You should do the data processing step outside of the model formula/fitting. When creating the factor from b you can specify the ordering of the levels using factor(b, levels = c(3,1,2,4,5)). Do this in a data processing step outside the lm() call though.

You can check the package leaps and in particular the function regsubsets() functions for model selection. As stated in the documentation: As stated in the documentation: Model selection by exhaustive search, forward or backward stepwise, or sequential replacement

May 10, 2015 · I have multiple regression models in R, which I want to summarize in a nice table format that could be included in the publication. I have all the results ready, but couldn't find a way to export t...

Oct 6, 2016 · I am trying to fit a regression model in R, after figuring out the main predictors, I want to check the interaction effects for the predictors. However, there are 14 predictors in total, which means hundreds of combinations possible. If I do this: lm.fit2=lm(medv~chas*dis*tax*black*rm*lstat*age*nox*zn*crim*rad*indus*ptratio,data=Boston)

Oct 8, 2016 · I have a regression model for some time series data investigating drug utilisation. The purpose is to fit a spline to a time series and work out 95% CI etc. The model goes as follows: id <- ts(1:

Nov 4, 2020 · Using the "survival" library and the "lung" data set, I first fit a cox proportional hazards model to the data. Then, I tried to predict and plot the survival curve for a hypothetical new observation (I entered the details for this hypothetical new observation in the "list" command).

Mar 30, 2013 · Model matrix only allows what it calls "dummy" coding for the first factor in a formula. If the intercept is present, it plays that role. To get the desired effect of a redundant index matrix (where you have a 1 in every column for the corresponding factor level and 0 elsewhere), you can lie to model.matrix() and pretend there's an extra level ...

Jun 26, 2015 · Model residuals can also be plotted to communicate results. Like @MrFlick commented, it depends on what you want to communicate. If you want to add confidence/prediction bands around your estimates, you'll have to dig deeper and consider bigger statistical issues ( example1 , example2 ).

Feb 22, 2013 · In the same way the effect of c, aka its contribution is represented by its slope in the same multiple model with c. The increasing in proportion of explained variance could be simply derived subtracting the R 2 from the multiple model with c from model without c. The overall evaluatons doesn't need additional packages. lm gives all requsted ...

Feb 23, 2017 · There are many different ways to compute R^2 and the adjusted R^2, the following are few of them (computed with the data you provided): from sklearn.linear_model import LinearRegression model = LinearRegression() X, y = df[['NumberofEmployees','ValueofContract']], df.AverageNumberofTickets model.fit(X, y) SST = SSR + SSE (ref definitions)