Package 'prediction'

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.

See Also

find_data, build_datalist, mean_or_mode, seq_range

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

See Also

find_data, mean_or_mode, seq_range

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.

See Also

prediction, build_datalist, mean_or_mode, seq_range

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

See Also

prediction

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.

See Also

prediction, build_datalist, seq_range

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)

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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.

See Also

mean_or_mode, build_datalist

Examples

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

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