loess')), the line always seems imperfect - it doesn't cut through the data to my liking (red line is lm in figure).
After playing around for a while, I realized that the 2d kernel density lines (geom_density2d) actually do a good job of showing the slope/trends of my data, so I manually drew a line that kind of cuts through the density lines (black line in figure).
My question: how can I automatically draw a line that cuts through the kernel density lines, as for the black line in the figure? (Rather than manually playing with different intercepts and slopes till something looks good).
The best approach I can think of is to somehow calculate intercept and slope of the longest diameter for each of the kernel lines, take an average of all those intercepts and slopes and plot that line, but that's a bit out of my league. Maybe someone here has experience with this and can help?
A more hacky approach may be getting the x,y coords of each kernel density line from ggplot_build, and going from there, but it feels too hacky (and is also out of my league).
Thanks!
EDIT: Changed a few details to make the figure/analysis easier. (Density lines are smoother now).
Reprex:
library(MASS)
set.seed(123)
samples <- 10000
r <- 0.9
data <- mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(2, r, r, 2), nrow=2))
x <- data[, 1] # standard normal (mu=0, sd=1)
y <- data[, 2] # standard normal (mu=0, sd=1)
test.df <- data.frame(x = x, y = y)
lm(y ~ x, test.df)
ggplot(test.df, aes(x, y)) +
geom_point(color = 'grey') +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = c(2,2)) + ### EDIT: h = c(2,2)
geom_smooth(method = "glm", se = F, lwd = 1, color = 'red') +
geom_abline(intercept = 0, slope = 0.7, lwd = 1, col = 'black') ## EDIT: slope to 0.7
Figure:
I generally agree with #Hack-R.
However, it was kind of a fun problem and looking into ggplot_build is not such a big deal.
require(dplyr)
require(ggplot2)
p <- ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = c(2,2))
#basic version of your plot
p_built <- ggplot_build(p)
p_data <- p_built$data[[1]]
p_maxring <- p_data[p_data[['level']] == min(p_data[['level']]),] %>%
select(x,y) # extracts the x/y coordinates of the points on the largest ellipse from your 2d-density contour
Now this answer helped me to find the points on this ellipse which are furthest apart.
coord_mean <- c(x = mean(p_maxring$x), y = mean(p_maxring$y))
p_maxring <- p_maxring %>%
mutate (mean_dev = sqrt((x - mean(x))^2 + (y - mean(y))^2)) #extra column specifying the distance of each point to the mean of those points
coord_farthest <- c('x' = p_maxring$x[which.max(p_maxring$mean_dev)], 'y' = p_maxring$y[which.max(p_maxring$mean_dev)])
# gives the coordinates of the point farthest away from the mean point
farthest_from_farthest <- sqrt((p_maxring$x - coord_farthest['x'])^2 + (p_maxring$y - coord_farthest['y'])^2)
#now this looks which of the points is the farthest from the point farthest from the mean point :D
coord_fff <- c('x' = p_maxring$x[which.max(farthest_from_farthest)], 'y' = p_maxring$y[which.max(farthest_from_farthest)])
ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = c(2,2)) +
# geom_segment using the coordinates of the points farthest apart
geom_segment((aes(x = coord_farthest['x'], y = coord_farthest['y'],
xend = coord_fff['x'], yend = coord_fff['y']))) +
geom_smooth(method = "glm", se = F, lwd = 1, color = 'red') +
# as per your request with your geom_smooth line
coord_equal()
coord_equal is super important, because otherwise you will get super weird results - it messed up my brain too. Because if the coordinates are not set equal, the line will seemingly not pass through the point furthest apart from the mean...
I leave it to you to build this into a function in order to automate it. Also, I'll leave it to you to calculate the y-intercept and slope from the two points
Tjebo's approach was kind of good initially, but after a close look, I found that it found the longest distance between two points on an ellipse. While this is close to what I wanted, it failed with either an irregular shape of the ellipse, or the sparsity of points in the ellipse. This is because it measured the longest distance between two points; whereas what I really wanted is the longest diameter of an ellipse; i.e.: the semi-major axis. See image below for examples/details.
Briefly:
To find/draw density contours of specific density/percentage:
R - How to find points within specific Contour
To get the longest diameter ("semi-major axis") of an ellipse:
https://stackoverflow.com/a/18278767/3579613
For function that returns intercept and slope (as in OP), see last piece of code.
The two pieces of code and images below compare two Tjebo's approach vs. my new approach based on the above posts.
