tidymodels | Easily install and load the tidymodels packages | Data Visualization library

I struggle with multilevel models and prepared a reproducible example to be clear.

Let's say I would like to predict the height of children after 12 months of follow_up, i.e. their height at month == 12, using the previous values obtained for the height, but also their previous values of weight, with such a dataframe.

I've been trying follow the approach set out by Hadley Wickham for running multiple models in a nested data frame as per https://r4ds.had.co.nz/many-models.html

I've managed to write this code below to create the multiple linear models:

I want to use purrr::map_* functions to extract info from multiple models involving linear regression method. I am first creating some random dataset. The dataset has three dependent variables, and one independent variable.

I'm new to tidymodels but apparently the step_pca() arguments such as nom_comp or threshold are not being implemented when being trained. as in example below, I'm still getting 4 component despite setting nom_comp = 2.

I'm trying to preform PCA (principal component analysis) using TidyModels. I have created a recipe but I don't know how can I change the default rotation used in `step_pca() method (such as changing it to say Varimax rotation). any ideas?

this is my recipe:

I'm trying to figure out how step_dummy() from recipes package wrangles the data. Although there's a reference page for this function, I'm still unable to wrap my head around how to do it using "regular" tidyverse tools I know. Here's some code based on recipes and rsample packages. I would like to achieve the same data output but just using dplyr/tidyr tools.

I chose diamonds dataset from ggplot2 for this demonstration.

I want to use xgboost for a classification problem, and two predictors (out of several) are binary columns that also happen to have some missing values. Before fitting a model with xgboost, I want to replace those missing values by imputing the mode in each binary column.

My problem is that I want to do this imputation as part of a tidymodels "recipe". That is, not using typical data wrangling procedures such as dplyr/tidyr/data.table, etc. Doing the imputation within a recipe should guard against "information leakage".

Although the recipes package provides many step_*() functions that are designed for data preprocessing, I could not find a way to do the desired imputation by mode on numeric binary columns. While there is a function called step_impute_mode(), it accepts only nominal variables (i.e., of class factor or character). But I need my binary columns to remain numeric so they could be passed to the xgboost engine.

Consider the following toy example. I took it from this reference page and changed the data a bit to reflect the problem.

create toy data

I know that in tidymodels you can set a custom tunable parameter space by interacting directly with the workflow object as follows:

As I want to produce some visualizations and analysis on forecasted data outside the modeltime framework, I need to extract confidence values, fitted values and maybe also residuals.

The documentation indicates, that I need to use the function modeltime_calibrate() to get the confidence values and residuals. So one question would be, where do I extract the fitted values from?

My main question is whatsoever, how to do calibration on recursive ensembles. For any non-ensemble model I was able to do it, but in case of recursive ensembles I encounter some error messages, if I want to calibrate.

To illustrate the problem, look at the example code below, which ends up failing to calibrate all models: