---
title: "parsnip fits and condvis2"
author: "Catherine B. Hurley"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{parsnip fits and condvis2}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width=5, fig.height=5 ,fig.align="center"
)
fpath <- ""
```

`parsnip` is a R package that offers
a unified interface to many machine learning models. 
By writing an interface between `condvis2` and `parsnip` a vast number
of machine learning fits may be explored with `condvis`.

A list of models supported by parsnip is found on this link:
<https://www.tidymodels.org/find/parsnip/>


## Regression

Fit the regression model with parsnip.

```{r eval=F}
library(parsnip)
library(MASS)
library(condvis2)
Boston1 <- Boston[,9:14]

fitlm <-
  linear_reg() %>%
  set_engine("lm") %>%
  fit(medv ~ ., data = Boston1)

fitrf <- rand_forest(mode="regression") %>%
  set_engine("randomForest") %>%
  fit(medv ~ ., data = Boston1)
  
```

Use condvis to explore the models:
```{r eval=F}
condvis(Boston1, model=list(lm=fitlm,rf=fitrf), response="medv", sectionvars="lstat")
```
Choose tour "Diff fits" to explore differences between the fits


Some tasks, for example linear regression, support  confidence intervals.
Tell `condvis` to plot an interval using `pinterval="confidence` for that fit.
The forest fit does not support confidence intervals so the predictArgs for that fit are
NULL.

```{r eval=F}
condvis(Boston1, model=list(lm=fitlm,rf=fitrf), response="medv", sectionvars="lstat",
        predictArgs=list(list(pinterval="confidence"), NULL))
```



## Classification

Fit some classification models:
```{r eval=F}
clmodel <-
   svm_poly(mode="classification") %>%
  set_engine("kernlab") %>%
  fit(Species ~ ., data = iris )
```

Explore with `condvis`:
```{r eval=F}
condvis(iris, model=clmodel, response="Species", sectionvars=c("Petal.Length", "Petal.Width"), pointColor="Species")
```

Click on "Show probs" to see class probabilities.

## Survival

Fit a survival model and explore with condvis:
```{r eval=F}
library(survival) # for the data
smodel <-
  surv_reg() %>%
  set_engine("survival") %>%
  fit(Surv(time, status) ~ inst+age+sex+ph.ecog, data=lung)

condvis(na.omit(lung), smodel, response="time", sectionvars = c("inst","sex"), conditionvars=c("age","ph.ecog"))

```


## Clustering

Unlike `mlr`, `parsnip` does not yet offer support for clustering fits.