View source: R/paired.concordance.index.withsim.R
paired.concordance.index.withsim | R Documentation |
This function returns the concordance index and its p-value along with the lower and upper confidence intervals of said p-value.
paired.concordance.index.withsim(
predictions,
observations,
delta.pred = 0,
delta.obs = 0,
alpha = 0.05,
outx = FALSE,
alternative = c("two.sided", "less", "greater"),
logic.operator = c("and", "or"),
CPP = TRUE,
comppairs = 10,
simulate.p.value = FALSE,
ncores = 2
)
predictions |
numeric A vector of predicted drug responces which could be either continuous or discrete |
observations |
numeric A vector of observed continuous drug responces |
delta.pred |
numeric The minimunm reliable difference between two values in the predictions vector to be considered as significantly various values. |
delta.obs |
numeric The minimunm reliable difference between two values in the observations vector to be considered as significantly various values. In drug sensitivity , default value for delta.pred is picked by looking into delta auc values (drug response metric) between biological replicates across three large pharmacogenomic studies, CTRPv2 (370 drugs over ~15-20 cells), GDSC (1 drug over ~600 cells), GRAY (85 drugs over ~10-50 cells) |
alpha |
numeric alpha level to compute confidence interval |
outx |
boolean set to TRUE to not count pairs of predictions that are tied as a relevant pair. This results in a Goodman-Kruskal gamma type rank correlation. |
alternative |
character What is the alternative hypothesis? Must be one of "two.sides", "less", and "greater" and defaults to two.sides". |
logic.operator |
character determines how strict should the test be to remove noisy pairs. Must be one of "and" or "or" and defaults to "and". |
CPP |
boolean Whether to use the C version of the code for faster execution |
comppairs |
numeric minimum number of pairs to calculate a valid CI |
[list] ! list of concordance index and its pvalue along with the lower and upper confidence intervals
data(PLX4720_data)
pci_PLX4720 <- paired.concordance.index(predictions = PLX4720_data[ ,"AAC_CTRPv2"],
observations = PLX4720_data[ ,"AAC_GDSC"], delta.pred = 0, delta.obs = 0,
outx = TRUE)
pci_PLX4720$cindex
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