hanaml.CoxProportionalHazard.Rd
hanaml.CoxProportionalHazard is an R wrapper for SAP HANA PAL Cox proportional hazard model.
hanaml.CoxProportionalHazard(
data = NULL,
key = NULL,
label = NULL,
status = NULL,
features = NULL,
formula = NULL,
tie.method = NULL,
max.iter = NULL,
tol = NULL,
significance.level = NULL,
calculate.hazard = NULL,
output.fitted = NULL
)
DataFrame
DataFrame containting the data.
character, optional
Name of the ID column.
Defaults to the first column if not provided.
character, optional
Name of the regression target column of Cox proportional hazard model,
which specifies the time before a failure/death event occurs or data
is right censored.
If not provided, it defaults to the 1st non-ID column of data.
character, optional
Name of the status column that indicates if the individual is an event
or right-censored data.
If not specified, then data is assumed to have no status column,
and all timestamps in label column are thus assumed to be
associated with death/failure.
list/vector of character, optional
Names of the covariate columns.
Defaults to all non-ID, non-label, non-status columns if not specified.
formula type, optional
Formula to be used for model generation.
format = label~<feature_list>
e.g.: formula=CATEGORY~V1+V2+V3
You can either give the formula,
or a feature and label combination, but do not provide both.
Defaults to NULL.
c("breslow", "efron"), optional
Specifies the method for dealing with tied events.
Defaults to "efron".
integer, optional
Maximum number of iterations for numeric optimization in maximum
log-likelihood estimation.
Defaults to 100.
numeric, optional
Convergence(stopping) criterion for numeric optimization.
Defaults to 1e-8.
numeric, optional
Significance level for the confidence interval of estimated coefficients.
Defaults to 0.05.
logical, optional
Specifies whether or not to calculate hazard function as well as survival function.
Defaults to TRUE.
logical, optional
Specifies whether or not to output the fitted response.
Defaults to FALSE.
Return a "CoxProportionalHazard" object with following values:
statistics: DataFrame
Regression-related statistics, like mean square error, F-statistics, etc.
coefficients: DataFrame
Fitted regression coefficients for the Cox PH model.
covariance: DataFrame
Covariance values between features.
hazard: DataFrame
Calculated cumulative baseline hazard function and survival function.
fitted: DataFrame
Predicted linear predictors and exponential responses in risk space.
Cox proportional hazard model is a special generalized linear model. It is a well-known realization-of-survival model that demonstrates failure or death at a certain time
Input DataFrame data:
> data$Collect()
ID TIME STATUS X1 X2
1 1 4 1 0 0
2 2 3 1 2 0
3 3 1 1 1 0
4 4 1 0 1 0
5 5 2 1 1 1
6 6 2 1 0 1
7 7 3 0 0 1
Call the function:
cph <- hanaml.CoxProportionalHazard(data = data,
key = "ID",
label = "TIME",
status = "STATUS",
tie.method = "efron",
calculate.hazard = TRUE,
output.fitted = TRUE)
Output:
> cph$coefficients
VARIABLE_NAME MEAN COEFFICIENT SE SCORE PROBABILITY CI_LOWER CI_UPPER
1 X1 0.7142857 0.7811819 0.7975689 0.9794538 0.3273558 -0.7820244 2.344388
2 X2 0.4285714 0.9337832 1.4081100 0.6631465 0.5072367 -1.8260617 3.693628