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Create design variables and a design space for a full description of an optimization problem.

Source: R/create_design_space.R
create_design_space.Rd

create_design_space takes an initial design and arguments for a design space and creates a design and design space for design optimization. Checks the sizes of supplied design space variables and changes them to sizes that make sense if there are inconsistencies. Function arguments can use shorthand notation (single values, vectors, lists of vectors and list of list) or matricies. Returns a list of matricies compatible with PopED.

Usage

create_design_space(
  design,
  maxni = NULL,
  minni = NULL,
  maxtotni = NULL,
  mintotni = NULL,
  maxgroupsize = NULL,
  mingroupsize = NULL,
  maxtotgroupsize = NULL,
  mintotgroupsize = NULL,
  maxxt = NULL,
  minxt = NULL,
  xt_space = NULL,
  maxa = NULL,
  mina = NULL,
  a_space = NULL,
  x_space = NULL,
  use_grouped_xt = FALSE,
  grouped_xt = NULL,
  use_grouped_a = FALSE,
  grouped_a = NULL,
  use_grouped_x = FALSE,
  grouped_x = NULL,
  our_zero = NULL
)

Arguments

design

The output from a call to create_design.

maxni

Vector defining the maximum number of samples per group.

minni

Vector defining the minimum number of samples per group.

maxtotni

Number defining the maximum number of samples allowed in the experiment.

mintotni

Number defining the minimum number of samples allowed in the experiment.

maxgroupsize

Vector defining the maximum size of the different groups (maximum number of individuals in each group)

mingroupsize

Vector defining the minimum size of the different groups (minimum num individuals in each group)

maxtotgroupsize

The total maximal groupsize over all groups

mintotgroupsize

The total minimal groupsize over all groups

maxxt

Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value.

minxt

Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value

xt_space

Cell array cell defining the discrete variables allowed for each xt value. Can also be a vector of values c(1:10) (same values allowed for all xt), or a list of lists list(1:10, 2:23, 4:6) (one for each value in xt in row major order or just for one row in xt, and all other rows will be duplicated).

maxa

Vector defining the maximum value for each covariate. IF a single value is supplied then all a values are given the same maximum value

mina

Vector defining the minimum value for each covariate. IF a single value is supplied then all a values are given the same minimum value

a_space

Cell array cell defining the discrete variables allowed for each a value. Can also be a list of values list(1:10) (same values allowed for all a), or a list of lists list(1:10, 2:23, 4:6) (one for each value in a).

x_space

Cell array cell defining the discrete variables for each x value.

use_grouped_xt

Group sampling times between groups so that each group has the same values (TRUE or FALSE).

grouped_xt

Matrix defining the grouping of sample points. Matching integers mean that the points are matched. Allows for finer control than use_grouped_xt

use_grouped_a

Group continuous design variables between groups so that each group has the same values (TRUE or FALSE).

grouped_a

Matrix defining the grouping of continuous design variables. Matching integers mean that the values are matched. Allows for finer control than use_grouped_a.

use_grouped_x

Group discrete design variables between groups so that each group has the same values (TRUE or FALSE).

grouped_x

Matrix defining the grouping of discrete design variables. Matching integers mean that the values are matched. Allows for finer control than use_grouped_x.

our_zero

Value to interpret as zero in design.

Details

If a value (or a vector or a list of values) is supplied that corresponds to only one group and the design has multiple groups then all groups will have the same value(s). If a matrix is expected then a list of lists can be supplied instead, each list corresponding to a group.

See also

Other poped_input: convert_variables(), create.poped.database(), create_design(), downsizing_general_design(), poped.choose()

Examples

library(PopED)

design_1 <- create_design(xt=list(c(1,2,3,4,5),
                                  c(1,2,3,4)),
                          groupsize=c(50,20),
                          a=list(c(WT=70,DOSE=1000),
                                 c(DOSE=1000,WT=35)))

ds_1 <- create_design_space(design_1)

ds_1_a <- create_design_space(design_1,our_zero = 1e-5)

ds_2 <- create_design_space(design_1,maxni=10,maxxt=10,minxt=0)

ds_3 <- create_design_space(design_1,maxni=10,mingroupsize=20,maxxt=10,minxt=0)

ds_4 <- create_design_space(design_1,maxa=c(100,2000))

ds_5 <- create_design_space(design_1,mina=c(10,20))

design_2 <- create_design(xt=list(c(1,2,3,4,5),
                                  c(1,2,3,4)),
                          groupsize=c(50,20),
                          a=list(c(WT=70,DOSE=1000),
                                 c(WT=35,DOSE=1000)),
                          x=list(c(SEX=1,DOSE_discrete=100),
                                 c(SEX=2,DOSE_discrete=200)))

ds_6 <- create_design_space(design_2) 

ds_7 <- create_design_space(design_2,
                            x_space=list(SEX=c(1,2),
                                         DOSE_discrete=seq(100,400,by=20)))

ds_8 <- create_design_space(design_2,
                            x_space=list(SEX=c(1,2),
                                         DOSE_discrete=seq(100,400,by=20)),
                            grouped_xt=c(1,2,3,4,5))

ds_9 <- create_design_space(design_2,
                            x_space=list(SEX=c(1,2),
                                         DOSE_discrete=seq(100,400,by=20)),
                            use_grouped_xt=TRUE)

design_3 <- create_design(xt=list(c(1,2,3,4,5),
                                  c(1,2,3,4)),
                          groupsize=c(50,20),
                          a=list(c(WT=35,DOSE=1000)),
                          x=list(c(SEX=1,DOSE_discrete=100)))

ds_10 <- create_design_space(design_3,
                             x_space=list(SEX=c(1,2),DOSE_discrete=seq(100,400,by=20)),
                             use_grouped_a=TRUE)

ds_11 <- create_design_space(design_2,
                             x_space=list(SEX=c(1,2),DOSE_discrete=seq(100,400,by=20)),
                             grouped_a=list(c(1,2),c(3,2)))

ds_12 <- create_design_space(design_3,
                             x_space=list(SEX=c(1,2),DOSE_discrete=seq(100,400,by=20)),
                             use_grouped_x=TRUE)

ds_13 <- create_design_space(design_3,
                             x_space=list(SEX=c(1,2),DOSE_discrete=seq(100,400,by=20)),
                             grouped_x=list(c(1,2),c(3,2)))

seq_1 <- 1:10
ds_14 <- create_design_space(design_1,maxxt=10,minxt=0,
                             xt_space = list(seq_1,seq_1,seq_1,seq_1,seq_1))
ds_15 <- create_design_space(design_1,maxxt=10,minxt=0,xt_space = list(seq_1))

possible_values <- as.matrix(cbind(list(0:10),list(0:10),list(0:10),list(0:20),list(0:20)))
xt_space <- as.matrix(rbind(possible_values,possible_values))

ds_16 <- create_design_space(design_1,maxxt=10,minxt=0,xt_space = xt_space)

ds_17 <- create_design_space(design_1,a_space = list(1:100,seq(1000,100000,by=1000)))