#### Reprex from OP
require(dplyr)
require(ggplot2)
require(MASS)
set.seed(123)
samples <- 10000
r <- 0.9
data <- mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(2, r, r, 2), nrow=2))
x <- data[, 1] # standard normal (mu=0, sd=1)
y <- data[, 2] # standard normal (mu=0, sd=1)
test.df <- data.frame(x = x, y = y)
#### From Tjebo
p <- ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = 2)
p_built <- ggplot_build(p)
p_data <- p_built$data[[1]]
p_maxring <- p_data[p_data[['level']] == min(p_data[['level']]),][,2:3]
coord_mean <- c(x = mean(p_maxring$x), y = mean(p_maxring$y))
p_maxring <- p_maxring %>%
mutate (mean_dev = sqrt((x - mean(x))^2 + (y - mean(y))^2)) #extra column specifying the distance of each point to the mean of those points
p_maxring = p_maxring[round(seq(1, nrow(p_maxring), nrow(p_maxring)/23)),] #### Make a small ellipse to illustrate flaws of approach
coord_farthest <- c('x' = p_maxring$x[which.max(p_maxring$mean_dev)], 'y' = p_maxring$y[which.max(p_maxring$mean_dev)])
# gives the coordinates of the point farthest away from the mean point
farthest_from_farthest <- sqrt((p_maxring$x - coord_farthest['x'])^2 + (p_maxring$y - coord_farthest['y'])^2)
#now this looks which of the points is the farthest from the point farthest from the mean point :D
coord_fff <- c('x' = p_maxring$x[which.max(farthest_from_farthest)], 'y' = p_maxring$y[which.max(farthest_from_farthest)])
farthest_2_points = data.frame(t(cbind(coord_farthest, coord_fff)))
plot(p_maxring[,1:2], asp=1)
lines(farthest_2_points, col = 'blue', lwd = 2)
#### From answer in another post
d = cbind(p_maxring[,1], p_maxring[,2])
r = ellipsoidhull(d)
exy = predict(r) ## the ellipsoid boundary
lines(exy)
me = colMeans((exy))
dist2center = sqrt(rowSums((t(t(exy)-me))^2))
max(dist2center) ## major axis
lines(exy[dist2center == max(dist2center),], col = 'red', lwd = 2)
#### The plot here is made from the data in the reprex in OP, but with h = 0.5
library(MASS)
set.seed(123)
samples <- 10000
r <- 0.9
data <- mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(2, r, r, 2), nrow=2))
x <- data[, 1] # standard normal (mu=0, sd=1)
y <- data[, 2] # standard normal (mu=0, sd=1)
test.df <- data.frame(x = x, y = y)
## MAKE BLUE LINE
p <- ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = 0.5) ## NOTE h = 0.5
p_built <- ggplot_build(p)
p_data <- p_built$data[[1]]
p_maxring <- p_data[p_data[['level']] == min(p_data[['level']]),][,2:3]
coord_mean <- c(x = mean(p_maxring$x), y = mean(p_maxring$y))
p_maxring <- p_maxring %>%
mutate (mean_dev = sqrt((x - mean(x))^2 + (y - mean(y))^2))
coord_farthest <- c('x' = p_maxring$x[which.max(p_maxring$mean_dev)], 'y' = p_maxring$y[which.max(p_maxring$mean_dev)])
farthest_from_farthest <- sqrt((p_maxring$x - coord_farthest['x'])^2 + (p_maxring$y - coord_farthest['y'])^2)
coord_fff <- c('x' = p_maxring$x[which.max(farthest_from_farthest)], 'y' = p_maxring$y[which.max(farthest_from_farthest)])
## MAKE RED LINE
## h = 0.5
## Given the highly irregular shape of the contours, I will use only the largest contour line (0.95) for draing the line.
## Thus, average = 1. See function below for details.
ln = long.diam("x", "y", test.df, h = 0.5, average = 1) ## NOTE h = 0.5
## PLOT
ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = 0.5) + ## NOTE h = 0.5
geom_segment((aes(x = coord_farthest['x'], y = coord_farthest['y'],
xend = coord_fff['x'], yend = coord_fff['y'])), col = 'blue', lwd = 2) +
geom_abline(intercept = ln[1], slope = ln[2], color = 'red', lwd = 2) +
coord_equal()
Finally, I came up with the following function to deal with all this. Sorry for the lack of comments/clarity
#### This will return the intercept and slope of the longest diameter (semi-major axis).
####If Average = TRUE, it will average the int and slope across different density contours.
