library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
library(broom)
library(gganimate)
library(cowplot)
library(multiverse)We will be using the same data as the vignette: A complete
implementation of a multiverse analysis (see
vignette("complete-multiverse-analysis")).
data("durante")
df_durante <- durante %>%
mutate(
Abortion = abs(7 - Abortion) + 1,
StemCell = abs(7 - StemCell) + 1,
Marijuana = abs(7 - Marijuana) + 1,
RichTax = abs(7 - RichTax) + 1,
StLiving = abs(7 - StLiving) + 1,
Profit = abs(7 - Profit) + 1,
FiscConsComp = FreeMarket + PrivSocialSec + RichTax + StLiving + Profit,
SocConsComp = Marriage + RestrictAbortion + Abortion + StemCell + Marijuana
)In a multiverse analysis, it may occur that the value of one variable
might depend on the value of another variable defined previously. For
example, in our example, we are excluding participants based on their
cycle length. This can be done in two ways: we can use the
values of the variable,ComputedCycleLength or
ReportedCycleLength. If we are using
ComputedCycleLength to exclude participants, this means
that we should not calculate the variable
NextMenstrualOnset (date for the onset of the next
menstrual cycle) using the ReportedCycleLength value.
Similarly, if we are using ReportedCycleLength to exclude
participants it is inconsistent to calculate
NextMenstrualOnset using
ComputedCycleLength.
We should be able to express these conditions in the multiverse. We
can do this in two ways: 1. %when%: when declaring a
branch, we can use this operator to specify the conditional \(A | B\) as A %when% B. The
conditional \(A | B\) is also referred
to as the connective \(A \implies B\).
This has the meaning “if A is true, then B is also true” and is an
abbreviation for \(\neg A | B\) 2.
branch_assert: this function allows the user to specify any
condition in the form of a logical operation
%when% operatorThere are two ways in which you can specify the %when%
operator. The first is to specify it at the end of the branch. This will
work even if you omit the branch option name.
df <- df %>%
mutate(NextMenstrualOnset = branch(menstrual_calculation,
"mc_option1" ~ (StartDateofLastPeriod + ComputedCycleLength) %when% (cycle_length != "cl_option3"),
"mc_option2" ~ (StartDateofLastPeriod + ReportedCycleLength) %when% (cycle_length != "cl_option2"),
"mc_option3" ~ StartDateNext)
)The other is to specify it at the head of the branch, right after the option name:
df %>%
mutate(NextMenstrualOnset = branch(menstrual_calculation,
"mc_option1" %when% (cycle_length != "cl_option3") ~ StartDateofLastPeriod + ComputedCycleLength,
"mc_option2" %when% (cycle_length != "cl_option2") ~ (StartDateofLastPeriod + ReportedCycleLength),
"mc_option3" ~ StartDateNext)
)Note: In this example we will be using the
inside syntax to enter code into the multiverse, instead of
multiverse code blocks to highlight the syntax of the
conditional declaration as multiverse code blocks shows the
code for a universe, and hides the actual code declared by the user.
We can write the complete analysis by specifying the condition with the %when% operator as:
M = multiverse()
inside(M, {
df.1 <- df_durante %>%
mutate( ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast ) %>%
dplyr::filter( branch(cycle_length,
"cl_option1" ~ TRUE,
"cl_option2" ~ ComputedCycleLength > 25 & ComputedCycleLength < 35,
"cl_option3" ~ ReportedCycleLength > 25 & ReportedCycleLength < 35
)) %>%
mutate(NextMenstrualOnset = branch(menstrual_calculation,
"mc_option1" %when% (cycle_length != "cl_option3") ~ StartDateofLastPeriod + ComputedCycleLength,
"mc_option2" %when% (cycle_length != "cl_option2") ~ StartDateofLastPeriod + ReportedCycleLength,
"mc_option3" ~ StartDateNext)
)
})In multiverse code chunks conditionals can be declared
in pretty much the same way:
```{multiverse default-m-1, inside = M}
df <- df_durante %>%
mutate( ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast ) %>%
dplyr::filter( branch(cycle_length,
"cl_option1" ~ TRUE,
"cl_option2" ~ ComputedCycleLength > 25 & ComputedCycleLength < 35,
"cl_option3" ~ ReportedCycleLength > 25 & ReportedCycleLength < 35
)) %>%
mutate(NextMenstrualOnset = branch(menstrual_calculation,
"mc_option1" %when% (cycle_length != "cl_option3") ~ StartDateofLastPeriod + ComputedCycleLength,
"mc_option2" %when% (cycle_length != "cl_option2") ~ StartDateofLastPeriod + ReportedCycleLength,
"mc_option3" ~ StartDateNext)
)
```Note: in this vignette we used the script-oriented
inside() function for implementing the multiverse. However,
we can implement the exact same multiverse in RMarkdown using the
multiverse-code-block for more interactive programming. To
implement this using a multiverse-code-block, we can simply
place the code passed into the inside function (the second argument)
inside a code block of type multiverse, provide it with the
appropriate labels and multiverse object, and execute it. See
(multiverse-in-rmd) and (branch) for more details and examples.
