SimDesign (version 2.18)

Attach: Attach objects for easier reference

Description

The behaviour of this function is very similar to attach, however it is environment specific, and therefore only remains defined in a given function rather than in the Global Environment. Hence, this function is much safer to use than the attach, which incidentally should never be used in your code. This is useful primarily as a convenience function when you prefer to call the variable names in condition directly rather than indexing with condition$sample_size or with(condition, sample_size), for example.

Usage

Attach(
  ...,
  omit = NULL,
  check = TRUE,
  attach_listone = TRUE,
  RStudio_flags = FALSE
)

Arguments

...

a comma separated list of data.frame, tibble, list, or matrix objects containing (column) elements that should be placed in the current working environment

omit

an optional character vector containing the names of objects that should not be attached to the current environment. For instance, if the objects named 'a' and 'b' should not be attached then use omit = c('a', 'b'). When NULL (default) all objects are attached

check

logical; check to see if the function will accidentally replace previously defined variables with the same names as in condition? Default is TRUE, which will avoid this error

attach_listone

logical; if the element to be assign is a list of length one then assign the first element of this list with the associated name. This generally avoids adding an often unnecessary list 1 index, such as name <- list[[1L]]

RStudio_flags

logical; print R script output comments that disable flagged missing variables in RStudio? Requires the form Attach(Design, RStudio_flags=TRUE) or in an interactive debugging session Attach(condition, RStudio_flags=TRUE)

Author

Phil Chalmers

Details

Note that if you are using RStudio with the "Warn if variable used has no definition in scope" diagnostic flag then using Attach() will raise suspensions. To suppress such issues, you can either disable such flags (the atomic solution) or evaluate the following output in the R console and place the output in your working simulation file.

Attach(Design, RStudio_flags = TRUE)

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")

See Also

runSimulation, Generate

Examples

Design <- createDesign(N1=c(10,20),
                       N2=c(10,20),
                       sd=c(1,2))
Design

# does not use Attach()
Generate <- function(condition, fixed_objects ) {
    # condition = single row of Design input (e.g., condition <- Design[1,])
    N1 <- condition$N1
    N2 <- condition$N2
    sd <- condition$sd

    group1 <- rnorm(N1)
    group2 <- rnorm(N2, sd=sd)
    dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
                      DV = c(group1, group2))
    dat
}

# similar to above, but using the Attach() function instead of indexing
Generate <- function(condition, fixed_objects ) {
    Attach(condition) # N1, N2, and sd are now 'attached' and visible

    group1 <- rnorm(N1)
    group2 <- rnorm(N2, sd=sd)
    dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
                      DV = c(group1, group2))
    dat
}

#####################
# NOTE: if you're using RStudio with code diagnostics on then evaluate + add the
# following output to your source file to manually support the flagged variables

Attach(Design, RStudio_flags=TRUE)

# Below is the same example, however with false positive missing variables suppressed
# when # !diagnostics ... is added added to the source file(s)

# !diagnostics suppress=N1,N2,sd
Generate <- function(condition, fixed_objects ) {
    Attach(condition) # N1, N2, and sd are now 'attached' and visible

    group1 <- rnorm(N1)
    group2 <- rnorm(N2, sd=sd)
    dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
                      DV = c(group1, group2))
    dat
}