Estimates differences between groups in preparation for plotting by
DurgaPlot. The formula interface allows the value and group
columns to be specified in a formula, which means, for example, that
transformation functions can be applied to columns.
# S3 method for formula
DurgaDiff(x, data = NULL, id.col, ...)A DurgaDiff object, which is a list containing:
group.statisticsMatrix with a row for each group, columns
are: mean, median, sd (standard deviation), se
(standard error of the mean), CI.lower and CI.upper (lower
and upper bootstrapped confidence intervals of the mean, confidence level
as set by the ci.conf parameter) and n (group sample size).
If there are fewer than 3 distinct values in the group, or if R is
NA, the confidence interval will not be calculated and
CI.lower and CI.upper will be NA.
group.differencesList of DurgaGroupDiff objects,
which are boot objects with added confidence interval information.
See boot and boot.ci. This element will be missing
if contrasts is empty or NULL
groupsVector of group names
group.namesLabels used to identify groups
effect.typeValue of effect.type parameter
effect.nameName of the effect type; may include formatting such as subscripts
effect.name.printText-only version of
effect.name for printing; subscripts are indicated by "_"
data.colValue of data.col parameter; may be an index
or a name
data.col.nameName of the data.col column
group.colValue of group.col parameter; may be an
index or a name
group.col.nameName of the group.col column
id.colValue of id.col parameter. May be NULL
paired.dataTRUE if paired differences
were estimated
dataThe input data frame (x), or the reshaped (long format) data
frame if the input data set was in wide format
callHow this function was called
A DurgaGroupDiff object is a boot object (as returned by
boot) with added bootci components (as returned
by boot.ci) and components identifying the groups used
to estimate the difference. Particularly relevant members are:
t0The observed value of the statistic
bca[4]The lower endpoint of the confidence interval
bca[5]The upper endpoint of the confidence interval
groupsThe difference is estimated on groups[1] -
groups[2]
a formula, such as y ~ grp, where y is a numeric
vector of data values or measurements to be split into groups according to
the grouping variable grp, which is typically a categorical value.
Multiple group columns can be separated by +, in which case Durga treats
each unique combination of group variables as a distinct group.
a data.frame (or list) from which the variables in formula should be taken.
Specify for paired data/repeated measures/with-subject
comparisons only. Name or index of ID column for repeated measures/paired
data. Observations for the same individual must have the same ID. For
non-paired data, do not specify an id.col, (or use id.col =
NA).
Arguments passed on to DurgaDiff.default
groupsVector of group names. Defaults to all groups in x in
natural order. If groups is a named vector, the names are
used as group labels for plotting or printing. If data.col and
group.col are not specified, x is assumed be to in wide
format, and groups must be a list of column names identifying the
group/treatment data (see example).
contrastsSpecify the pairs of groups to be compared. By default, all
pairwise differences are generated. May be a single string, a vector of
strings, or a matrix. Specify
NULL to avoid calculating any contrasts. See Details for more information.
effect.typeType of group difference to be estimated. Values cannot be abbreviated. See Details for further information.
RThe number of bootstrap replicates. R should be larger than
your sample size, so the default value of 1000 may need to be increased for
large sample sizes. If R <= nrow(x), an error such as "Error in
bca.ci... estimated adjustment 'a' is NA" will be thrown. Additionally,
warnings such as "In norm.inter(t, adj.alpha) : extreme order
statistics used as endpoints" may be avoided by increasing R.
Specify R = NA if you do not wish to calculate any CIs, either
for group means for for effect sizes. This may be useful if Durga is
only being used for plotting large data sets.
boot.paramsOptional list of additional names parameters to pass to
the boot function.
ci.confNumeric confidence level of the required confidence interval,
e.g. ci.conf = 0.95 specifies that 95\
be calculated. Applies to both CI of effect sizes and CI of group means.
boot.ci.paramsOptional list of additional names parameters to pass to
the boot.ci function.
na.rma logical evaluating to TRUE or FALSE indicating whether NA
values should be stripped before the computation proceeds. If TRUE
for "paired" data (i.e. id.col is specified), all rows
(observations) for IDs with missing data are stripped.
Applies the formula, x, and a data set, data, to construct a
data frame that is then passed, with all remaining arguments, to the function
DurgaDiff.default.
Cumming, G. (2012). Understanding the new statistics : effect sizes, confidence intervals, and meta-analysis (1st ed.). New York: Routledge.
Delacre, M., Lakens, D., Ley, C., Liu, L., & Leys, C. (2021). Why Hedges' g* based on the non-pooled standard deviation should be reported with Welch's t-test. tools:::Rd_expr_doi("10.31234/osf.io/tu6mp")
Khan, M. K., & McLean, D. J. (2023). Durga: An R package for effect size estimation and visualisation. bioRxiv, 2023.2002.2006.526960. tools:::Rd_expr_doi("10.1101/2023.02.06.526960")
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4. tools:::Rd_expr_doi("10.3389/fpsyg.2013.00863")
d <- DurgaDiff(log(sugar) ~ treatment, insulin, id.col = "id")
print(d)
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