As the condition implies, the parameter
menstrual_calcaultion (which is for calculating the
variable NextMenstrualCalculation) cannot take the value of
“mc_option1” when we filter cycle_length using
“cl_option3”. Similarly, menstrual_calcaultion cannot take
the value of “mc_option2” when we filter cycle_length using
“cl_option2”. In the multiverse table below, you can see that those two
parameter combinations are absent (universes indexed 3 and 5.
expand(M)## # A tibble: 7 × 7
## .universe cycle_length menstrual_calcu… .parameter_assi… .code .results
## <int> <chr> <chr> <list> <list> <list>
## 1 1 cl_option1 mc_option1 <named list [2]> <named list> <env>
## 2 2 cl_option1 mc_option2 <named list [2]> <named list> <env>
## 3 3 cl_option1 mc_option3 <named list [2]> <named list> <env>
## 4 4 cl_option2 mc_option1 <named list [2]> <named list> <env>
## 5 5 cl_option2 mc_option3 <named list [2]> <named list> <env>
## 6 6 cl_option3 mc_option2 <named list [2]> <named list> <env>
## 7 7 cl_option3 mc_option3 <named list [2]> <named list> <env>
## # … with 1 more variable: .errors <list>branch_assert() functionThe same can be performed with the branch_assert()
function. The benefit of using this is that within the branch assert
function, the user can specify any logical operation.
For eg: the above logical operations can be specified as:
branch_assert((menstrual_calculation != "mc_option1" | cycle_length != "cl_option3"))
Both these operations have the same result, but the first may not be
as easily interpretable. We specify the conditionals using the
branch_assert() function in our example as:
df %>%
branch_assert( (menstrual_calculation != "mc_option1" | (cycle_length != "cl_option3")) ) %>%
branch_assert( (menstrual_calculation != "mc_option2" | (cycle_length != "cl_option2")) )Using the branch_assert(), we can perform the exact same
analysis:
M = multiverse()
inside(M, {
df.2 <- df_durante %>%
mutate( ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast ) %>%
filter( branch(cycle_length,
"cl_option1" ~ TRUE,
"cl_option2" ~ ComputedCycleLength > 25 & ComputedCycleLength < 35,
"cl_option3" ~ ReportedCycleLength > 25 & ReportedCycleLength < 35
)) %>%
mutate(NextMenstrualOnset = branch(menstrual_calculation,
"mc_option1" ~ StartDateofLastPeriod + ComputedCycleLength,
"mc_option2" ~ StartDateofLastPeriod + ReportedCycleLength,
"mc_option3" ~ StartDateNext)
) %>%
branch_assert( (menstrual_calculation != "mc_option1" | (cycle_length != "cl_option3")) ) %>%
branch_assert( (menstrual_calculation != "mc_option2" | (cycle_length != "cl_option2")) )
})As we can see, this results in the same multiverse, where universes indexed 3 and 5 are not compatible.
expand(M)## # A tibble: 7 × 7
## .universe cycle_length menstrual_calcu… .parameter_assi… .code .results
## <int> <chr> <chr> <list> <list> <list>
## 1 1 cl_option1 mc_option1 <named list [2]> <named list> <env>
## 2 2 cl_option1 mc_option2 <named list [2]> <named list> <env>
## 3 3 cl_option1 mc_option3 <named list [2]> <named list> <env>
## 4 4 cl_option2 mc_option1 <named list [2]> <named list> <env>
## 5 5 cl_option2 mc_option3 <named list [2]> <named list> <env>
## 6 6 cl_option3 mc_option2 <named list [2]> <named list> <env>
## 7 7 cl_option3 mc_option3 <named list [2]> <named list> <env>
## # … with 1 more variable: .errors <list>Specifying these conditions allows us to exclude inconsistent combinations from our analyses. Let’s update the example from home page by including these conditions:
M = multiverse()```{multiverse default-m-2, inside = M, echo = FALSE}
df <- df_durante %>%
mutate( ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast ) %>%
dplyr::filter( branch(cycle_length,
"cl_option1" ~ TRUE,
"cl_option2" ~ ComputedCycleLength > 25 & ComputedCycleLength < 35,
"cl_option3" ~ ReportedCycleLength > 25 & ReportedCycleLength < 35
)) %>%
dplyr::filter( branch(certainty,
"cer_option1" ~ TRUE,
"cer_option2" ~ Sure1 > 6 | Sure2 > 6
)) %>%
mutate(NextMenstrualOnset = branch(menstrual_calculation,
"mc_option1" %when% (cycle_length != "cl_option3") ~ StartDateofLastPeriod + ComputedCycleLength,
"mc_option2" %when% (cycle_length != "cl_option2") ~ StartDateofLastPeriod + ReportedCycleLength,
"mc_option3" ~ StartDateNext)
) %>%
mutate(
CycleDay = 28 - (NextMenstrualOnset - DateTesting),
CycleDay = ifelse(CycleDay > 1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28))
) %>%
mutate( Fertility = branch( fertile,
"fer_option1" ~ factor( ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 25, "low", NA)) ),
"fer_option2" ~ factor( ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 27, "low", NA)) ),
"fer_option3" ~ factor( ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >= 18 & CycleDay <= 25, "low", NA)) ),
"fer_option4" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low") ),
"fer_option45" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low") )
)) %>%
mutate(RelationshipStatus = branch(relationship_status,
"rs_option1" ~ factor(ifelse(Relationship==1 | Relationship==2, 'Single', 'Relationship')),
"rs_option2" ~ factor(ifelse(Relationship==1, 'Single', 'Relationship')),
"rs_option3" ~ factor(ifelse(Relationship==1, 'Single', ifelse(Relationship==3 | Relationship==4, 'Relationship', NA))) )
)
```After excluding the inconsistent choice combinations, \(270 − 2 \times (5 \times 1 \times 3 \times 1 \times 2) = 210\) choice combinations remain:
expand(M) %>% nrow()## [1] 210Now, we’ve created the complete multiverse that was presented as example #2 from Steegen et al.’s paper.