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 Failed to load latest commit information. R Oct 15, 2018 inst/doc Sep 24, 2018 man Sep 23, 2018 tests Sep 19, 2018 vignettes Sep 24, 2018 .Rbuildignore .gitignore Sep 24, 2018 .travis.yml Sep 19, 2018 DESCRIPTION Oct 15, 2018 NAMESPACE Sep 24, 2018 README.md Sep 24, 2018 advanced-r-4.Rproj Oct 15, 2018 codecov.yml Sep 20, 2018 linear.regression.Rproj Sep 18, 2018

# Advanced Programming in R - Assignment 4

This is the 4rd assignment of the course Advanced Programming in R at Linköping University in 2018.

Course information and all assignments can be found at https://www.ida.liu.se/~732A94/info/courseinfo.en.shtml.

## Exercise

The exercise for the 4rd assignment is to implement a linear regression and create some plots.

## Installation

devtools::install_github("AnnalenaE/advanced-r-4")


## Vignette

After installing, run:

browseVignettes("linear.regression")


## Example Usage Methods

### print()

A method call that gives back the formula along with the coeficients.

### plot()

This one method call returns two plots containning the residuals in relation to the fitted values. the first one gives the Residuals vs Fitted, while the seccond one gives the Scale - Location.

### resid()

A method to call on the residuals

$$\hat{e} = y - \hat{y} = y - X\hat{\beta}$$

### pred()

A method call to get the predicted values $\hat{y}$.

### coef()

A method call to get the coefficients as a named vector.

### summary()

This returns a printout presenting the coefficients with their standard error, t-value and p-value as well as the estimate of $\hat{\sigma}$ along with the degrees of freedom in the model.

## Examples

linreg_mod = linreg$new(Petal.Length~Sepal.Width+Sepal.Length, data=iris) linreg_mod$print()

#> Coefficients:

#>  (Intercept) Sepal.Width Sepal.Length
#>       -2.525      -1.339        1.776

#> Call:
#> linreg(formula = Petal.Length ~ Sepal.Width + Sepal.Length, data = iris)

linreg_mod\$summary()

#> Coefficients:

#>              Estimate Std. Error t value Pr(>|t|)
#> (Intercept)     -2.52       0.56   -4.48 1.48e-05 ***
#> Sepal.Width     -1.34       0.12  -10.94 9.43e-21 ***
#> Sepal.Length     1.78       0.06   27.57 5.85e-60 ***

#> Residual standard error: 0.64648051265712 on 147 degrees of freedom
`

## References

Matrix decompositions for regression analysis

Some Notes on Least Squares, QR-factorization, SVD and Fitting