Arul - 1 year ago 69

R Question

I have set of Temperature and Discomfort index value for each temperature data. When I plot a graph between temperature(x axis) and Calculated Discomfort index value( y axis) I get a reversed U-shape curve. I want to do non linear regression out of it and convert it into PMML model. My aim is to get the predicted discomfort value if I give certain temperature.

Please find the below dataset :

`Temp <- c(0,5,10,6 ,9,13,15,16,20,21,24,26,29,30,32,34,36,38,40,43,44,45, 50,60)`

Disc<-c(0.00,0.10,0.25,0.15,0.24,0.26,0.30,0.31,0.40,0.41,0.49,0.50,0.56, 0.80,0.90,1.00,1.00,1.00,0.80,0.50,0.40,0.20,0.15,0.00)

How to do non linear regression (possibly with

`nls`

Answer Source

I did take a look at this, then I think it is not as simple as using `nls`

as most of us first thought.

`nls`

fits a parametric model, but from your data (the scatter plot), it is hard to propose a reasonable model assumption. I would suggest using non-parametric smoothing for this.

There are many scatter plot smoothing methods, like kernel smoothing `ksmooth`

, smoothing spline `smooth.spline`

and LOESS `loess`

. I prefer to using `smooth.spline`

, and here is what we can do with it:

```
fit <- smooth.spline(Temp, Disc)
```

Please read `?smooth.spline`

for what it takes and what it returns. We can check the fitted spline curve by

```
plot(Temp, Disc)
lines(fit, col = 2)
```

Should you want to make prediction elsewhere, use `predict`

function (`predict.smooth.spline`

). For example, if we want to predict `Temp = 20`

and `Temp = 44`

, we can use

```
predict(fit, c(20,44))$y
# [1] 0.3940963 0.3752191
```

Prediction outside `range(Temp)`

is not recommended, as it suffers from potential bad extrapolation effect.

Before I resort to non-parametric method, I also tried non-linear regression with regression splines and orthogonal polynomial basis, but they don't provide satisfying result. The major reason is that there is no penalty on the smoothness. As an example, I show some try with `poly`

:

```
try1 <- lm(Disc ~ poly(Temp, degree = 3))
try2 <- lm(Disc ~ poly(Temp, degree = 4))
try3 <- lm(Disc ~ poly(Temp, degree = 5))
plot(Temp, Disc, ylim = c(-0.3,1.0))
x<- seq(min(Temp), max(Temp), length = 50)
newdat <- list(Temp = x)
lines(x, predict(try1, newdat), col = 2)
lines(x, predict(try2, newdat), col = 3)
lines(x, predict(try3, newdat), col = 4)
```

We can see that the fitted curve is artificial.