prediction: Extract Predictions from a Model Object

In leeper/prediction: Tidy, Type-Safe 'prediction()' Methods

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 = 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 '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 '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 '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 '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 'truncreg'
prediction(model, data, at = NULL, calculate_se = FALSE, ...)

## 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 (including “ffdf”-backed models provided by bigglm.ffdf)

• “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 in http://indiana.edu/~jslsoc/stata/ci_computations/spost_deltaci.pdf.

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)

leeper/prediction documentation built on Jan. 1, 2020, 6:10 p.m.