Help for package prediction

Type:

Package

Title:

Tidy, Type-Safe 'prediction()' Methods

Description:

A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' https://cran.r-project.org/package=margins. The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing.

License:

MIT + file LICENSE

Version:

0.3.18

URL:

https://github.com/bbolker/prediction

BugReports:

https://github.com/bbolker/prediction/issues

Depends:

R (≥ 3.5.0)

Imports:

utils, stats, data.table

Suggests:

datasets, methods, testthat

Enhances:

AER, aod, betareg, biglm, brglm, caret, crch, e1071, earth, ff, gam (≥ 1.15), gee, glmnet, glmx, kernlab, lme4, MASS, mclogit, mda, mlogit, MNP, nlme, nnet, ordinal, plm, pscl, quantreg, rpart, sampleSelection, speedglm, survey (≥ 3.31-5), survival, truncreg, VGAM

ByteCompile:

true

Encoding:

UTF-8

RoxygenNote:

7.3.1

NeedsCompilation:

Packaged:

2024-06-11 13:27:53 UTC; bolker

Author:

Thomas J. Leeper

[aut], Carl Ganz [ctb], Vincent Arel-Bundock

[ctb], Ben Bolker

[ctb, cre]

Maintainer:

Ben Bolker <bolker@mcmaster.ca>

Repository:

CRAN

Date/Publication:

2024-06-11 15:20:02 UTC

Extract Predictions from a Model Object

Description

Extract predicted values via predict from a model object, conditional on data, and return a data frame.

Usage

prediction(model, ...)

## Default S3 method:
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = "response",
  vcov = stats::vcov(model),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'Arima'
prediction(model, calculate_se = TRUE, ...)

## S3 method for class 'ar'
prediction(model, data, at = NULL, calculate_se = TRUE, ...)

## S3 method for class 'arima0'
prediction(model, data, at = NULL, calculate_se = TRUE, ...)

## S3 method for class 'betareg'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link", "precision", "variance", "quantile"),
  calculate_se = FALSE,
  ...
)

## S3 method for class 'bigLm'
prediction(model, data = NULL, calculate_se = FALSE, ...)

## S3 method for class 'bigglm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = "response",
  calculate_se = TRUE,
  ...
)

## S3 method for class 'biglm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = "response",
  calculate_se = TRUE,
  ...
)

## S3 method for class 'bruto'
prediction(
  model,
  data = NULL,
  at = NULL,
  type = "fitted",
  calculate_se = FALSE,
  ...
)

## S3 method for class 'clm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = TRUE,
  category,
  ...
)

## S3 method for class 'coxph'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("risk", "expected", "lp"),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'crch'
prediction(
  model,
  data = find_data(model),
  at = NULL,
  type = c("response", "location", "scale", "quantile"),
  calculate_se = FALSE,
  ...
)

## S3 method for class 'earth'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  calculate_se = TRUE,
  category,
  ...
)

## S3 method for class 'fda'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'Gam'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link", "terms"),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'gausspr'
prediction(
  model,
  data,
  at = NULL,
  type = NULL,
  calculate_se = TRUE,
  category,
  ...
)

## S3 method for class 'gee'
prediction(model, calculate_se = FALSE, ...)

## S3 method for class 'glimML'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'glimQL'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'glm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  vcov = stats::vcov(model),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'glmnet'
prediction(
  model,
  data,
  lambda = model[["lambda"]][1L],
  at = NULL,
  type = c("response", "link"),
  calculate_se = FALSE,
  ...
)

## S3 method for class 'glmx'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  calculate_se = FALSE,
  ...
)

## S3 method for class 'gls'
prediction(
  model,
  data = find_data(model),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'hetglm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link", "scale"),
  calculate_se = FALSE,
  ...
)

## S3 method for class 'hurdle'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "count", "prob", "zero"),
  calculate_se = FALSE,
  ...
)

## S3 method for class 'hxlr'
prediction(
  model,
  data = find_data(model),
  at = NULL,
  type = c("class", "probability", "cumprob", "location", "scale"),
  calculate_se = FALSE,
  ...
)

## S3 method for class 'ivreg'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'knnreg'
prediction(model, data, at = NULL, calculate_se = FALSE, ...)

