Using the cars data in R, I would like to create a polynomial regression model with varying degree values. Through research online I have found two methods of creating these models: library(tidyverse) data(cars) # method 1 polyreg_deg2 <- lm(dist ~ speed+I(speed^2), data=cars) # method 2 polyreg_deg2_again <- lm(dist ~ poly(speed, degree = 2), data =… Read More Polynomial regression models return different values: `lm(dist ~ speed+I(speed^2), data=cars)` and `lm(dist ~ poly(speed, degree = 2), data = cars)`

I have three raster layers, two coarse resolution and one fine resolution. I perform simple linear regression using the coarse resolution rasters and then I extract the coefficients of the regression (slope and intercept) and I use them with my fine resolution raster. So far, I am doing this manually by plotting the coefficients in… Read More R: apply regression's model parameters at another spatial scale

Firstly, let’s say I have a data frame df with variables y, x1, x2, x1 is a continuous variable and x2 is a factor. Let’s say I have a model: model <- glm(y ~ x1 + x2, data = df, family = binomial) This will result in an object where I can extract the coefficients… Read More How to add coefficients to existing data base such that their effect on the final intercept is given?

I want to create a matrix that displays the r.squared coefficient of determination of some predictions made over the years and the actual values. My goal is to display a matrix that looks something like this. The only way I found is to make multiple lists, calculate each row/ column individually using map2_dbl(l.predicted_line1, l.actual, ~… Read More r.squared matrix of predictions vs actual values in R