Anchortoptop(Link back to Version 4 Archive)

Contents

Functions amended

ds.glm

New parameters (i.e. arguments) added to the function: ds.glm: data, weights and offset, checks, startBetas.

• data allows for user to specify the name of an optional data frame that holds the variables in the regression formula so one can can write for example:

  Code Block
  language xml
  ds.glm(formula='DIS_DIAB~GENDER', data='D', family='binomial')

  which is equivalent to

  Code Block
  language xml
  ds.glm(formula='D$DIS_DIAB~D$GENDER', family='binomial')

  In the previous release only the latter command was possible when the variables in the regression formula were held in a data frame 'D'.

• weights to specify the name of a numeric variable of 'prior weights' to be used in the model fitting process.
• offset to specify the name of a known component to be included in the linear predictor.
• checks to help with error tracking. These checks are switched off by default. If set to TRUE thorough checks are carried out before the process starts.
• startBetas to specify starting values for the parameters in the linear predictor. In earlier versions this parameter was named startCoeff.

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In the previous version of this function a t.test was only possible for two continuous vectors.
The new version allows for the comparison of the mean values of a continuous vector across the categories of factor as in the below example where both vectors are in a data frame 'D':

# The continuous vector is on the left of the formula whilst the factor is on the right side
ds.tTest(x='D$PM_BMICONTINUOUS~D$GENDER')

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In the new version of this function it is possible to compute the mean and standard deviation of a continuous vector across the categories of a factor as follows:

# The continuous vector is on the left of the formula whilst the factor is on the right side. In the below example both vector are in a data frame named 'D'.
ds.meanByClass(x='D$LAB_HDL~D$GENDER')

This addition was implemented for improved flexibility and is equivalent to the below syntax which still works and should be used to specify more than one outcome or covariate.

ds.meanByClass(x='D', outvar=c('LAB_HDL'), covar=c('GENDER'))

New functions

ds.subsetByClass

This function replaces the previous ds.subclass

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This function has been replaced by ds.subsetByClass

Actions required from users

• Some of the changes listed above might affect your previous scripts/commands, particularly the change of function names and syntax (e.g. ds.subclass replaced by ds.subsetByClass). Please amend your previous scripts/commands to adopt the new functions names and syntax to avoid errors.

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Actions required from opal server administrators

If you are the administrator of the opal server for a cohort: Please deploy the new package in your servers. Install all the packages via opal as follows:

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List of packages and functions in the version 4.0.0

A list of all DataSHIELD packages and their functions /wiki/spaces/DSDEV/pages/12943456 is available.