## S3 method for class 'kqr'
prediction(model, data, at = NULL, calculate_se = FALSE, ...)

## S3 method for class 'ksvm'
prediction(
  model,
  data,
  at = NULL,
  type = NULL,
  calculate_se = TRUE,
  category,
  ...
)

## S3 method for class 'lda'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'lm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = "response",
  vcov = stats::vcov(model),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'lme'
prediction(
  model,
  data = find_data(model),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'loess'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = "response",
  calculate_se = TRUE,
  ...
)

## S3 method for class 'lqs'
prediction(
  model,
  data = find_data(model),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'mars'
prediction(
  model,
  data = NULL,
  at = NULL,
  type = "fitted",
  calculate_se = FALSE,
  ...
)

## S3 method for class 'mca'
prediction(
  model,
  data = find_data(model),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'mclogit'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = "response",
  vcov = stats::vcov(model),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'merMod'
prediction(
  model,
  data = find_data(model),
  at = NULL,
  type = c("response", "link"),
  re.form = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'mlogit'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'mnlogit'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'mnp'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'multinom'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'naiveBayes'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'nls'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'nnet'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'plm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'polr'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'polyreg'
prediction(
  model,
  data = NULL,
  at = NULL,
  type = "fitted",
  calculate_se = FALSE,
  ...
)

## S3 method for class 'ppr'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'princomp'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'qda'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'rlm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = "response",
  vcov = stats::vcov(model),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'rpart'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'rq'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = TRUE,
  ...
)

## S3 method for class 'selection'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = "response",
  calculate_se = FALSE,
  ...
)

## S3 method for class 'speedglm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  calculate_se = FALSE,
  ...
)

## S3 method for class 'speedlm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  calculate_se = FALSE,
  ...
)

## S3 method for class 'survreg'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "lp", "quantile", "uquantile"),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'svm'
prediction(model, data = NULL, at = NULL, calculate_se = TRUE, category, ...)

## S3 method for class 'svyglm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  calculate_se = TRUE,
  ...
)

## S3 method for class 'train'
prediction(
  model,
  data = find_data(model),
  at = NULL,
  type = c("raw", "prob"),
  ...
)

## S3 method for class 'tree'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = NULL,
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'truncreg'
prediction(model, data, at = NULL, calculate_se = FALSE, ...)

## S3 method for class 'vgam'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  calculate_se = FALSE,
  category,
  ...
)

## S3 method for class 'vglm'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "link"),
  calculate_se = TRUE,
  category,
  ...
)

## S3 method for class 'zeroinfl'
prediction(
  model,
  data = find_data(model, parent.frame()),
  at = NULL,
  type = c("response", "count", "prob", "zero"),
  calculate_se = FALSE,
  ...
)

prediction_summary(model, ..., level = 0.95)

Arguments

model

A model object, perhaps returned by lm or glm.

...

Additional arguments passed to predict methods.

data

A data.frame over which to calculate marginal effects. If missing, find_data is used to specify the data frame.

at

A list of one or more named vectors, specifically values at which to calculate the predictions. These are used to modify the value of data (see build_datalist for details on use).

type

A character string indicating the type of marginal effects to estimate. Mostly relevant for non-linear models, where the reasonable options are “response” (the default) or “link” (i.e., on the scale of the linear predictor in a GLM). For models of class “polr” (from polr), possible values are “class” or “probs”; both are returned.

vcov

A matrix containing the variance-covariance matrix for estimated model coefficients, or a function to perform the estimation with model as its only argument.

calculate_se

A logical indicating whether to calculate standard errors for observation-specific predictions and average predictions (if possible). The output will always contain a “calculate_se” column regardless of this value; this only controls the calculation of standard errors. Setting it to FALSE may improve speed.

category

For multi-level or multi-category outcome models (e.g., ordered probit, multinomial logit, etc.), a value specifying which of the outcome levels should be used for the "fitted" column. If missing, some default is chosen automatically.

lambda

For models of class “glmnet”, a value of the penalty parameter at which predictions are required.

re.form

An argument passed forward to predict.merMod.

level

A numeric value specifying the confidence level for calculating p-values and confidence intervals.

Details

This function is simply a wrapper around predict that returns a data frame containing the value of data and the predicted values with respect to all variables specified in data.

Methods are currently implemented for the following object classes:

