Description Usage Arguments Details Value Note See Also Examples
covariance matrix for survival data
1 2 3 4 5 6 7 8 9 10 
x 
A 
... 
Additional arguments (not implemented). 
reCalc 
Recalcuate the values?

n 
number at risk (total). 
ncg 
number at risk, per covariate group.

Gives variancecovariance matrix for comparing survival
data for two or more groups.
Inputs are vectors corresponding to observations at a set of discrete
time points for right censored data, except for n1,
the no. at risk by predictor.
This should be specified as a vector for one group,
otherwise as a matrix with each column corresponding to a group.
An array
.
The first two dimensions = the number of covariate groups K,
k = 1, 2, … K.
This is the square matrix below.
The third dimension is the number of observations
(discrete time points).
To calculate this, we use x
(= e[t] below) and
n1, the number at risk in covariate group 1.
Where there are 2 groups, the resulting sparse square matrix
(i.e. the nondiagonal elements are 0)
at time t has diagonal elements:
cov[t] =  n0[t] * n1[t] * e[t] * (n[t]  e[t]) / (n[t]^2 * (n[t]  1))
For >=2 groups, the resulting square matrix has diagonal elements given by:
cov[k, k, t] = n[k, t] * (n[t]  n[k, t]) * e[t] * (n[t]  e[t]) / (n[t]^2 * (n[t]  1))
The off diagonal elements are:
cov[k, l, t] =  n[k, t] * n[l, t] * e[t] * (n[t]  e[t]) / n[t]^2 * (n[t]  1)
Where the is just one subject at risk n=1 at
the final timepoint, the equations above may produce NaN
due to division by zero. This is converted to 0
for
simplicity.
Called by comp
The name of the function is capitalized
to distinguish it from:
?stats::cov
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## Two covariate groups
## K&M. Example 7.2, pg 210, table 7.2 (last column).
data("kidney", package="KMsurv")
k1 < with(kidney,
ten(Surv(time=time, event=delta) ~ type))
COV(k1)[COV(k1) > 0]
## Four covariate groups
## K&M. Example 7.6, pg 217.
data("larynx", package="KMsurv")
l1 < ten(Surv(time, delta) ~ stage, data=larynx)
rowSums(COV(l1), dims=2)
## example of numeric method
## Three covariate groups
## K&M. Example 7.4, pg 212.
data("bmt", package="KMsurv")
b1 < asWide(ten(Surv(time=t2, event=d3) ~ group, data=bmt))
rowSums(b1[, COV(x=e, n=n, ncg=matrix(data=c(n_1, n_2, n_3), ncol=3))], dims=2)

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