long.diam = function(x, y, df, probs = c(0.95, 0.5, 0.1), average = T, h = 2) {
fun.df = data.frame(cbind(df[,x], df[,y]))
colnames(fun.df) = c("x", "y")
dens = kde2d(fun.df$x, fun.df$y, n = 200, h = h)
dx <- diff(dens$x[1:2])
dy <- diff(dens$y[1:2])
sz <- sort(dens$z)
c1 <- cumsum(sz) * dx * dy
levels <- sapply(probs, function(x) {
approx(c1, sz, xout = 1 - x)$y
})
names(levels) = paste0("L", str_sub(formatC(probs, 2, format = 'f'), -2))
#plot(fun.df$x,fun.df$y, asp = 1)
#contour(dens, levels = levels, labels=probs, add=T, col = c('red', 'blue', 'green'), lwd = 2)
#contour(dens, add = T, col = 'red', lwd = 2)
#abline(lm(fun.df$y~fun.df$x))
ls <- contourLines(dens, levels = levels)
names(ls) = names(levels)
lines.info = list()
for (i in 1:length(ls)) {
d = cbind(ls[[i]]$x, ls[[i]]$y)
exy = predict(ellipsoidhull(d))## the ellipsoid boundary
colnames(exy) = c("x", "y")
me = colMeans((exy)) ## center of the ellipse
dist2center = sqrt(rowSums((t(t(exy)-me))^2))
#plot(exy,type='l',asp=1)
#points(d,col='blue')
#lines(exy[order(dist2center)[1:2],])
#lines(exy[rev(order(dist2center))[1:2],])
max.dist = data.frame(exy[rev(order(dist2center))[1:2],])
line.fit = lm(max.dist$y ~ max.dist$x)
lines.info[[i]] = c(as.numeric(line.fit$coefficients[1]), as.numeric(line.fit$coefficients[2]))
}
names(lines.info) = names(ls)
#plot(fun.df$x,fun.df$y, asp = 1)
#contour(dens, levels = levels, labels=probs, add=T, col = c('red', 'blue', 'green'), lwd = 2)
#abline(lines.info[[1]], col = 'red', lwd = 2)
#abline(lines.info[[2]], col = 'blue', lwd = 2)
#abline(lines.info[[3]], col = 'green', lwd = 2)
#abline(apply(simplify2array(lines.info), 1, mean), col = 'black', lwd = 4)
if (isTRUE(average)) {
apply(simplify2array(lines.info), 1, mean)
} else {
lines.info[[average]]
}
}
Finally, here's the final implementation of the different answers:
library(MASS)
set.seed(123)
samples = 10000
r = 0.9
data = mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(2, r, r, 2), nrow=2))
x = data[, 1] # standard normal (mu=0, sd=1)
y = data[, 2] # standard normal (mu=0, sd=1)
#plot(x, y)
test.df = data.frame(x = x, y = y)
#### Find furthest two points of contour
## BLUE
p <- ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 2, contour = T, h = 2)
p_built <- ggplot_build(p)
p_data <- p_built$data[[1]]
p_maxring <- p_data[p_data[['level']] == min(p_data[['level']]),][,2:3]
coord_mean <- c(x = mean(p_maxring$x), y = mean(p_maxring$y))
p_maxring <- p_maxring %>%
mutate (mean_dev = sqrt((x - mean(x))^2 + (y - mean(y))^2))
coord_farthest <- c('x' = p_maxring$x[which.max(p_maxring$mean_dev)], 'y' = p_maxring$y[which.max(p_maxring$mean_dev)])
farthest_from_farthest <- sqrt((p_maxring$x - coord_farthest['x'])^2 + (p_maxring$y - coord_farthest['y'])^2)
coord_fff <- c('x' = p_maxring$x[which.max(farthest_from_farthest)], 'y' = p_maxring$y[which.max(farthest_from_farthest)])
#### Find the average intercept and slope of 3 contour lines (0.95, 0.5, 0.1), as in my long.diam function above.
## RED
ln = long.diam("x", "y", test.df)
#### Plot everything. Black line is GLM
ggplot(test.df, aes(x, y)) +
geom_point(color = 'grey') +
geom_density2d(color = 'red', lwd = 1, contour = T, h = 2) +
geom_smooth(method = "glm", se = F, lwd = 1, color = 'black') +
geom_abline(intercept = ln[1], slope = ln[2], col = 'red', lwd = 1) +
geom_segment((aes(x = coord_farthest['x'], y = coord_farthest['y'],
xend = coord_fff['x'], yend = coord_fff['y'])), col = 'blue', lwd = 1) +
coord_equal()
I have the following example:
require(mvtnorm)
require(ggplot2)
set.seed(1234)
xx <- data.frame(rmvt(100, df = c(13, 13)))
ggplot(data = xx, aes(x = X1, y= X2)) + geom_point() + geom_density2d()
Here is what I get:
However, I would like to get the density contour from the mutlivariate t density given by the dmvt function. How do I tweak geom_density2d to do that?
