Reference

-- B -- balnet()

-- C -- coef.balnet() coef.cv.balnet() cv.balnet()

-- P -- plot.balnet() plot.cv.balnet() predict.balnet() predict.cv.balnet() print.balnet() print.cv.balnet()

Pathwise estimation of covariate balancing propensity scores.

Description

Fits regularized logistic regression models using covariate balancing loss functions, targeting the ATE, ATT, or treated/control means.

Usage

balnet(
  X,
  W,
  target = c("ATE", "ATT", "treated", "control"),
  sample.weights = NULL,
  nlambda = 100L,
  lambda.min.ratio = 0.01,
  lambda = NULL,
  penalty.factor = NULL,
  groups = NULL,
  alpha = 1,
  standardize = TRUE,
  thresh = 1e-07,
  maxit = as.integer(1e+05),
  verbose = FALSE,
  num.threads = 1L,
  ...
)

Arguments

X

A numeric matrix or data frame with pre-treatment covariates.

W

Treatment vector (0: control, 1: treated).

target

The target estimand. Default is ATE.

sample.weights

Optional sample weights. If NULL (default), then each unit receives the same weight.

nlambda

Number of values for lambda, if generated automatically. Default is 100.

lambda.min.ratio

Ratio between smallest and largest value of lambda. Default is 1e-2.

lambda

Optional lambda sequence. By default, the lambda sequence is constructed automatically using nlambda and lambda.min.ratio.

penalty.factor

Penalty factor per feature. Default is 1 (i.e, each feature recieves the same penalty).

groups

An optional list of group indices for group penalization.

alpha

Elastic net mixing parameter. Default is 1 (lasso). 0 is ridge.

standardize

Whether to standardize the input matrix. This should only be set to FALSE if X already has zero-mean columns with unit variance; for target = "ATT", standardization should be based on the treated group. Alternatively, set to "inplace" to overwrite X with its standardized version, avoiding an additional copy.

thresh

Coordinate descent convergence tolerance, default 1e-7.

maxit

Maximum total number of coordinate descent iterations, default is 1e5.

verbose

Whether to display information during fitting. Default is FALSE.

num.threads

Number of threads, default is 1.

...

Additional internal arguments passed to solver.

Value

A fit balnet object.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
fit <- balnet(X, W)

# Print path summary.
print(fit)

#> Call:  balnet(X = X, W = W) 
#> 
#> Control (path: 51/100)
#>    Nonzero Mean |SMD|  Lambda
#> 1        0    0.05624 0.34194
#> 2        1    0.05580 0.32640
#> 3        1    0.05542 0.31157
#> ...
#> 49      16    0.02941 0.03667
#> 50      17    0.02828 0.03500
#> 51      18    0.02796 0.03341
#> 
#> Treated (path: 31/100)
#>    Nonzero Mean |SMD|  Lambda
#> 1        0    0.13123 0.79787
#> 2        1    0.12886 0.76161
#> 3        1    0.12915 0.72699
#> ...
#> 29       9    0.14532 0.21691
#> 30       9    0.14071 0.20705
#> 31      10    0.13410 0.19764

# Plot path diagnostics.
plot(fit)

A plot recorded by xfun::record()

# Plot covariate imbalance at the end of the path (closest to lambda = 0).
plot(fit, lambda = 0)

A plot recorded by xfun::record()

# Predict propensity scores.
pp <- predict(fit, X)

# Extract coefficients.
coefs <- coef(fit)

Extract coefficients from a balnet object.

Description

Extract coefficients from a balnet object.

Usage

## S3 method for class 'balnet'
coef(object, lambda = NULL, ...)

Arguments

object

A balnet object.

lambda

Value(s) of the penalty parameter lambda at which coefficients are required.

  • If NULL (default), the full lambda path from the fit is used (if new values are supplied, linear interpolation is performed).

  • For dual-arm fits (control and treatment), lambda can be a list or two-column matrix: the first element/column corresponds to the control arm and the second to the treatment arm.

...

Additional arguments (currently ignored).

Value

The estimated coefficients.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
fit <- balnet(X, W)

# Extract coefficients.
coefs <- coef(fit)

Extract coefficients from a cv.balnet object.

Description

Extract coefficients from a cv.balnet object.

Usage

## S3 method for class 'cv.balnet'
coef(object, lambda = "lambda.min", ...)

Arguments

object

A cv.balnet object.

lambda

The lambda to use. Defaults to the cross-validated lambda.

...

Additional arguments (currently ignored).

Value

The estimated coefficients.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
cv.fit <- cv.balnet(X, W)

# Extract coefficients at cross-validated lambda.
coefs <- coef(cv.fit)

Cross-validation for balnet.

Description

Cross-validation for balnet.

Usage

cv.balnet(
  X,
  W,
  type.measure = c("balance.loss"),
  nfolds = 10,
  foldid = NULL,
  ...
)

Arguments

X

A numeric matrix or data frame with pre-treatment covariates.

W

Treatment vector (0: control, 1: treated).

type.measure

The loss to minimize for cross-validation. Default is balance loss.

nfolds

The number of folds used for cross-validation, default is 10.

foldid

An optional n-vector specifying which fold 1 to nfold a sample belongs to. If NULL, this defaults to sample(rep(seq(nfolds), length.out = nrow(X))).

