# Describe the smooth term (for GAMs) or non-linear predictors

Source:`R/describe_nonlinear.R`

`describe_nonlinear.Rd`

This function summarises the smooth term trend in terms of linear segments. Using the approximative derivative, it separates a non-linear vector into quasi-linear segments (in which the trend is either positive or negative). Each of this segment its characterized by its beginning, end, size (in proportion, relative to the total size) trend (the linear regression coefficient) and linearity (the R2 of the linear regression).

## Usage

```
describe_nonlinear(data, ...)
# S3 method for data.frame
describe_nonlinear(data, x = NULL, y = NULL, ...)
estimate_smooth(data, ...)
```

## Arguments

- data
The data containing the link, as for instance obtained by

`estimate_relation()`

.- ...
Other arguments to be passed to or from.

- x, y
The name of the responses variable (

`y`

) predicting variable (`x`

).

## Examples

```
library(modelbased)
# Create data
data <- data.frame(x = rnorm(200))
data$y <- data$x^2 + rnorm(200, 0, 0.5)
model <- lm(y ~ poly(x, 2), data = data)
link_data <- estimate_relation(model, length = 100)
#> Warning: Could not recover model data from environment. Please make sure your
#> data is available in your workspace.
#> Trying to retrieve data from the model frame now.
describe_nonlinear(link_data, x = "x")
#> Start | End | Length | Change | Slope | R2
#> ----------------------------------------------
#> -2.50 | -0.05 | 0.43 | -6.19 | -2.52 | 0.18
#> -0.05 | 3.15 | 0.56 | 10.13 | 3.17 | 0.18
```