ipd {ipd} | R Documentation |
The main wrapper function to conduct ipd using various methods and models, and returns a list of fitted model components.
ipd(
formula,
method,
model,
data,
label = NULL,
unlabeled_data = NULL,
seed = NULL,
intercept = TRUE,
alpha = 0.05,
alternative = "two-sided",
n_t = Inf,
na_action = "na.fail",
...
)
formula |
An object of class |
method |
The method to be used for fitting the model. Must be one of
|
model |
The type of model to be fitted. Must be one of |
data |
A |
label |
A |
unlabeled_data |
(optional) A |
seed |
(optional) An |
intercept |
|
alpha |
The significance level for confidence intervals. Default is
|
alternative |
A string specifying the alternative hypothesis. Must be
one of |
n_t |
(integer, optional) Size of the dataset used to train the
prediction function (necessary for the |
na_action |
(string, optional) How missing covariate data should be
handled. Currently |
... |
Additional arguments to be passed to the fitting function. See
the |
1. Formula:
The ipd
function uses one formula argument that specifies both the
calibrating model (e.g., PostPI "relationship model", PPI "rectifier" model)
and the inferential model. These separate models will be created internally
based on the specific method
called.
2. Data:
The data can be specified in two ways:
Single data argument (data
) containing a stacked
data.frame
and a label identifier (label
).
Two data arguments, one for the labeled data (data
) and one
for the unlabeled data (unlabeled_data
).
For option (1), provide one data argument (data
) which contains a
stacked data.frame
with both the unlabeled and labeled data and a
label
argument that specify the column that identifies the labeled
versus the unlabeled observations in the stacked data.frame
NOTE: Labeled data identifiers can be:
"l", "lab", "label", "labeled", "labelled", "tst", "test", "true"
TRUE
Non-reference category (i.e., binary 1)
Unlabeled data identifiers can be:
"u", "unlab", "unlabeled", "unlabelled", "val", "validation", "false"
FALSE
Non-reference category (i.e., binary 0)
For option (2), provide separate data arguments for the labeled data set
(data
) and the unlabeled data set (unlabeled_data
). If the
second argument is provided, the function ignores the label identifier and
assumes the data provided is stacked.
3. Method:
Use the method
argument to specify the fitting method:
Wang et al. (2020) Post-Prediction Inference (PostPI)
Angelopoulos et al. (2023) Prediction-Powered Inference (PPI)
Angelopoulos et al. (2023) PPI++
Miao et al. (2023) Assumption-Lean and Data-Adaptive Post-Prediction Inference (PSPA)
4. Model:
Use the model
argument to specify the type of model:
Mean value of the outcome
q
th quantile of the outcome
Linear regression
Logistic regression
Poisson regression
The ipd
wrapper function will concatenate the method
and
model
arguments to identify the required helper function, following
the naming convention "method_model".
5. Auxiliary Arguments:
The wrapper function will take method-specific auxiliary arguments (e.g.,
q
for the quantile estimation models) and pass them to the helper
function through the "..." with specified defaults for simplicity.
6. Other Arguments:
All other arguments that relate to all methods (e.g., alpha, ci.type), or other method-specific arguments, will have defaults.
a summary of model output.
A list containing the fitted model components:
Estimated coefficients of the model
Standard errors of the estimated coefficients
Confidence intervals for the estimated coefficients
The formula used to fit the ipd model.
The data frame used for model fitting.
The method used for model fitting.
The type of model fitted.
Logical. Indicates if an intercept was included in the model.
Fitted model object containing estimated coefficients, standard errors, confidence intervals, and additional method-specific output.
Additional output specific to the method used.
#-- Generate Example Data
set.seed(12345)
dat <- simdat(n = c(300, 300, 300), effect = 1, sigma_Y = 1)
head(dat)
formula <- Y - f ~ X1
#-- PostPI Analytic Correction (Wang et al., 2020)
ipd(formula, method = "postpi_analytic", model = "ols",
data = dat, label = "set")
#-- PostPI Bootstrap Correction (Wang et al., 2020)
nboot <- 200
ipd(formula, method = "postpi_boot", model = "ols",
data = dat, label = "set", nboot = nboot)
#-- PPI (Angelopoulos et al., 2023)
ipd(formula, method = "ppi", model = "ols",
data = dat, label = "set")
#-- PPI++ (Angelopoulos et al., 2023)
ipd(formula, method = "ppi_plusplus", model = "ols",
data = dat, label = "set")
#-- PSPA (Miao et al., 2023)
ipd(formula, method = "pspa", model = "ols",
data = dat, label = "set")