Description Usage Arguments Details Value Author(s) References See Also Examples
Conduct Kadaptive partitioning algorithm for survival data
1 
formula 
Formula object with a response on the left hand side of the '~' operator, and the covariate terms on the right side. The response has to be a survival object with survival time and censoring status in the Surv function. For more details, see Formula page. 
data 
data frame with variables used in formula. It needs at least three variables including survival time, censoring status, and a covariate. Multivariate covariates can be supported with "+" sign. 
K 
number of subgroups used in the model fitting. The default value is 2:4 which means finding optimal subgroups ranging from 2 to 4. 
type 
Select a type of algorithm in order to find optimal number of subgroups. Two options are provided: 
mindat 
the minimum number of observations at each subgroup. The default value is 5% of observations. 
... 
a list of tuning parameters with the class, "kapsOptions". For more details, see kaps.control. 
This function provides routines to conduct KAPS algorithm which is designed to classify cutoff values by the minimaxbased rule.
The function returns an object with class "kaps" with the following slots.

evaluated function call 

formula to be used in the model fitting 

data to be used in the model fitting 

information about the subgroup classification 

an index for the optimal subgroup among the candidate K 

test statistic with the worst pair of subgroups for the split set s 

the overall test staitstic with K subgroups using the split set s 

selected pair of subgroups 

selected covariate in the model fitting 

selected set of cutoff points 

minimum number of observations at a subgroup 

Bonferroni corrected pvalue matrix. The first row means overall pvalues and the second one denotes pvalues of the worstpair against K. The column in the matrix describes the order of K. 

adjusted overall test statistic by Bootstrapping 

adjusted worstpair test statistic by Bootstrapping 

candidate K used in the argument 

list object about the results of each candidate K 

tuning parameters 
SooHeang Eo [email protected]
SeungMo Hong [email protected]
HyungJun Cho [email protected]
SH Eo, SM Hong and H Cho (2014). Kadaptive partitioning for survival data, submitted.
show
, plot
, predict
, print
and summary
for the convenient use of kaps()
kaps.control
to control kaps() more detail
count.mindat
to calculate minimum subgroup sample size
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  ## Not run:
data(toy)
f < Surv(time, status) ~ meta
# Fit kaps algorithm without crossvalidation.
# It means the step to finding optimal K is not entered.
fit1 < kaps(f, data = toy, K = 3)
# show the object of kaps (it contains apss S4 class)
fit1
# plot KaplanMeire estimates
plot(fit1)
# Fit kaps algorithm for selection optimal number of subgropus.
fit2 < kaps(f, data = toy, K= 2:4)
fit2
# plot outputs with subgroup selection
require(locfit) # for scatterplot smoothing
plot(fit2)
print(fit2,K=2)
summary(fit2)
summary(fit2,K=2)
# require(party)
# fit4 < ctree(f, data = toy)
## End(Not run)

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