ds.rNorm {dsBaseClient} | R Documentation |

Generates 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 )

`samp.size` |
an integer value or an integer vector that defines the length of the random numeric vector to be created in each source. |

`mean` |
the mean value or vector of the Normal distribution to be created. |

`sd` |
the standard deviation of the Normal distribution to be created. |

`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 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. |

`force.output.to.k.decimal.places` |
an integer vector that forces the output random numbers vector to have k decimals. |

`datasources` |
a list of |

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`

.

`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`

).

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.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) ## End(Not run)

[Package *dsBaseClient* version 6.1.1 ]