View source: R/plot.logis_fe.R
plot.logis_fe | R Documentation |
logis_fe
object for institutional comparisonsCreates a funnel plot from a logistic fixed effect model to compare provider performance.
## S3 method for class 'logis_fe'
plot(
x,
null = "median",
test = "score",
target = 1,
alpha = 0.05,
labels = c("lower", "expected", "higher"),
point_colors = c("#E69F00", "#56B4E9", "#009E73"),
point_shapes = c(15, 17, 19),
point_size = 2,
point_alpha = 0.8,
line_size = 0.8,
target_line_type = "longdash",
...
)
x |
a model fitted from |
null |
a character string or a number specifying null hypotheses of fixed provider effects. The default is |
test |
a character string specifying the type of testing methods to be conducted. The default is "score". |
target |
a numeric value representing the target outcome. The default value is 1. |
alpha |
a number or a vector of significance levels. The default is 0.05. |
labels |
a vector of labels for the plot. |
point_colors |
a vector of colors representing different provider flags. The default is |
point_shapes |
a vector of shapes representing different provider flags. The default is |
point_size |
size of the points. The default is 2. |
point_alpha |
transparency level of the points. The default is 0.8. |
line_size |
size of all lines, including control limits and the target line. The default is 0.8. |
target_line_type |
line type for the target line. The default is "longdash". |
... |
additional arguments that can be passed to the function. |
This function generates a funnel plot from a logistic fixed-effect model. Currently, it only supports the indirect standardized ratio.
The parameter alpha
is a vector used to calculate control limits at different significance levels.
The first value in the vector is used as the significance level for flagging each provider, utilizing the test.logis_fe
function.
A ggplot object representing the funnel plot.
Wu, W., Kuriakose, J. P., Weng, W., Burney, R. E., & He, K. (2023). Test-specific funnel plots for healthcare provider profiling leveraging
individual- and summary-level information. Health Services and Outcomes Research Methodology, 23(1), 45-58.
logis_fe
, SM_output.linear_re
, test.logis_fe
data(ExampleDataBinary)
outcome <- ExampleDataBinary$Y
covar <- ExampleDataBinary$Z
ID <- ExampleDataBinary$ID
fit_fe <- logis_fe(Y = outcome, Z = covar, ID = ID)
plot(fit_fe)
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