jbssm - 9 months ago 46

R Question

I have this data frame:

`> dat`

x y yerr

1 -1 -1.132711 0.001744498

2 -2 -2.119657 0.003889120

3 -3 -3.147378 0.007521881

4 -4 -4.220129 0.012921450

5 -5 -4.586586 0.021335644

6 -6 -5.389198 0.032892630

7 -7 -6.002848 0.048230946

And I can plot it with the standard error smoothing as:

`p <- ggplot(dat, aes(x=x, y=y)) + geom_point()`

p <- p + geom_errorbar(data=dat, aes(x=x, ymin=y-yerr, ymax=y+yerr), width=0.09)

p + geom_smooth(method = "lm", formula = y ~ x)

But what I need is to use the

Answer

Well, I found a way to answer this.

Since in any scientific experiment where we gather data, if that experiment is correctly executed, all the data values must have an error associated.

**In some cases the variance of the error may be equal in all the points, but in many, like the present case states in the original question, that is not true.** So we must use that different in the variances of the error values for different measurements when fitting a curve to our data.

That way to do it is to attribute the weight to the error values, which according to statistical analysis methods are equal to 1/sqrt(errorValue), so, it becomes:

```
p <- ggplot(dat, aes(x=x, y=y, weight = 1/sqrt(yerr))) +
geom_point() +
geom_errorbar(aes(ymin=y-yerr, ymax=y+yerr), width=0.09) +
geom_smooth(method = "lm", formula = y ~ x)
```

Source (Stackoverflow)