ds.cor | R Documentation |

This function calculates the correlation of two variables or the correlation matrix for the variables of an input data frame.

ds.cor(x = NULL, y = NULL, type = "split", datasources = NULL)

`x` |
a character string providing the name of the input vector, data frame or matrix. |

`y` |
a character string providing the name of the input vector, data frame or matrix. Default NULL. |

`type` |
a character string that represents the type of analysis to carry out.
This must be set to |

`datasources` |
a list of |

In addition to computing correlations; this function produces a table outlining the number of complete cases and a table outlining the number of missing values to allow the user to decide the 'relevance' of the correlation based on the number of complete cases included in the correlation calculations.

If the argument `y`

is not NULL, the dimensions of the object have to be
compatible with the argument `x`

.

The function calculates the pairwise correlations based on casewise complete cases which means that it omits all the rows in the input data frame that include at least one cell with a missing value, before the calculation of correlations.

If `type`

is set to `'split'`

(default), the correlation of two variables or the
variance-correlation matrix of an input data frame and the number of complete cases and missing
values are returned for every single study. If type is set to `'combine'`

, the pooled
correlation, the total number of complete cases and the total number of missing values aggregated
from all the involved studies, are returned.

Server function called: `corDS`

`ds.cor`

returns a list containing the number of missing values in each variable,
the number of missing variables casewise, the correlation matrix,
the number of used complete cases. The function applies two disclosure controls. The first disclosure
control checks that the number of variables is not bigger than a percentage of the individual-level records (the allowed
percentage is pre-specified by the 'nfilter.glm'). The second disclosure control checks that none of them is dichotomous
with a level having fewer counts than the pre-specified 'nfilter.tab' threshold.

DataSHIELD Development Team

## Not run: ## Version 6, for version 5 see the Wiki # Connecting to the Opal servers require('DSI') require('DSOpal') require('dsBaseClient') builder <- DSI::newDSLoginBuilder() builder$append(server = "study1", url = "http://192.168.56.100:8080/", user = "administrator", password = "datashield_test&", table = "CNSIM.CNSIM1", driver = "OpalDriver") builder$append(server = "study2", url = "http://192.168.56.100:8080/", user = "administrator", password = "datashield_test&", table = "CNSIM.CNSIM2", driver = "OpalDriver") builder$append(server = "study3", url = "http://192.168.56.100:8080/", user = "administrator", password = "datashield_test&", table = "CNSIM.CNSIM3", driver = "OpalDriver") logindata <- builder$build() # Log onto the remote Opal training servers connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D") # Example 1: Get the correlation matrix of two continuous variables ds.cor(x="D$LAB_TSC", y="D$LAB_TRIG", type="combine", datasources = connections) # Example 2: Get the correlation matrix of the variables in a dataframe ds.dataFrame(x=c("D$LAB_TSC", "D$LAB_TRIG", "D$LAB_HDL", "D$PM_BMI_CONTINUOUS"), newobj="D.new", check.names=FALSE, datasources=connections) ds.cor("D.new", type="combine", datasources = connections) # clear the Datashield R sessions and logout datashield.logout(connections) ## End(Not run)

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