ds.mean | R Documentation |
This function computes the statistical mean of a given server-side vector.
ds.mean(
x = NULL,
type = "split",
checks = FALSE,
save.mean.Nvalid = FALSE,
datasources = NULL
)
x |
a character specifying the name of a numerical vector. |
type |
a character string that represents the type of analysis to carry out.
This can be set as |
checks |
logical. If TRUE optional checks of model components will be undertaken. Default is FALSE to save time. It is suggested that checks should only be undertaken once the function call has failed. |
save.mean.Nvalid |
logical. If TRUE generated values of the mean and the number of valid (non-missing) observations will be saved on the data servers. Default FALSE. For more information see Details. |
datasources |
a list of |
This function is similar to the R function mean
.
The function can carry out 3 types of analysis depending on
the argument type
:
(1) If type
is set to 'combine'
, 'combined'
,
'combines'
or 'c'
, a global mean is calculated.
(2) If type
is set to 'split'
, 'splits'
or 's'
,
the mean is calculated separately for each study.
(3) If type
is set to 'both'
or 'b'
,
both sets of outputs are produced.
If the argument save.mean.Nvalid
is set to TRUE
study-specific means and Nvalids
as well as the global equivalents across all studies combined
are saved in the server-side.
Once the estimated means and Nvalids
are written into the server-side R environments, they can be used directly to centralize
the variable of interest around its global mean or its study-specific means. Finally,
the isDefined
internal function checks whether the key variables have been created.
Server function called: meanDS
ds.mean
returns to the client-side a list including:
Mean.by.Study
: estimated mean, Nmissing
(number of missing observations), Nvalid
(number of valid observations) and
Ntotal
(sum of missing and valid observations)
separately for each study (if type = split
or type = both
).
Global.Mean
: estimated mean, Nmissing
, Nvalid
and Ntotal
across all studies combined (if type = combine
or type = both
).
Nstudies
: number of studies being analysed.
ValidityMessage
: indicates if the analysis was possible.
If save.mean.Nvalid
is set as TRUE, the objects
Nvalid.all.studies
, Nvalid.study.specific
,
mean.all.studies
and mean.study.specific
are written to the server-side.
DataSHIELD Development Team
ds.quantileMean
to compute quantiles.
ds.summary
to generate the summary of a variable.
## 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()
connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")
#Calculate the mean of a vector in the server-side
ds.mean(x = "D$LAB_TSC",
type = "split",
checks = FALSE,
save.mean.Nvalid = FALSE,
datasources = connections)
# clear the Datashield R sessions and logout
datashield.logout(connections)
## End(Not run)
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