ds.ranksSecure.RdSecurely generate the ranks of a numeric vector and estimate true qlobal quantiles across all data sources simultaneously
ds.ranksSecure(
input.var.name = NULL,
quantiles.for.estimation = "0.05-0.95",
generate.quantiles = TRUE,
output.ranks.df = NULL,
summary.output.ranks.df = NULL,
ranks.sort.by = "ID.orig",
shared.seed.value = 10,
synth.real.ratio = 2,
NA.manage = "NA.delete",
rm.residual.objects = TRUE,
monitor.progress = FALSE,
datasources = NULL
)a character string in a format that can pass through the DataSHIELD R parser which specifies the name of the vector to be ranked. Needs to have same name in each data source.
one of a restricted set of character strings. To mitigate disclosure risk only the following set of quantiles can be generated: c(0.025,0.05,0.10,0.20,0.25,0.30,0.3333,0.40,0.50,0.60,0.6667, 0.70,0.75,0.80,0.90,0.95,0.975). The allowable formats for the argument are of the general form: "0.025-0.975" where the first number is the lowest quantile to be estimated and the second number is the equivalent highest quantile to estimate. These two quantiles are then estimated along with all allowable quantiles in between. The allowable argument values are then: "0.025-0.975", "0.05-0.95", "0.10-0.90", "0.20-0.80". Two alternative values are "quartiles" i.e. c(0.25,0.50,0.75), and "median" i.e. c(0.50). The default value is "0.05-0.95". If the sample size is so small that an extreme quartile could be disclosive the function will be terminated and an error message returned telling you that you might try using an argument with a narrower set of quantiles. This disclosure trap will be triggered if the total number of subjects across all studies divided by the total number of quantile values being estimated is less than or equal to nfilter.tab (the minimum cell size in a contingency table).
a logical value indicating whether the ds.ranksSecure function should carry on to estimate the key quantile values specified by argument <quantiles.for.estimation> or should stop once the global ranks have been created and written to the serverside. Default is TRUE and as the key quantiles are generally non-disclosive this is usually the setting to use. But, if there is some abnormal configuration of the clusters of values that are being ranked such that some values are treated as being missing and the processing stops, then setting generate.quantiles to FALSE allows the generation of ranks to complete so they can then be used for non-parameteric analysis, even if the key values cannot be estimated. A real example of an unusual configuration was in a reasonably large dataset of survival times, where a substantial proportion of survival profiles were censored at precisely 10 years. This meant that the 97.5 the former was allocated the value NA. This stopped processing of the ranks which could then be enabled by setting generate.quantiles to FALSE. However, if this problem is detected an error message is returned which indicates that in some cases (as in this case in fact) the problem can be circumvented by selecting a narrow range of key quantiles to estimate. In this case, in fact, this simply required changing the <quantiles.for.estimation> argument from "0.025-0.975" to "0.05-0.95".
a character string in a format that can pass through the DataSHIELD R parser which specifies an optional name for the data.frame written to the serverside on each data source that contains 11 of the key output variables from the ranking procedure pertaining to that particular data source. This includes the global ranks and quantiles of each value of the V2BR (i.e. the values are ranked across all studies simultaneously). If no name is specified, the default name is allocated as "full.ranks.df". This data.frame contains disclosive information and cannot therefore be passed to the clientside.
a character string in a format that can pass through the DataSHIELD R parser which specifies an optional name for the summary data.frame written to the serverside on each data source that contains 5 of the key output variables from the ranking procedure pertaining to that particular data source. This again includes the global ranks and quantiles of each value of the V2BR (i.e. the values are ranked across all studies simultaneously). If no name is specified, the default name is allocated as "summary.ranks.df" This data.frame contains disclosive information and cannot therefore be passed to the clientside.
a character string taking two possible values. These are "ID.orig" and "vals.orig". These define the order in which the output.ranks.df and summary.output.ranks.df data frames are presented. If the argument is set as "ID.orig" the order of rows in the output data frames are precisely the same as the order of original input vector that is being ranked (i.e. V2BR). This means the ranks can simply be cbinded to the matrix, data frame or tibble that originally included V2BR so it also includes the corresponding ranks. If it is set as "vals.orig" the output data frames are in order of increasing magnitude of the original values of V2BR. Default value is "ID.orig".
