LOR.Rd
Calculates the log-odds ratio effect size index, with or without bias correction (Pustejovsky, 2015)
LOR(
A_data,
B_data,
condition,
outcome,
baseline_phase = NULL,
intervention_phase = NULL,
improvement = "increase",
scale = "percentage",
intervals = NULL,
D_const = NULL,
bias_correct = TRUE,
confidence = 0.95
)
vector of numeric data for A phase. Missing values are dropped.
vector of numeric data for B phase. Missing values are dropped.
vector identifying the treatment condition for each observation in the series.
vector of outcome data for the entire series.
character string specifying which value of
condition
corresponds to the baseline phase. Defaults to first
observed value of condition
.
character string specifying which value of
condition
corresponds to the intervention phase. Defaults to second
unique value of condition
.
character string indicating direction of improvement. Default is "increase".
character string indicating the scale of the outcome variable.
Must be either "percentage"
for percentages with range 0-100 or
"proportion"
for proportions with range 0-1. If a vector, the most
frequent unique value will be used. "percentage"
is assumed by
default.
for interval recording procedures, the total number of intervals per observation session. If a vector, the mean number of intervals will be used.
constant used for calculating the truncated sample mean (see Pustejovsky, 2015). If a vector, the mean value will be used.
logical value indicating whether to use bias-correction.
Default is TRUE
.
confidence level for the reported interval estimate. Set to
NULL
to omit confidence interval calculations.
A data.frame containing the estimate, standard error, and approximate confidence interval.
The odds ratio parameter is the ratio of the odds of the outcome. The log-odds ratio is the natural logarithm of the odds ratio. This effect size is appropriate for outcomes measured on a percentage or proportion scale. Unlike the LRRd and LRRi, the LOR is symmetric in valence, so that the LOR for an positively-valenced outcome is equal to -1 times the LOR calculated after reversing the scale of the outcome so that it is negatively valenced.
Without bias correction, the log-odds ratio is estimated by substituting the sample mean level in each phase in place of the corresponding parameter. A delta-method bias correction to the estimator is used by default.
The standard error of LOR is calculated based on a delta-method approximation, allowing for the possibility of different degrees of dispersion in each phase. The confidence interval for LOR is based on a large-sample (z) approximation.
To account for the possibility of sample means of zero, a truncated mean is
calculated following the method described in Pustejovsky (2015). Truncated
sample variances are also calculated to ensure that standard errors will be
strictly larger than zero. The truncation constant depends on the total
number of intervals per session (or the total number of items for other
percentage/proportion scales). The arguments scale
and
intervals
must be specified in order to calculate an appropriate
truncation constant. For outcomes measured using continuous recording
procedures, set intervals
equal to 60 times the length of the
observation session in minutes.
Pustejovsky, J. E. (2015). Measurement-comparable effect sizes for single-case studies of free-operant behavior. Psychological Methods, 20(3), 342–359. doi:doi:10.1037/met0000019
A_pct <- c(20, 20, 25, 25, 20, 25)
B_pct <- c(30, 25, 25, 25, 35, 30, 25)
LOR(A_data = A_pct, B_data = B_pct,
scale = "percentage", intervals = 20, bias_correct = FALSE)
#> ES Est SE CI_lower CI_upper
#> 1 LOR 0.2852038 0.09790282 0.09331776 0.4770898
LOR(A_data = A_pct, B_data = B_pct,
scale = "percentage", intervals = 20)
#> ES Est SE CI_lower CI_upper
#> 1 LOR 0.2852854 0.09790282 0.09339935 0.4771713
LOR(A_data = A_pct, B_data = B_pct, scale = "percentage")
#> ES Est SE CI_lower CI_upper
#> 1 LOR 0.2852854 0.09790282 0.09339935 0.4771713
LOR(A_data = A_pct / 100, B_data = B_pct / 100, scale = "proportion")
#> ES Est SE CI_lower CI_upper
#> 1 LOR 0.2852854 0.09790282 0.09339935 0.4771713
LOR(A_data = A_pct, B_data = B_pct, scale = "percentage", improvement = "decrease")
#> ES Est SE CI_lower CI_upper
#> 1 LOR -0.2852854 0.09790282 -0.4771713 -0.09339935