“lm”, see lm
“glm”, see glm, glm.nb, glmx, hetglm, brglm
“ar”, see ar
“Arima”, see arima
“arima0”, see arima0
“bigglm”, see bigglm
“betareg”, see betareg
“bruto”, see bruto
“clm”, see clm
“coxph”, see coxph
“crch”, see crch
“earth”, see earth
“fda”, see fda
“Gam”, see gam
“gausspr”, see gausspr
“gee”, see gee
“glmnet”, see glmnet
“gls”, see gls
“glimML”, see betabin, negbin
“glimQL”, see quasibin, quasipois
“hurdle”, see hurdle
“hxlr”, see hxlr
“ivreg”, see ivreg
“knnreg”, see knnreg
“kqr”, see kqr
“ksvm”, see ksvm
“lda”, see lda
“lme”, see lme
“loess”, see loess
“lqs”, see lqs
“mars”, see mars
“mca”, see mca
“mclogit”, see mclogit
“mda”, see mda
“merMod”, see lmer, glmer
“mnp”, see mnp
“naiveBayes”, see naiveBayes
“nlme”, see nlme
“nls”, see nls
“nnet”, see nnet
“plm”, see plm
“polr”, see polr
“polyreg”, see polyreg
“ppr”, see ppr
“princomp”, see princomp
“qda”, see qda
“rlm”, see rlm
“rpart”, see rpart
“rq”, see rq
“selection”, see selection
“speedglm”, see speedglm
“speedlm”, see speedlm
“survreg”, see survreg
“svm”, see svm
“svyglm”, see svyglm
“tobit”, see tobit
“train”, see train
“truncreg”, see truncreg
“zeroinfl”, see zeroinfl

Where implemented, prediction also returns average predictions (and the variances thereof). Variances are implemented using the delta method, as described by Xu and Long 2005 doi:10.1177/1536867X0500500405.

Value

A data frame with class “prediction” that has a number of rows equal to number of rows in data, or a multiple thereof, if !is.null(at). The return value contains data (possibly modified by at using build_datalist), plus a column containing fitted/predicted values ("fitted") and a column containing the standard errors thereof ("calculate_se"). Additional columns may be reported depending on the object class. The data frame also carries attributes used by print and summary, which will be lost during subsetting.

Examples

require("datasets")
x <- lm(Petal.Width ~ Sepal.Length * Sepal.Width * Species, data = iris)
# prediction for every case
prediction(x)

# prediction for first case
prediction(x, iris[1,])

# basic use of 'at' argument
summary(prediction(x, at = list(Species = c("setosa", "virginica"))))

# basic use of 'at' argument
prediction(x, at = list(Sepal.Length = seq_range(iris$Sepal.Length, 5)))

# prediction at means/modes of input variables
prediction(x, at = lapply(iris, mean_or_mode))

# prediction with multi-category outcome
## Not run: 
  library("mlogit")
  data("Fishing", package = "mlogit")
  Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide", choice = "mode")
  mod <- mlogit(mode ~ price + catch, data = Fish)
  prediction(mod)
  prediction(mod, category = 3)

## End(Not run)

Build list of data.frames

Description

Construct a list of data.frames based upon an input data.frame and a list of one or more at values

Usage

build_datalist(data, at = NULL, as.data.frame = FALSE, ...)

Arguments

data

A data.frame containing the original data.

at

A list of one or more named vectors of values, which will be used to specify values of variables in data. All possible combinations are generated. Alternatively, this can be a data frame of combination levels if only a subset of combinations are desired. See examples.

as.data.frame

A logical indicating whether to return a single stacked data frame rather than a list of data frames

...

Ignored.

Value

A list of data.frames, unless as.data.frame = TRUE in which case a single, stacked data frame is returned.

Author(s)

Thomas J. Leeper

Examples

# basic examples
require("datasets")
build_datalist(head(mtcars), at = list(cyl = c(4, 6)))

str(build_datalist(head(mtcars), at = list(cyl = c(4,6), wt = c(2.75,3,3.25))), 1)

str(build_datalist(head(mtcars), at = data.frame(cyl = c(4,4), wt = c(2.75,3))))

Extract data from a model object

Description

Attempt to reconstruct the data used to create a model object

Usage

find_data(model, ...)

## Default S3 method:
find_data(model, env = parent.frame(), ...)

## S3 method for class 'data.frame'
find_data(model, ...)

## S3 method for class 'crch'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'glimML'
find_data(model, ...)

## S3 method for class 'glimQL'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'glm'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'hxlr'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'lm'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'mca'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'merMod'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'svyglm'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'train'
find_data(model, ...)

## S3 method for class 'vgam'
find_data(model, env = parent.frame(), ...)

## S3 method for class 'vglm'
find_data(model, env = parent.frame(), ...)

Arguments

model

The model object.

...

Additional arguments passed to methods.

env

An environment in which to look for the data argument to the modelling call.

Details

This is a convenience function and, as such, carries no guarantees. To behave well, it typically requires that a model object be specified using a formula interface and an explicit data argument. Models that can be specified using variables from the .GlobalEnv or with a non-formula interface (e.g., a matrix of data) will tend to generate errors. find_data is an S3 generic so it is possible to expand it with new methods.

Value

A data frame containing the original data used in a modelling call, modified according to the original model's 'subset' and 'na.action' arguments, if appropriate.

