| ds.rPois {dsBaseClient} | R Documentation |
Generates random (pseudorandom) non-negative integers
with a Poisson distribution.
Besides, ds.rPois allows creating different vector lengths in each server.
ds.rPois(
samp.size = 1,
lambda = 1,
newobj = "newObject",
seed.as.integer = NULL,
return.full.seed.as.set = FALSE,
datasources = NULL
)
samp.size |
an integer value or an integer vector that defines the length of the random numeric vector to be created in each source. |
lambda |
the number of events mean per interval. |
newobj |
a character string that provides the name for the output variable
that is stored on the data servers. Default |
seed.as.integer |
an integer or a NULL value which provides the random seed in each data source. |
return.full.seed.as.set |
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. |
datasources |
a list of |
Creates a vector of random or pseudorandom non-negative integer values
distributed with a Poisson distribution in each data source.
The ds.rPois function's arguments specify lambda,
the length and the seed of the output vector in each source.
To specify different lambda value in each source, you can use a character vector
(..., lambda = "vector.of.lambdas"...) or the datasources
parameter to create the random vector for one source at a time,
changing lambda as required.
Default value for lambda> = 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.
Server functions called: rPoisDS and setSeedDS.
ds.rPois returns random number vectors with a Poisson 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).
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")
# Generating the vectors in the Opal servers
ds.rPois(samp.size=c(13,20,25), #the length of the vector created in each source is different
lambda=as.character(c(2,3,4)), #different mean per interval (2,3,4) in each source
newobj="Pois.dist",
seed.as.integer=1234,
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.rPois(samp.size=13,
lambda=5,
newobj="Pois.dist",
seed.as.integer=1234,
return.full.seed.as.set=FALSE,
datasources=connections[1]) #only the first Opal server is used ("study1")
# Clear the Datashield R sessions and logout
datashield.logout(connections)
## End(Not run)