This is not an easy question to answer: because the contours need to be calculated and the ellipse drawn using the ellipse package.
Done with elliptical t-densities to illustrate the plotting better.
nu <- 5 ## this is the degrees of freedom of the multivariate t.
library(mvtnorm)
library(ggplot2)
sig <- matrix(c(1, 0.5, 0.5, 1), ncol = 2) ## this is the sigma parameter for the multivariate t
xx <- data.frame( rmvt(n = 100, df = c(nu, nu), sigma = sig)) ## generating the original sample
rtsq <- rowSums(x = matrix(rt(n = 2e6, df = nu)^2, ncol = 2)) ## generating the sample for the ellipse-quantiles. Note that this is a cumbersome calculation because it is the sum of two independent t-squared random variables with the same degrees of freedom so I am using simulation to get the quantiles. This is the sample from which I will create the quantiles.
g <- ggplot( data = xx
, aes( x = X1
, y = X2
)
) + geom_point(colour = "red", size = 2) ## initial setup
library(ellipse)
for (i in seq(from = 0.01, to = 0.99, length.out = 20)) {
el.df <- data.frame(ellipse(x = sig, t = sqrt(quantile(rtsq, probs = i)))) ## create the data for the given quantile of the ellipse.
names(el.df) <- c("x", "y")
g <- g + geom_polygon(data=el.df, aes(x=x, y=y), fill = NA, linetype=1, colour = "blue") ## plot the ellipse
}
g + theme_bw()
This yields:
I still have a question: how does one reduce the size of the plotting ellispe lines?
Could someone show me how to fit a polynomial marginal distribution to my data? I have done a binomial and beta binomial, but I would like to see how to fit a polynomial. I would also be interested in trying a gamma if that is something you know how to do.
This is what I have done so far.
nodes <- read.table("https://web.stanford.edu/~hastie/CASI_files/DATA/nodes.txt",
header = T)
nodes %>%
ggplot(aes(x=x/n))+
geom_histogram(bins = 30)+
theme_bw()+
labs(x = "nodes",
n = "p=x/n")
# log-likelihood function
ll <- function(alpha, beta) {
x <- nodes$x
total <- nodes$n
-sum(VGAM::dbetabinom.ab(x, total, alpha, beta, log = TRUE))
}
# maximum likelihood estimation
m <- mle(ll, start = list(alpha = 1, beta = 10), method = "L-BFGS-B",
lower = c(0.0001, .1))
ab <- coef(m)
alpha0 <- ab[1]
beta0 <- ab[2]
nodes %>%
ggplot() +
geom_histogram(aes(x/n, y = ..density..), bins= 30) +
stat_function(fun = function(x) dbeta(x, alpha0, beta0), color = "red",
size = 1) +
xlab("p=x/n")
Here is another fit
ll <- function(a){
x <- nodes$x
total <- nodes$n
-sum(stats::dbinom(x, total, a, log = TRUE))
}
#stats::dbinom()
m <- mle(ll, start = list(a=.5), method = "L-BFGS-B",
lower = c(0.0001, .1))
a = coef(m)
nodes %>%
ggplot() +
geom_histogram(aes(x/n, y = ..density..), bins=40) +
stat_function(fun = function(x) dbeta(x, a, 1), color = "red",
size = 1) +
xlab("proportion x/n")
For fitting a gamma distribution:
data(iris)
library(MASS) ##for the fitdistr function
fit.params <- fitdistr(iris$Sepal.Length, "gamma", lower = c(0, 0))
ggplot(data = iris) +
geom_histogram(data = as.data.frame(x), aes(x=iris$Sepal.Length, y=..density..)) +
geom_line(aes(x=iris$Sepal.Length,
y=dgamma(iris$Sepal.Length,fit.params$estimate["shape"],
fit.params$estimate["rate"])), color="red", size = 1) +
theme_classic()
You might also like to take a look at the distribution of the quantiles using the qqp function in the car package. Here are a few examples:
library(car)
qqp(iris$Sepal.Length, "norm") ##normal distribution
qqp(iris$Sepal.Length, "lnorm") ##log-normal distribution
gamma <- fitdistr(iris$Sepal.Length, "gamma")
qqp(iris$Sepal.Length, "gamma", shape = gamma$estimate[[1]],
rate = gamma$estimate[[2]]) ##gamma distribution
nbinom <- fitdistr(iris$Sepal.Length, "Negative Binomial")
qqp(iris$Sepal.Length, "nbinom", size = nbinom$estimate[[1]],
mu = nbinom$estimate[[2]]) ##negative binomial distribution
You can use the fitdistr function for ggplots or qqPlots. It supports lots of different distributions. Take a look at ?fitdistr
I would like to fit a weibull curve to some event data and then include the fitted weibull curve in a survival plot plotted by survminer::ggsurvplot. Any ideas of how?
