ds.rNorm.RdGenerates normally distributed random (pseudorandom) scalar numbers.
Besides, ds.rNorm allows creating different vector lengths in each server.
ds.rNorm(
samp.size = 1,
mean = 0,
sd = 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.
the mean value or vector of the Normal distribution to be created.
the standard deviation of the Normal distribution to be created.
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 returns 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 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.rNorm returns random number vectors with a normal 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 pseudorandom numbers distributed
with a Normal distribution in each data source.
The ds.rNorm function's arguments specify the mean and the standard deviation
(sd) of the normal distribution and
the length and the seed of the output vector in each source.
To specify a different mean value in each source,
you can use a character vector (..., mean="vector.of.means"...)
or the datasources parameter to create the random vector for one source at a time,
changing the mean as required.
Default value for mean = 0.
To specify different sd value in each source,
you can use a character vector (..., sd="vector.of.sds"...
or the datasources parameter to create the random vector for one source at a time,
changing the <mean> as required.
Default value for sd = 0.
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 integer.
If k = 9, no rounding of output numbers occurs.
The default value of force.output.to.k.decimal.places = 9.
Server functions called: rNormDS 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.rNorm(samp.size=c(10,20,45), #the length of the vector created in each source is different
mean=c(1,6,4), #the mean of the Normal distribution changes in each server
sd=as.character(c(1,4,3)), #the sd of the Normal distribution changes in each server
newobj="Norm.dist",
seed.as.integer=2345,
return.full.seed.as.set=FALSE,
force.output.to.k.decimal.places=c(4,5,6), #output random numbers have different
#decimal quantity in each source
datasources=connections) #all the Opal servers are used, in this case 3
#(see above the connection to the servers)
ds.rNorm(samp.size=10,
mean=1.4,
sd=0.2,
newobj="Norm.dist",
seed.as.integer=2345,
return.full.seed.as.set=FALSE,
force.output.to.k.decimal.places=1,
datasources=connections[2]) #only the second Opal server is used ("study2")
# Clear the Datashield R sessions and logout
datashield.logout(connections)
} # }