R: A predict generic function for condvis

CVpredict {condvis2}

R Documentation

A predict generic function for condvis

Description

A predict generic function for condvis

Usage

CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  pinterval = NULL,
  pinterval_level = 0.95
)

## Default S3 method:
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  pinterval = NULL,
  pinterval_level = 0.95,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'lm'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  pinterval = NULL,
  pinterval_level = 0.95,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'glm'
CVpredict(
  fit,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  pinterval = NULL,
  pinterval_level = 0.95,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'lda'
CVpredict(
  fit,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'qda'
CVpredict(
  fit,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'nnet'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'randomForest'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'ranger'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'rpart'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'tree'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'C5.0'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'svm'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'gbm'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  n.trees = fit$n.trees,
  ptrans = NULL
)

## S3 method for class 'loess'
CVpredict(fit, newdata = NULL, ...)

## S3 method for class 'ksvm'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'glmnet'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  s = NULL,
  makex = NULL
)

## S3 method for class 'cv.glmnet'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  makex = NULL
)

## S3 method for class 'glmnet.formula'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  s = NULL
)

## S3 method for class 'cv.glmnet.formula'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'keras.engine.training.Model'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  batch_size = 32,
  response = NULL,
  predictors = NULL
)

## S3 method for class 'kde'
CVpredict(fit, newdata = fit$x, ..., scale = TRUE)

## S3 method for class 'densityMclust'
CVpredict(
  fit,
  newdata = NULL,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  scale = TRUE
)

## S3 method for class 'MclustDA'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'MclustDR'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'Mclust'
CVpredict(
  fit,
  newdata,
  ...,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'train'
CVpredict(
  fit,
  newdata,
  ...,
  type = "response",
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'bartMachine'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'wbart'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'lbart'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'pbart'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'bart'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL
)

## S3 method for class 'model_fit'
CVpredict(
  fit,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  pinterval = NULL,
  pinterval_level = 0.95
)

## S3 method for class 'WrappedModel'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  pinterval = NULL,
  pinterval_level = 0.95
)

## S3 method for class 'Learner'
CVpredict(
  fit,
  newdata,
  ...,
  type = NULL,
  ptype = "pred",
  pthreshold = NULL,
  ylevels = NULL,
  ptrans = NULL,
  pinterval = NULL,
  pinterval_level = 0.95
)

Arguments

`fit`	A fitted model
`newdata`	Where to calculate predictions.
`...`	extra arguments to predict
`ptype`	One of "pred","prob" or "probmatrix"
`pthreshold`	Used for calculating classes from probs, in the two class case
`ylevels`	The levels of the response, when it is a factor
`ptrans`	A function to apply to the result
`pinterval`	NULL, "confidence" or "prediction". Only for lm, parsnip, mlr(regression, confidence only)
`pinterval_level`	Defaults to 0.95
`type`	For some predict methods
`n.trees`	Used by CVpredict.gbm, passed to predict
`s`	Used by CVpredict.glmnet and CVpredict.cv.glmnet, passed to predict
`makex`	Used by CVpredict.glmnet and CVpredict.cv.glmnet. A function to construct xmatrix for predict.
`batch_size`	Used by CVpredict.keras.engine.training.Model, passed to predict
`response`	Used by CVpredict.keras.engine.training.Model. Name of response (optional)
`predictors`	Used by CVpredict.keras.engine.training.Model. Name of predictors
`scale`	Used by CVpredict for densities. If TRUE (default) rescales the conditional density to integrate to 1.

Details

This is a wrapper for predict used by condvis. When the model response is numeric, the result is a vector of predictions. When the model response is a factor the result depends on the value of ptype. If ptype="pred", the result is a factor. If also threshold is numeric, it is used to threshold a numeric prediction to construct the factor when the factor has two levels. For ptype="prob", the result is a vector of probabilities for the last factor level. For ptype="probmatrix", the result is a matrix of probabilities for each factor level.

Value

a vector of predictions, or a matrix when type is "probmatrix"

Methods (by class)

CVpredict(default): CVpredict method
CVpredict(lm): CVpredict method
CVpredict(glm): CVpredict method
CVpredict(lda): CVpredict method
CVpredict(qda): CVpredict method
CVpredict(nnet): CVpredict method
CVpredict(randomForest): CVpredict method
CVpredict(ranger): CVpredict method
CVpredict(rpart): CVpredict method
CVpredict(tree): CVpredict method
CVpredict(C5.0): CVpredict method
CVpredict(svm): CVpredict method
CVpredict(gbm): CVpredict method
CVpredict(loess): CVpredict method
CVpredict(ksvm): CVpredict method
CVpredict(glmnet): CVpredict method
CVpredict(cv.glmnet): CVpredict method
CVpredict(glmnet.formula): CVpredict method
CVpredict(cv.glmnet.formula): CVpredict method
CVpredict(keras.engine.training.Model): CVpredict method
CVpredict(kde): CVpredict method
CVpredict(densityMclust): CVpredict method
CVpredict(MclustDA): CVpredict method
CVpredict(MclustDR): CVpredict method
CVpredict(Mclust): CVpredict method
CVpredict(train): CVpredict method for caret
CVpredict(bartMachine): CVpredict method
CVpredict(wbart): CVpredict method
CVpredict(lbart): CVpredict method
CVpredict(pbart): CVpredict method
CVpredict(bart): CVpredict method
CVpredict(model_fit): CVpredict method for parsnip
CVpredict(WrappedModel): CVpredict method for mlr
CVpredict(Learner): CVpredict method for mlr3

Examples

#Fit a model. 
f <- lm(Fertility~ ., data=swiss)
CVpredict(f)

#Fit a model with a factor response
swiss1 <- swiss
swiss1$Fertility <-  cut(swiss$Fertility, c(0,80,100))
levels(swiss1$Fertility)<- c("lo", "hi")
f <- glm(Fertility~ ., data=swiss1, family="binomial")
CVpredict(f) # by default gives a factor
CVpredict(f, ptype="prob") # gives prob of level hi
CVpredict(f, ptype="probmatrix") # gives prob of both levels

[Package condvis2 version 0.1.2 Index]