L1mstate (version 1.0.1)

plot.probs.l1mstate: Plot the transition probabilities

Description

Plot the transition probabilities produced by probs.l1mstate

Usage

# S3 method for probs.l1mstate
plot(x,from,type=c("stacked","filled","single","separate"),
     ord,cols,xlab="Years since transplant",ylab="Probability",
     xlim,ylim,lwd,lty,cex,legend,legend.pos,bty="o",...)

Arguments

x

object produced from probs.l1mstate

from

the starting state from which the probabilities are used to plot

type

with "stacked" type, the transition probabilities are stacked and the distance between two adjacent curves indicates the probability; with "filled" type, it is the same but the space between adjacent curves are filled; with "single" type, the probabilities are shown as different curves in a single plot; with "separate" type, separate plots are shown for the estimated transition probabilities

ord

A vector indicates the order of plotting in case "stacked" or "filled"

cols

A vector specifying colors for the different transitions

xlab

A title for the x-axis; default is "Years since transplant"

ylab

A title for the y-axis; default is "Probability"

xlim

The x limits of the plot; default is range of time

ylim

The y limits of the plot

lwd

The line width; default is 3

lty

The line type

cex

Character size

legend

The transition numbers; if missing, these will be taken from the transition matrix contained in cumhaz.l1mstate object.

legend.pos

The position of the legend; default is "topleft"

bty

The box type of the legend

…

Further arguments to plot

Value

No return value.

Details

A plot is produced, and nothing is returned.

Examples

# NOT RUN {
library(L1mstate)
set.seed(1001)

N <- 200

x1 <- rbinom(N, 1, .5)
x2 <- rbinom(N, 1, .5)
x3 <- rbinom(N, 1, .5)
x4 <- rbinom(N, 1, .5)
x5 <- rbinom(N, 1, .5)
x6 <- rbinom(N, 1, .5)
x <-  data.frame(x1,x2,x3,x4,x5,x6)
col_names  <-  c(sprintf("X%d", seq(1,6)))
colnames(x)  <-  col_names

beta12 <- c(-.65,-.65,-.65,0,0,0)
beta13 <- c(-.65,0,0,0,-.65,0) 
beta23 <- c(0,-.65,-.65,0,0,-.65)

N12  <-  N-2*N%/%5
N13 <- N-N12
hx12 <- exp(as.matrix(x[1:N12,]) %*% beta12) 
ty12 <- rexp(N12,hx12) 
hx13 <- exp(as.matrix(x[(N12+1):N,]) %*% beta13) 
ty13 <- rexp(N13,hx13)
hx23 <- exp(as.matrix(x[1:N12,]) %*% beta23) 
ty23 <- rexp(N12,hx23) 
t2 <- rep(0,N)
t2[1:N12]  <-  ty12
t2[(N12+1):N]  <-  ty13
s2  <-  rep(0,N)
s2[1:N12] <-  rep(1,N12)
t3 <- rep(0,N)
t3[1:N12]  <-  ty12+ty23
t3[(N12+1):N]  <-  ty13
#censor case for state 3
tcens3 <- rbinom(n=N, prob = 0.3, size = 1)
s3 <- 1-tcens3

# pre-process data into long-format using msprep
dt <- data.frame(illt=t2, ills=s2, dt=t3, ds=s3, x)
tmat <- matrix(c(NA,NA,NA,1,NA,NA,2,3,NA), nrow = 3)
longdt <- msprep(time=c(NA,"illt","dt"),status=c(NA,"ills","ds"),
                 keep = col_names, data=dt,trans=tmat)

out <- cv.l1mstateR(longdt, nlambda = 100, nfolds = 10)

ptsA <- longdt[which(longdt$X1==1 & longdt$X2==0 & longdt$X3==1 & longdt$X4==0 
               & longdt$X5==1 & longdt$X6==1),]
## observed transitions (ground truth)
# predicted time = 0
events(ptsA)
# use models to predict the transition probabilities
# L1MSTATE
ptA <- ptsA[which(ptsA$id == unique(ptsA$id)[1]),]
ptA <- ptA[,c(4,9:14)]

cumhazA <- cumhaz.l1mstate(object=out, longdt=longdt, newdata=ptA, cv.method="pcvl")
probA_0 <- probs.l1mstate(cumhazA, longdt = longdt, tmat, predt = 0, direction = "forward")
statecols <- heat_hcl(6, c = c(90, 10), l = c(20, 80), power = c(1/5, 2))[c(6, 5, 3, 4, 2, 1)]
ord <- c(1, 2, 3, 4, 5, 6)
plot.probs.l1mstate(probA_0, main = "L1MSTATE", from = 1, ord = ord,
                    las=1, cex=0, type = "filled", col = statecols[ord])
# }