an integer value which is used to set the random seed generator in each study. Initially, the seed is set to be the same in all studies, so the order and parameters of the repeated encryption procedures are precisely the same in each study. Then a study-specific modification of the seed in each study ensures that the procedures initially generating the masking pseudodata (which are then subject to the same encryption procedures as the real data) are different in each study. For further information about the shared seed and how we intend to transmit it in the future, please see the detailed associated header document.
an integer value specifying the ratio between the number of masking pseudodata values generated in each study compared to the number of real data values in V2BR.
character string taking three possible values: "NA.delete", "NA.low","NA.hi". This argument determines how missing values are managed before ranking. "NA.delete" results in all missing values being removed prior to ranking. This means that the vector of ranks in each study is shorter than the original vector of V2BR values by an amount corresponding to the number of missing values in V2BR in that study. Any rows containing missing values in V2BR are simply removed before the ranking procedure is initiated so the order of rows without missing data is unaltered. "NA.low" indicates that all missing values should be converted to a new value that has a meaningful magnitude that is lower (more negative or less positive) than the lowest non-missing value of V2BR in any of the studies. This means, for example, that if there are a total of M values of V2BR that are missing across all studies, there will be a total of M observations that are ranked lowest each with a rank of (M+1)/2. So if 7 are missing the lowest 7 ranks will be 4,4,4,4,4,4,4 and if 4 are missing the first 4 ranks will be 2.5,2.5,2.5,2.5. "NA.hi" indicates that all missing values should be converted to a new value that has a meaningful magnitude that is higher(less negative or more positive)than the highest non-missing value of V2BR in any of the studies. This means, for example, that if there are a total of M values of V2BR that are missing across all studies and N non-missing values, there will be a total of M observations that are ranked highest each with a rank of (2N-M+1)/2. So if there are a total of 1000 V2BR values and 9 are missing the highest 9 ranks will be 996, 996 ... 996. If NA.manage is either "NA.low" or "NA.hi" the final rank vector in each study will have the same length as the V2BR vector in that same study. 2.5,2.5,2.5,2.5. The default value of the "NA.manage" argument is "NA.delete"
logical value. Default = TRUE: at the beginning and end of each run of ds.ranksSecure delete all extraneous objects that are otherwise left behind. These are not usually needed, but could be of value if one were investigating a problem with the ranking. FALSE: do not delete the residual objects
logical value. Default = FALSE. If TRUE, function outputs information about its progress.
specifies the particular opal object(s) to use. If the <datasources> argument is not specified (NULL) the default set of opals will be used. If <datasources> is specified, it should be set without inverted commas: e.g. datasources=opals.em. If you wish to apply the function solely to e.g. the second opal server in a set of three, the argument can be specified as: e.g. datasources=opals.em[2]. If you wish to specify the first and third opal servers in a set you specify: e.g. datasources=opals.em[c(1,3)].
the data frame objects specified by the arguments output.ranks.df and summary.output.ranks.df. These are written to the serverside in each study. Provided the sort order is consistent these data frames can be cbinded to any other data frame, matrix or tibble object containing V2BR or to the V2BR vector itself, allowing the global ranks and quantiles to be analysed rather than the actual values of V2BR. The last call within the ds.ranksSecure function is to another clientside function ds.extractQuantile (for further details see header for that function). This returns an additional data frame "final.quantile.df" of which the first column is the vector of key quantiles to be estimated as specified by the argument <quantiles.for.estimation> and the second column is the list of precise values of V2BR which correspond to these key quantiles. Because the serverside functions associated with ds.ranksSecure and ds.extractQuantile block potentially disclosive output (see information for parameter quantiles.for.estimation) the "final.quantile.df" is returned to the client allowing the direct reporting of V2BR values corresponding to key quantiles such as the quartiles, the median and 95th percentile etc. In addition a copy of the same data frame is also written to the serverside in each study allowing the value of key quantiles such as the median to be incorporated directly in calculations or transformations on the serverside regardless in which study (or studies) those key quantile values have occurred.
ds.ranksSecure is a clientside function which calls a series of other clientside and serverside functions to securely generate the global ranks of a numeric vector "V2BR" (vector to be ranked) in order to set up analyses on V2BR based on non-parametric methods, some types of survival analysis and to derive true global quantiles (such as the median, lower (25 and the 95 global quantiles are, in general, different to the mean or median of the equivalent quantiles calculated independently in each data source separately. For more details about the cluster of functions that collectively enable secure global ranking and estimation of global quantiles see the associated document entitled "secure.global.ranking.docx".