ds.rUnif.RdGenerates uniformly distributed random (pseudorandom) scalar numbers.
Besides, ds.rUnif allows creating different vector lengths in each server.
ds.rUnif(
samp.size = 1,
min = 0,
max = 1,
newobj = "newObject",
seed.as.integer = NULL,
return.full.seed.as.set = FALSE,
force.output.to.k.decimal.places = 9,
datasources = NULL
)an integer value or an integer vector that defines the length of the random numeric vector to be created in each source.
a numeric scalar that specifies the minimum value of the random numbers in the distribution.
a numeric scalar that specifies the maximum value of the random numbers in the distribution.
a character string that provides the name for the output variable
that is stored on the data servers. Default newObject.
an integer or a NULL value which provides the random seed in each data source.
logical, if TRUE will return the full random number seed in each data source (a numeric vector of length 626). If FALSE it will only return the trigger seed value you have provided. Default is FALSE.
an integer or an integer vector that forces the output random numbers vector to have k decimals.
a list of DSConnection-class objects obtained after login.
If the datasources argument is not specified
the default set of connections will be used: see datashield.connections_default.
ds.Unif returns random number vectors with a uniform distribution for each study,
taking into account the values specified in each parameter of the function.
The created vectors are stored in the server-side. If requested, it also returned to the
client-side the full 626 lengths random seed vector generated in each source
(see info for the argument return.full.seed.as.set).
It creates a vector of pseudorandom numbers distributed
with a uniform probability in each data source.
The ds.Unif function's arguments specify
the minimum and maximum of the uniform distribution
and the length and the seed of the output vector in each source.
To specify different min values in each source,
you can use a character vector (..., min="vector.of.mins"...)
or the datasources parameter to create the random vector for one source at a time,
changing the min value as required.
Default value for min = 0.
To specify different max values in each source,
you can use a character vector (..., max="vector.of.maxs"...)
or the datasources parameter to create the random vector for one source at a time,
changing the max value as required.
Default value for max = 1.
If seed.as.integer is an integer
e.g. 5 and there is more than one source (N) the seed is set as 5*N.
For example, in the first study the seed is set as 938*1,
in the second as 938*2
up to 938*N in the Nth study.
If seed.as.integer is set as 0 all sources will start with the seed value
0 and all the random number generators will, therefore, start from the same position.
Also, to use the same starting seed in all studies but do not wish it to
be 0, you can use datasources argument to generate
the random number vectors one source at a time.
In force.output.to.k.decimal.places the range of k is 1-8 decimals.
If k = 0 the output random numbers are forced to an integer.
If k = 9, no rounding of output numbers occurs.
The default value of force.output.to.k.decimal.places = 9.
If you wish to generate integers with equal probabilities in the range 1-10
you should specify min = 0.5 and max = 10.5.
Default value for k = 9.
Server functions called: rUnifDS and setSeedDS.
if (FALSE) { # \dontrun{
## 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")
# Generating the vectors in the Opal servers
ds.rUnif(samp.size = c(12,20,4), #the length of the vector created in each source is different
min = as.character(c(0,2,5)), #different minumum value of the function in each source
max = as.character(c(2,5,9)), #different maximum value of the function in each source
newobj = "Unif.dist",
seed.as.integer = 234,
return.full.seed.as.set = FALSE,
force.output.to.k.decimal.places = c(1,2,3),
datasources = connections) #all the Opal servers are used, in this case 3
#(see above the connection to the servers)
ds.rUnif(samp.size = 12,
min = 0,
max = 2,
newobj = "Unif.dist",
seed.as.integer = 12345,
return.full.seed.as.set = FALSE,
force.output.to.k.decimal.places = 2,
datasources = connections[2]) #only the second Opal server is used ("study2")
# Clear the Datashield R sessions and logout
datashield.logout(connections)
} # }