CVpredict | R Documentation |
A predict generic function for condvis
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 )
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. |
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.
a vector of predictions, or a matrix when type is "probmatrix"
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
#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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.