VEplot | R Documentation |
Vaccine efficacy plots.
VEplot (object, ...)
## S3 method for class 'cox.zph'
VEplot(object, resid = TRUE, se = TRUE, df = 4, nsmo = 40,
var, ylab="VE", xlab="Time", xaxt="s", cex.axis=1, ...)
## S3 method for class 'glm'
VEplot(object, X1, X2, x, ...)
## S3 method for class 'cox.zph'
myplot(object, resid = TRUE, se = TRUE, df = 4, nsmo = 40, var,
coef.transform=NULL,
ylab=NULL,
xlab="Time", xaxt="s", cex.axis=1,
...)
object |
An object |
resid |
Boolean, whether to plot residuals |
se |
Boolean, whether to plot confidence band |
df |
degrees of freedom |
nsmo |
number of points used to plot the fitted spline |
var |
estimated variance matrix from the Cox model fit |
xlab |
x label |
xaxt |
x axis |
cex.axis |
cex for axis |
ylab |
y label |
coef.transform |
a function to transform Cox hazard ratio estimate |
X1 |
a matrix of dimension k by p, where k is the length of x (see below) and p is the length of coef(object) |
X2 |
a matrix of dimension k by p, where k is the length of x (see below) and p is the length of coef(object) |
x |
a vector of length k that represents the x coordinate of the VE plot |
... |
additional parameters |
VEplot and myplot.cox.zph are extensions of survival::plot.cox.zph to plot VE curve and other transformations.
myplot.cox.zph adds the following parameters to the original list of parameters in plot.cox.zph: coef.transform: a function to transform the coefficients ylab: y axis label xlab: x axis label
VEplot.glm computes a series of k VEs: for i in 1...k, VE[i] = P(Y=1|X1[i,])/P(Y=1|X2[i,]). It returns a 3 by k matrix, whose first row contains VE estimates and the second and third rows contain lower and upper bounds, respectively.
Youyi Fong, Dennis Chao
Durham, Longini, Halloran, Clemens, Azhar and Rao (1998) "Estimation of vaccine efficacy in the presence of waning: application to cholera vaccines." American Journal of Epidemiology 147(10): 948-959.
library(survival)
vfit <- coxph(Surv(time,status) ~ trt + factor(celltype) +
karno + age, data=veteran, x=TRUE)
temp <- cox.zph(vfit)
par(mfrow=c(2,2))
for (v in c("trt","age")) {
VEplot(temp, var=v, resid=FALSE, main=v, ylab="VE", cex.axis=1.5)
plot(temp, var=v, resid=FALSE, main=v)
}
library(survival)
fit <- glm(status ~ trt + trt*age, data=veteran)
summary(fit)
age=seq(min(veteran$age),max(veteran$age),length=10)
out = VEplot(fit, X1=cbind(1,1,age,1*age), X2=cbind(1,0,age,0*age), x=age)
out
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