Here is an example to work on:
A function for simulating weibull data:
# N = sample size
# lambda = scale parameter in h0()
# rho = shape parameter in h0()
# beta = fixed effect parameter
# rateC = rate parameter of the exponential distribution of C
simulWeib <- function(N, lambda, rho, beta, rateC)
{
# covariate --> N Bernoulli trials
x <- sample(x=c(0, 1), size=N, replace=TRUE, prob=c(0.5, 0.5))
# Weibull latent event times
v <- runif(n=N)
Tlat <- (- log(v) / (lambda * exp(x * beta)))^(1 / rho)
# censoring times
C <- rexp(n=N, rate=rateC)
# follow-up times and event indicators
time <- pmin(Tlat, C)
status <- as.numeric(Tlat <= C)
# data set
data.frame(id=1:N,
time=time,
status=status,
x=x)
}
generate data
set.seed(1234)
betaHat <- rep(NA, 1e3)
for(k in 1:1e3)
{
dat <- simulWeib(N=100, lambda=0.01, rho=1, beta=-0.6, rateC=0.001)
fit <- coxph(Surv(time, status) ~ x, data=dat)
betaHat[k] <- fit$coef
}
#Estimate a survival function
survfit(Surv(as.numeric(time), x)~1, data=dat) -> out0
#plot
library(survminer)
ggsurvplot(out0, data = dat, risk.table = TRUE)
gg1 <- ggsurvplot(
out0, # survfit object with calculated statistics.
data = dat, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimaes of survival curves.
xlim = c(0,2000), # present narrower X axis, but not affect
# survival estimates.
break.time.by = 500, # break X axis in time intervals by 500.
ggtheme = theme_minimal(), # customize plot and risk table with a theme.
risk.table.y.text.col = T, # colour risk table text annotations.
risk.table.y.text = FALSE,
surv.median.line = "hv",
color = "darkgreen",
conf.int.fill = "lightblue",
title = "Survival probability",# show bars instead of names in text annotations
# in legend of risk table
)
gg1
As far as I see this, it is not possible do it with ggsurvplot at this moment.
I created an issue requesting this feature: https://github.com/kassambara/survminer/issues/276
You can plot survivor curves of a weibull model with ggplot2 like this:
library("survival")
wbmod <- survreg(Surv(time, status) ~ x, data = dat)
s <- seq(.01, .99, by = .01)
t_0 <- predict(wbmod, newdata = data.frame(x = 0),
type = "quantile", p = s)
t_1 <- predict(wbmod, newdata = data.frame(x = 1),
type = "quantile", p = s)
smod <- data.frame(time = c(t_0, t_1),
surv = rep(1 - s, times = 2),
strata = rep(c(0, 1), each = length(s)),
upper = NA, lower = NA)
head(surv_summary(cm))
library("ggplot2")
ggplot() +
geom_line(data = smod, aes(x = time, y = surv, color = factor(strata))) +
theme_classic()
However to my knowledge you cannot use survminer (yet):
library("survminer")
# wrong:
ggsurvplot(smod)
# does not work:
gg1$plot + geom_line(data = smod, aes(x = time, y = surv, color = factor(strata)))
The following works for me. Probably the credit goes to Heidi filling a feature request.
Hope, someone finds this useful.
library(survminer)
library(tidyr)
s <- with(lung,Surv(time,status))
sWei <- survreg(s ~ as.factor(sex),dist='weibull',data=lung)
fKM <- survfit(s ~ sex,data=lung)
pred.sex1 = predict(sWei, newdata=list(sex=1),type="quantile",p=seq(.01,.99,by=.01))
pred.sex2 = predict(sWei, newdata=list(sex=2),type="quantile",p=seq(.01,.99,by=.01))
df = data.frame(y=seq(.99,.01,by=-.01), sex1=pred.sex1, sex2=pred.sex2)
df_long = gather(df, key= "sex", value="time", -y)
p = ggsurvplot(fKM, data = lung, risk.table = T)
p$plot = p$plot + geom_line(data=df_long, aes(x=time, y=y, group=sex))