ds.rBinom.RdGenerates random (pseudorandom) non-negative integers from a Binomial distribution.
Also, ds.rBinom allows creating different vector lengths in each server.
ds.rBinom(
samp.size = 1,
size = 0,
prob = 1,
newobj = NULL,
seed.as.integer = NULL,
return.full.seed.as.set = FALSE,
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 positive integer that specifies the number of Bernoulli trials.
a numeric scalar value or vector in range 0 > prob > 1 which specifies the probability of a positive response (i.e. 1 rather than 0).
a character string that provides the name for the output variable
that is stored on the data servers. Default rbinom.newobj.
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.
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.rBinom returns random number vectors
with a Binomial distribution for each study,
taking into account the values specified in each parameter of the function.
The output vector is written to 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).
Creates a vector of random or pseudorandom non-negative integer values distributed with a Binomial distribution. The ds.rBinom function's arguments specify the number of trials, the success probability, the length and the seed of the output vector in each source.
To specify a different size in each source, you can use a character vector
(..., size="vector.of.sizes"...)
or the datasources parameter to create the random vector for one source at a time,
changing size as required.
The default value for size = 1 which simulates binary outcomes (all observations 0 or 1).
To specify different prob in each source, you can use an integer or character vector
(..., prob="vector.of.probs"...) or the datasources parameter to create the random
vector for one source at a time, changing prob as required.
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.
Besides, 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.
Server functions called: rBinomDS 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.rBinom(samp.size=c(13,20,25), #the length of the vector created in each source is different
size=as.character(c(10,23,5)), #Bernoulli trials change in each source
prob=c(0.6,0.1,0.5), #Probability changes in each source
newobj="Binom.dist",
seed.as.integer=45,
return.full.seed.as.set=FALSE,
datasources=connections) #all the Opal servers are used, in this case 3
#(see above the connection to the servers)
ds.rBinom(samp.size=15,
size=4,
prob=0.7,
newobj="Binom.dist",
seed.as.integer=324,
return.full.seed.as.set=FALSE,
datasources=connections[2]) #only the second Opal server is used ("study2")
# Clear the Datashield R sessions and logout
datashield.logout(connections)
} # }