ds.cor {dsStatsClient} | R Documentation |
This is similar to the R base function 'cor'.
ds.cor(x = NULL, y = NULL, naAction = "pairwise.complete.obs", datasources = NULL)
x |
a character, the name of a numerical vector, matrix or dataframe |
y |
NULL (default) or the name of a vector, matrix or data frame with compatible dimensions to x. |
naAction |
a character string giving a method for computing covariances in the presence of missing values. This must be one of the strings: "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". The default value is set to "pairwise.complete.obs" |
datasources |
a list of opal object(s) obtained
after login in to opal servers; these objects hold also
the data assign to R, as |
In addition to computing correlations this function, unlike the R base function 'cor', produces a table outlining the number of complete cases to allow for the user to make a decision about the 'relevance' of the correlation based on the number of complete cases included in the correlation calculations.
a list containing the results of the test
Gaye, A.
{ # load that contains the login details data(logindata) # login and assign specific variable(s) # (by default the assigned dataset is a dataframe named 'D') myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER') opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar) # Example 1: generate the correlation matrix for the assigned dataset 'D' # which contains 4 vectors (2 continuous and 1 categorical) ds.cor(x='D') # Example 2: calculate the correlation between two vectors (first assign some vectors from the dataframe 'D') ds.assign(newobj='labhdl', toAssign='D$LAB_HDL') ds.assign(newobj='labtsc', toAssign='D$LAB_TSC') ds.assign(newobj='gender', toAssign='D$GENDER') ds.cor(x='labhdl', y='labtsc') ds.cor(x='labhdl', y='gender') # clear the Datashield R sessions and logout datashield.logout(opals) }