Description Usage Arguments Value
View source: R/datacollation.R
Generate summary dataframe of correlations with the variable of interest "MainVariable" and the variables in col_names_list at different "start size"
1 2 3 | get_Variable_cor_summary(summary, MainVariable, col_names_list,
num_parameters, min_count, Verbose = FALSE, ReturnCI = FALSE,
VariablesToNotGroupBy = NULL, method = "spearman")
|
summary |
dataframe including columns named "seed", "Generation", "start_time", "start_size", "gap" and "waiting_time" |
MainVariable |
variable of interest |
col_names_list |
char vector of column names in the summary dataframe |
num_parameters |
number of parameters, accounting for the first set of columns in the dataframe |
min_count |
minimum number of items in each column (otherwise result will be NA) |
Verbose |
if TRUE, helpful to debug, print the name of the variables with which compute the correlation |
ReturnCI |
if true, also return the 0.95 level confidence interval computed by bootstraping. |
VariablesToNotGroupBy |
vector of column names by which we don't want to group the simulation. For example, if mutation rate (mu_driver_birth) are random, then we don't want to group the simulations by the variable mu_driver_birth, so we need to set VariablesToNotGroupBy=c("mu_driver_birth") This is more general than creating a boolean argument as CombinedMutationRate=TRUE/FALSE. |
Dataframe with one row for each unique combination of parameter values and start_size (i.e. it summarises over "seed"), and including columns containing the correlations between "waiting_time" and each variable in col_names_list and the associated pValues for the two.sided test of the correlation coefficient. If the argument ReturnCI=TRUE, the 0.95 Confidence Intervals for the correlation coefficients are also computed. Argument VariablesToNotGroupBy allows to compute the correlation coefficients while not grouping simulations by variables contained into VariablesToNotGroupBy.
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