Usemethod("Predict"):No Applicable Method for 'Predict' Applied to an Object of Class "Train"

UseMethod(predict) : no applicable method for 'predict' applied to an object of class train

I think I found why this happened...The predict is a generic function from: stats package. I use the namespace ::-notation for invoking the functions from the caret package (that is the recommendation for creating a user packages) and the equivalent predict function from caret package is: predict.train, that is an internal function, that cannot be invoked by an external application. The only way to invoke this function, is using the generic predict function from stats package, then based on the class of the first input argument: predicted <- predict(fit, testData[-$Readmit]) it identifies the particular predict function will be invoked.

For this particular case the class of this function is train, so it would call actually the function: train.predict from caret package. This function also handles the particular function requested for prediction based on the algorithm (method) used, for example: predict.gbm or predict.glm, etc. It is explained, in detail, in the caret documentation section: "5.7 Extracting Predictions and Class Probabilities".

Therefore the ::-notation works well for other functions in the package, such as: caret.train for example, but not for this particular one: predict. In such cases it is necessary to explicitly load the library, so it internally can invoke predict.train function.

In short, the solution is just adding the following line before invoking the predict function:

library(caret)

Then error disappears.

LinearRegression Predict: No applicable method for 'predict' applied to an object of class data.Frame

That's because you used:

model_fit <- lm(diff ~ ., data = trainingData, method = "model.frame")
class(model_fit)
[1] "data.frame"

The above gives you the model matrix used to fit the data.

You can do instead:

model_fit <- lm(diff ~ ., data = trainingData,model=TRUE)
newdata <- data.frame(
  f.mean_slope = c(1,2,3)
)

distPred <- predict(model_fit, newdata) 

And the model matrix can be found in model_fit$model

No applicable method for 'predict' applied to an object of class c('ksvm', 'vm') for iml::Predictor in R

You don't need to select the model$finalModel (do you have a typo in that line? You have $finalMode - no l). You run a line such as:

pred <- predict(model, newdata, type = "prob")

and Caret will automatically employ the model with best score. The output will give you complementary probabilities for diabetes (column 1) or not (column 2) if you select type = "prob". If you want a specific model from the caret 'model' object, then I believe you can pick it out (from your previous folds question) - but I've never done it and am not sure how.

For your partial dependency plot, well, I use the pdp package, so something like this should work:

library(pdp)
varname = 'X1' # Change this to whatever your first variable is called, or subsequently variables you are interested in.
partial(model, pred.var = varname, 
        train = X, chull=T, prob = T, progress = "text")

where X is the data you trained your model on (X in your case I think?)

R function error, no applicable method for 'predict' applied to an object of class NULL

The issue is probably at for (i in 1:N-1)

R interprets 1:N-1 as sequence 1 to N, then subtract 1 from the whole vector.
Try 1:5-1 to see what I mean.

Try for (i in 1:(N-1))

Also in this line

 ee[[i]] = ks.test(predict(dd[[i]]), predict(dd[[i+1]])) ## this line may also have problem.

dd[[i+1]] will not be defined yet, as your loop has only filled dd to i.

Not sure this is what you are trying to do but maybe:

  if(i>1L)ee[[i]] = ks.test(predict(dd[[i-1]]), predict(dd[[i]]))

That way after you have filled the first entry of dd you start testing the current dd vs. the previous.

Error in UseMethod(predict) : no applicable method for 'predict' applied to an object of class c('double', 'numeric')

I think I found it. This seems to work, but not sure it his what you expect. In plot.bias I changed your use of calc.bias, (i.e. calc.bias(f, polyfit, x) instead of calc.bias(f, polydeg, x)). The whole code I used:

library(PolynomF)

f <- function(x) sin(x-5)/(x-5)
# From which we can sample datasets:
N <- 20
x <- runif(N,0,15)              
t <- f(x) + rnorm(N, sd=0.1)  

calc.bias <- function (f, polyfit, point) {
  predictions <- numeric(0)
  print(class(point))
  for (i in 1:100)
  {
    x <- runif(N, 0, 15)                 
    t <- f(x) + rnorm(N, sd=0.2)
    d <- data.frame(point)
    add <- predict(polyfit, newdata = data.frame(point))
    predictions <- c(predictions, add)
  }
  return((f(point)-mean(predictions))^2)
}

plot.bias <- function (f, polydeg) {
  plot(data.frame(x, t))
  curve(f, type="l", col="green", add=TRUE)
  polyfit <- lm(t ~ poly(x, polydeg, raw=TRUE))
  p <- polynom(coef(polyfit))
  curve(p, col="red", add=TRUE)
  points(x, calc.bias(f, polyfit, x), col="blue")
  abline(h=0, col='blue')
}

plot.bias(f, 1)

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