Examples

require("datasets")
x <- lm(mpg ~ cyl * hp + wt, data = head(mtcars))
find_data(x)

Artificial data for margins, copied from Stata

Description

The dataset is identical to the one provided by Stata and available from webuse::webuse("margex") with categorical variables explicitly encoded as factors.

Usage

data("margex")

Format

A data frame with 3000 observations on the following 11 variables.

‘⁠y⁠’: A numeric vector
‘⁠outcome⁠’: A binary numeric vector with values (0,1)
‘⁠sex⁠’: A factor with two levels
‘⁠group⁠’: A factor with three levels
‘⁠age⁠’: A numeric vector
‘⁠distance⁠’: A numeric vector
‘⁠ycn⁠’: A numeric vector
‘⁠yc⁠’: A numeric vector
‘⁠treatment⁠’: A factor with two levels
‘⁠agegroup⁠’: A factor with three levels
‘⁠arm⁠’: A factor with three levels

Source

https://www.stata-press.com/data/r14/margex.dta

Examples



# Examples from Stata's help files
# Also available from: webuse::webuse("margex")
data("margex")

# A simple case after regress
# . regress y i.sex i.group
# . margins sex
m1 <- lm(y ~ factor(sex) + factor(group), data = margex)
prediction(m1, at = list(sex = c("male", "female")))

# A simple case after logistic
# . logistic outcome i.sex i.group
# . margins sex
m2 <- glm(outcome ~ sex + group, binomial(), data = margex)
prediction(m2, at = list(sex = c("male", "female")))

# Average response versus response at average
# . margins sex
prediction(m2, at = list(sex = c("male", "female")))
# . margins sex, atmeans
## TODO

# Multiple margins from one margins command
# . margins sex group
prediction(m2, at = list(sex = c("male", "female")))
prediction(m2, at = list(group = c("1", "2", "3")))

# Margins with interaction terms
# . logistic outcome i.sex i.group sex#group
# . margins sex group
m3 <- glm(outcome ~ sex * group, binomial(), data = margex)
prediction(m3, at = list(sex = c("male", "female")))
prediction(m3, at = list(group = c("1", "2", "3")))

# Margins with continuous variables
# . logistic outcome i.sex i.group sex#group age
# . margins sex group
m4 <- glm(outcome ~ sex * group + age, binomial(), data = margex)
prediction(m4, at = list(sex = c("male", "female")))
prediction(m4, at = list(group = c("1", "2", "3")))

# Margins of continuous variables
# . margins, at(age=40)
prediction(m4, at = list(age = 40))
# . margins, at(age=(30 35 40 45 50))
prediction(m4, at = list(age = c(30, 35, 40, 45, 50)))

# Margins of interactions
# . margins sex#group
prediction(m4, at = list(sex = c("male", "female"), group = c("1", "2", "3")))

Class-dependent variable aggregation

Description

Summarize a vector/variable into a single number, either a mean (median) for numeric vectors or the mode for categorical (character, factor, ordered, or logical) vectors. Useful for aggregation.

Usage

mean_or_mode(x)

## Default S3 method:
mean_or_mode(x)

## S3 method for class 'numeric'
mean_or_mode(x)

## S3 method for class 'data.frame'
mean_or_mode(x)

median_or_mode(x)

## Default S3 method:
median_or_mode(x)

## S3 method for class 'numeric'
median_or_mode(x)

## S3 method for class 'data.frame'
median_or_mode(x)

Arguments

x

A vector.

Value

A numeric or factor vector of length 1.

Examples

require("datasets")
# mean for numerics
mean_or_mode(iris)
mean_or_mode(iris[["Sepal.Length"]])
mean_or_mode(iris[["Species"]])

# median for numerics
median_or_mode(iris)

Create a sequence over the range of a vector

Description

Define a sequence of evenly spaced values from the minimum to the maximum of a vector

Usage

seq_range(x, n = 2)

Arguments

x

A numeric vector

n

An integer specifying the length of sequence (i.e., number of points across the range of x)

Value

A vector of length n.

Examples

identical(range(1:5), seq_range(1:5, n = 2))
seq_range(1:5, n = 3)

Extract Predictions from a Model Object

Description

Usage

Arguments

Details

Value

See Also

Examples

Build list of data.frames

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Extract data from a model object

Description

Usage

Arguments

Details

Value

See Also

Examples

Artificial data for margins, copied from Stata

Description

Usage

Format

Source

See Also

Examples

Class-dependent variable aggregation

Description

Usage

Arguments

Value

See Also

Examples

Create a sequence over the range of a vector

Description

Usage

Arguments

Value

See Also

Examples