dropbig | R Documentation |
dropbig
is useful to identify replications with large point estimates or standard errors. Large values are defined as standardised values above a given threshold, as defined when calling dropbig
. Regular standardisation using mean and standard deviation is implemented, as well as robust standardisation using median and inter-quartile range. Further to that, the standardisation process is stratified by data-generating mechanism if by
factors are defined.
dropbig(
data,
estvarname,
se = NULL,
methodvar = NULL,
by = NULL,
max = 10,
semax = 100,
robust = TRUE
)
data |
A |
estvarname |
The name of the variable containing the point estimates. |
se |
The name of the variable containing the standard errors of the point estimates. |
methodvar |
The name of the variable containing the methods to compare. For instance, methods could be the models compared within a simulation study. Can be |
by |
A vector of variable names to compute performance measures by a list of factors. Factors listed here are the (potentially several) data-generating mechanisms used to simulate data under different scenarios (e.g. sample size, true distribution of a variable, etc.). Can be |
max |
Specifies the maximum acceptable absolute value of the point estimates, after standardisation. Defaults to 10. |
semax |
Specifies the maximum acceptable absolute value of the standard error, after standardisation. Defaults to 100. |
robust |
Specifies whether to use robust standardisation (using median and inter-quartile range) rather than normal standardisation (using mean and standard deviation). Defaults to |
The same data.frame
given as input with an additional column named .dropbig
identifying rows that are classified as large (.dropbig = TRUE
) according to the specified criterion.
data("frailty", package = "rsimsum")
frailty2 <- subset(frailty, par == "fv")
# Using low values of max, semax for illustration purposes:
dropbig(
data = frailty2, estvarname = "b", se = "se",
methodvar = "model", by = "fv_dist", max = 2, semax = 2
)
# Using regular standardisation:
dropbig(
data = frailty2, estvarname = "b", se = "se",
methodvar = "model", by = "fv_dist", max = 2, semax = 2, robust = FALSE
)
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