Description Usage Arguments Details Value Examples
View source: R/calculateeffectsizes.R
Calculates one or more effect size estimates, along with associated standard errors and confidence intervals, if available, for a singlecase data series.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  batch_calc_ES(
dat,
grouping,
condition,
outcome,
aggregate = NULL,
weighting = "1/V",
session_number = NULL,
baseline_phase = NULL,
intervention_phase = NULL,
ES = c("LRRd", "LRRi", "SMD", "Tau"),
improvement = "increase",
scale = "other",
intervals = NA,
observation_length = NA,
confidence = 0.95,
format = "long",
warn = TRUE,
...
)

dat 
data frame containing SCD series for which effect sizes will be calculated. 
grouping 
A variable name or list of (unquoted) variable names that uniquely identify each data series. 
condition 
A variable name that identifies the treatment condition for each observation in the series. 
outcome 
A variable name for the outcome data. Default is 
aggregate 
A variable name of list of (unquoted) variable names that
identify additional grouping variables. Effect sizes will be calculated
separately for each unique value of these variables, after which the effect
size estimates will be averaged across values of these variables (but not
across the values of the 
weighting 
character string specifying the weighting scheme for use
when variables are specified in 
session_number 
A variable name used to order the data within each series. 
baseline_phase 
character string specifying which value of

intervention_phase 
character string specifying which value of

ES 
character string or character vector specifying which effect size
index or indices to calculate. Available effect sizes are 
improvement 
character string either indicating the direction of uniform improvement ("increase" or "decrease") or the variable name of a variable identifying the direction of improvement for each series. Default is "increase". 
scale 
character string indicating the common scale of the outcome
variable across all of the series in the data set or the name of a variable
within the dataset that identifies the outcome scale within each series.
Possible values for the scale are 
intervals 
for interval recording procedures. Either the total number
of intervals per observation session common to all series in the dataset,
or the name of a variable within the dataset that identifies the number of
intervals for each observation. If a variable name, the mean number of
intervals within each series will be used. Missing values will be ignored.
Defaults to 
observation_length 
Used for the logresponse ratio. Either the common
observation session length (in minutes) across all series in the dataset or
a variable name containing the observation session length for each
observation. If a variable name, the mean observation session length within
each series will be used. Missing values will be ignored. Defaults to

confidence 
confidence level for the reported interval estimate. Set to

format 
character string specifying whether to organize the results in

warn 
logical indicating whether warnings should be displayed. Default
is 
... 
further arguments used for calculating some of the effect size indices. 
Calculates one or more effect size indices for each series in a dataset
A tibble containing the estimate, standard error, and/or confidence interval for each specified effect size.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  data(McKissick)
batch_calc_ES(McKissick,
grouping = Case_pseudonym,
condition = Condition,
outcome = Outcome,
ES = c("LRRd","LRRi"),
improvement = "decrease",
scale = "count",
observation_length = 20,
format = "long")
data(Schmidt2007)
batch_calc_ES(dat = Schmidt2007,
grouping = c(Behavior_type, Case_pseudonym, Phase_num),
condition = Condition,
outcome = Outcome,
ES = c("LRRi","LRRd"),
improvement = direction,
scale = Metric,
bias_correct = TRUE,
confidence = NULL,
format = "wide")
# Aggregate across phasepairs
batch_calc_ES(dat = Schmidt2007,
grouping = c(Behavior_type, Case_pseudonym),
aggregate = Phase_num,
weighting = "1/V",
condition = Condition,
outcome = Outcome,
ES = c("LRRi", "LRRd", "SMD", "Tau"),
improvement = direction,
scale = "count",
bias_correct = TRUE,
confidence = NULL,
format = "long")

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