How to do a weighted linear regression in R?
The lm () function (which represents the usual method of applying a linear regression), has an option to specify weights. As shown in the answer on the link, you can use a formula in the weights argument. In your case, the formula will likely take the form of 1/data$concentration. I think R help page of lm answers your question pretty well.
How is the LM ( ) function used in R?
S ummary: R linear regression uses the lm () function to create a regression model given some formula, in the form of Y~X+X2. To look at the model, you use the summary () function. To analyze the residuals, you pull out the $resid variable from your new model.
Do you need a weight argument in R?
The only requirement for weights is that the vector supplied must be the same length as the data. You can even supply only the name of the variable in the data set, R will take care of the rest, NA management, etc. You can also use formulas in the weight argument. Here is the example:
Do you need LM function for linear regression?
Residuals are the differences between the prediction and the actual results and you need to analyze these differences to find ways to improve your regression model. To do linear (simple and multiple) regression in R you need the built-in lm function. Here’s the data we will use, one year of marketing spend and company sales by month.
How are weights assigned to the LM ( function )?
What I am particularly curious about is how Rhandles weights in the lm()function when weights are assigned to be integers. From using ?lm:
How to test if your linear model has a good fit?
In the R summary of the lm function, you can see descriptive statistics about the residuals of the model, following the same example, the red square shows how the residuals are approximately zero. How to test if your linear model has a good fit? One measure very used to test how good is your model is the coefficient of determination or R².