View source: R/predict.plsRglmmodel.R
predict.plsRglmmodel | R Documentation |
This function provides a predict method for the class "plsRglmmodel"
## S3 method for class 'plsRglmmodel'
predict(
object,
newdata,
comps = object$computed_nt,
type = c("link", "response", "terms", "scores", "class", "probs"),
se.fit = FALSE,
weights,
dispersion = NULL,
methodNA = "adaptative",
verbose = TRUE,
...
)
object |
An object of the class |
newdata |
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. |
comps |
A value with a single value of component to use for prediction. |
type |
Type of predicted value. Available choices are the glms ones
(" |
se.fit |
If TRUE, pointwise standard errors are produced for the predictions using the Cox model. |
weights |
Vector of case weights. If |
dispersion |
the dispersion of the GLM fit to be assumed in computing the standard errors. If omitted, that returned by summary applied to the object is used. |
methodNA |
Selects the way of predicting the response or the scores of
the new data. For complete rows, without any missing value, there are two
different ways of computing the prediction. As a consequence, for mixed
datasets, with complete and incomplete rows, there are two ways of computing
prediction : either predicts any row as if there were missing values in it
( |
verbose |
should info messages be displayed ? |
... |
Arguments to be passed on to |
When type is "response
", a matrix of predicted response
values is returned.
When type is "scores
", a score matrix is
returned.
Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Societe Francaise de Statistique, 151(2), pages 1-18. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/47
See Also predict.glm
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
data(pine_sup)
Xpine_sup<-pine_sup[,1:10]
Xpine_supNA<-Xpine_sup
Xpine_supNA[1,1]<-NA
modpls=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",family="gaussian",
verbose=FALSE)
modplsform=plsRglm(x11~.,data=pine,nt=6,modele="pls-glm-family",family="gaussian", verbose=FALSE)
modpls2=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",
dataPredictY=Xpine_sup,family="gaussian", verbose=FALSE)
modpls2NA=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",
dataPredictY=Xpine_supNA,family="gaussian", verbose=FALSE)
#Identical to predict(modpls,type="link") or modpls$Std.ValsPredictY
cbind(modpls$Std.ValsPredictY,modplsform$Std.ValsPredictY,
predict(modpls),predict(modplsform))
#Identical to predict(modpls,type="response") or modpls$ValsPredictY
cbind(modpls$ValsPredictY,modplsform$ValsPredictY,
predict(modpls,type="response"),predict(modplsform,type="response"))
#Identical to modpls$ttPredictY
predict(modpls,type="scores")
predict(modplsform,type="scores")
#Identical to modpls2$ValsPredictY
cbind(predict(modpls,newdata=Xpine_sup,type="response"),
predict(modplsform,newdata=Xpine_sup,type="response"))
#Select the number of components to use to derive the prediction
predict(modpls,newdata=Xpine_sup,type="response",comps=1)
predict(modpls,newdata=Xpine_sup,type="response",comps=3)
predict(modpls,newdata=Xpine_sup,type="response",comps=6)
try(predict(modpls,newdata=Xpine_sup,type="response",comps=8))
#Identical to modpls2$ttValsPredictY
predict(modpls,newdata=Xpine_sup,type="scores")
#Select the number of components in the scores matrix
predict(modpls,newdata=Xpine_sup,type="scores",comps=1)
predict(modpls,newdata=Xpine_sup,type="scores",comps=3)
predict(modpls,newdata=Xpine_sup,type="scores",comps=6)
try(predict(modpls,newdata=Xpine_sup,type="scores",comps=8))
#Identical to modpls2NA$ValsPredictY
predict(modpls,newdata=Xpine_supNA,type="response",methodNA="missingdata")
cbind(predict(modpls,newdata=Xpine_supNA,type="response"),
predict(modplsform,newdata=Xpine_supNA,type="response"))
predict(modpls,newdata=Xpine_supNA,type="response",comps=1)
predict(modpls,newdata=Xpine_supNA,type="response",comps=3)
predict(modpls,newdata=Xpine_supNA,type="response",comps=6)
try(predict(modpls,newdata=Xpine_supNA,type="response",comps=8))
#Identical to modpls2NA$ttPredictY
predict(modpls,newdata=Xpine_supNA,type="scores",methodNA="missingdata")
predict(modplsform,newdata=Xpine_supNA,type="scores",methodNA="missingdata")
predict(modpls,newdata=Xpine_supNA,type="scores")
predict(modplsform,newdata=Xpine_supNA,type="scores")
predict(modpls,newdata=Xpine_supNA,type="scores",comps=1)
predict(modpls,newdata=Xpine_supNA,type="scores",comps=3)
predict(modpls,newdata=Xpine_supNA,type="scores",comps=6)
try(predict(modpls,newdata=Xpine_supNA,type="scores",comps=8))
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