Description Usage Arguments Details Value Examples
Create a random parametric bootstrap sample or a permutation of the input response vector or matrix (for survival outcomes).
1 2 3 4 5 6 7 8 9 10 11 12 | SampleResponses(
response_vec,
event_vec = NULL,
respType = c("survival", "regression", "categorical"),
parametric = TRUE
)
SampleSurv(response_vec, event_vec, parametric = TRUE)
SampleReg(response_vec, parametric = TRUE)
SampleCateg(response_vec, parametric = TRUE)
|
response_vec |
The dependent vector to sample from. For survival response, this is the vector of event times. For regression or n-ary classification, this is the vector of responses. |
event_vec |
The death / event observation indicator vector for survival response. This is coded as 0 for a right-censoring occurence and 1 for a recorded event. |
respType |
What type of response has been supplied. Options are
|
parametric |
Should the random sample be taken using a parametric
bootstrap sample? Defaults to |
The distributions (for parametric = TRUE
) are Weibull for
survival times, Normal for regression response, and n-ary Multinomial for
categorical response. Distributional parameters are estimated with their
maximum likelihood estimates. When parametric = FALSE
, the response
vector or survival matrix is randomly ordered by row. This option should
only be used when called from the AES-PCA method.
If parametric = FALSE
, a permutation of the supplied response
is returned (for AES-PCA). If parametric = TRUE
, we return a
parametric bootstrap sample of the response.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # DO NOT CALL THESE FUNCTIONS DIRECTLY.
# Use AESPCA_pVals() or SuperPCA_pVals() instead
## Not run:
data("colon_pathwayCollection")
data("colonSurv_df")
SampleResponses(
response_vec = colonSurv_df$OS_time,
event_vec = colonSurv_df$OS_event,
respType = "survival"
)
## End(Not run)
|
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