Computes thousands of consonance (confidence) intervals for the chosen parameter in the Cox model computed by the 'survival' package and places the interval limits for each interval level into a data frame along with the corresponding p-value and s-value.
curve_surv(data, x, steps = 10000, cores = getOption("mc.cores", 1L), table = TRUE)
Object where the Cox model is stored, typically a list produced by the 'survival' package.
Predictor of interest within the survival model for which the consonance intervals should be computed.
Indicates how many consonance intervals are to be calculated at various levels. For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe.
Select the number of cores to use in order to compute the intervals The default is 1 core.
Indicates whether or not a table output with some relevant statistics should be generated. The default is TRUE and generates a table which is included in the list object.
A list with 3 items where the dataframe of values is in the first object, the values needed to calculate the density function in the second, and the table for the values in the third if table = TRUE.
#> week arrest fin age race wexp mar paro prio educ #> 1 20 1 no 27 black no not married yes 3 3 #> 2 17 1 no 18 black no not married yes 8 4 #> 3 25 1 no 19 other yes not married yes 13 3 #> 4 52 0 yes 23 black yes married yes 1 5 #> 5 52 0 no 19 other yes not married yes 3 3library(survival) mod.allison <- coxph(Surv(week, arrest) ~ fin + age + race + wexp + mar + paro + prio, data = Rossi ) mod.allison#> Call: #> coxph(formula = Surv(week, arrest) ~ fin + age + race + wexp + #> mar + paro + prio, data = Rossi) #> #> coef exp(coef) se(coef) z p #> finyes -0.37942 0.68426 0.19138 -1.983 0.04742 #> age -0.05744 0.94418 0.02200 -2.611 0.00903 #> raceother -0.31390 0.73059 0.30799 -1.019 0.30812 #> wexpyes -0.14980 0.86088 0.21222 -0.706 0.48029 #> marnot married 0.43370 1.54296 0.38187 1.136 0.25606 #> paroyes -0.08487 0.91863 0.19576 -0.434 0.66461 #> prio 0.09150 1.09581 0.02865 3.194 0.00140 #> #> Likelihood ratio test=33.27 on 7 df, p=2.362e-05 #> n= 432, number of events= 114z <- curve_surv(mod.allison, "prio")