...

Arguments for balnet.

Value

A fit cv.balnet object.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
cv.fit <- cv.balnet(X, W)

# Print CV summary.
print(cv.fit)

#> Call:  cv.balnet(X = X, W = W) 
#> 
#> Lambda min (balance loss):
#>  Arm     Nonzero Mean |SMD| Lambda  Index
#>  Control 16      0.03727    0.04918 36   
#>  Treated 10      0.05971    0.08806 34   

# Plot at cross-validated lambda.
plot(cv.fit)

A plot recorded by xfun::record()

# Predict at cross-validated lambda.
pp <- predict(cv.fit, X)

# Extract coefficients at cross-validated lambda.
coefs <- coef(cv.fit)

Plot diagnostics for a balnet object.

Description

Plot diagnostics for a balnet object.

Usage

## S3 method for class 'balnet'
plot(x, lambda = NULL, groups = NULL, max = NULL, ...)

Arguments

x

A balnet object.

lambda

If NULL (default) diagnostics over the lambda path is shown. Otherwise, diagnostics for a single lambda value is shown. (if target = "ATE", lambda can be a 2-vector, arm 0 and arm 1.)

groups

A list of group indices.

max

The number of covariates to display in balance plot. Defaults to all covariates.

...

Additional arguments.

Value

Invisibly returns a list with the information underlying the plot.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
fit <- balnet(X, W)

# Plot path diagnostics.
plot(fit)

A plot recorded by xfun::record()

Plot diagnostics for a cv.balnet object.

Description

Plot diagnostics for a cv.balnet object.

Usage

## S3 method for class 'cv.balnet'
plot(x, lambda = "lambda.min", ...)

Arguments

x

A cv.balnet object.

lambda

The lambda to use. Defaults to the cross-validated lambda.

...

Additional arguments.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
cv.fit <- cv.balnet(X, W)

# Plot at cross-validated lambda.
plot(cv.fit)

A plot recorded by xfun::record()

Predict using a balnet object.

Description

Predict using a balnet object.

Usage

## S3 method for class 'balnet'
predict(object, newx, lambda = NULL, type = c("response"), ...)

Arguments

object

A balnet object.

newx

A numeric matrix.

lambda

Value(s) of the penalty parameter lambda at which coefficients are required.

  • If NULL (default), the full lambda path from the fit is used (if new values are supplied, linear interpolation is performed).

  • For dual-arm fits (control and treatment), lambda can be a list or two-column matrix: the first element/column corresponds to the control arm and the second to the treatment arm.

type

The type of predictions.

...

Additional arguments (currently ignored).

Value

Predictions.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
fit <- balnet(X, W)

# Predict propensity scores.
pp <- predict(fit, X)

Predict using a cv.balnet object.

Description

Predict using a cv.balnet object.

Usage

## S3 method for class 'cv.balnet'
predict(object, newx, lambda = "lambda.min", type = c("response"), ...)

Arguments

object

A cv.balnet object.

newx

A numeric matrix.

lambda

The lambda to use. Defaults to the cross-validated lambda.

type

The type of predictions.

...

Additional arguments (currently ignored).

Value

Predictions.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
cv.fit <- cv.balnet(X, W)

# Predict at cross-validated lambda.
pp <- predict(cv.fit, X)

Print a balnet object.

Description

Print a balnet object.

Usage

## S3 method for class 'balnet'
print(x, digits = max(3L, getOption("digits") - 3L), max = 3, ...)

Arguments

x

A balnet object.

digits

Number of digits to print.

max

Total number of rows to show from the beginning and end of the path

...

Additional print arguments.

Value

Invisibly returns a data.frame with the printed information.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
fit <- balnet(X, W)

# Print path summary.
print(fit)

#> Call:  balnet(X = X, W = W) 
#> 
#> Control (path: 51/100)
#>    Nonzero Mean |SMD|  Lambda
#> 1        0    0.04090 0.16682
#> 2        1    0.04106 0.15924
#> 3        1    0.04121 0.15200
#> ...
#> 49      20    0.01589 0.01789
#> 50      20    0.01518 0.01707
#> 51      20    0.01483 0.01630
#> 
#> Treated (path: 21/100)
#>    Nonzero Mean |SMD|  Lambda
#> 1        0    0.14500 0.59147
#> 2        1    0.14460 0.56458
#> 3        1    0.14488 0.53892
#> ...
#> 19       8    0.15084 0.25603
#> 20       8    0.15647 0.24439
#> 21      12    0.16712 0.23329

Print a cv.balnet object.

Description

Print a cv.balnet object.

Usage

## S3 method for class 'cv.balnet'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

x

A cv.balnet object.

digits

Number of digits to print.

...

Additional print arguments.

Examples

n <- 100
p <- 25
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(1 - X[, 1])))

# Fit an ATE model.
cv.fit <- cv.balnet(X, W)

# Print CV summary.
print(cv.fit)

#> Call:  cv.balnet(X = X, W = W) 
#> 
#> Lambda min (balance loss):
#>  Arm     Nonzero Mean |SMD| Lambda  Index
#>  Control 8       0.05793    0.08841 31   
#>  Treated 4       0.15939    0.28775 18