In 2015, the lead developer for seaborn replied to a feature request asking for access to the statistical values used to generate plots by saying, “It is not available, and it will not be made available.” So, unfortunately, this feature does not exist in seaborn, and seems unlikely to exist in the future. Update: in … Read more
Trying to close this out, so am providing the answer that David and larsmans have eloquently described in the comments section: Yes, this is supposed to happen. The actual MSE is simply the positive version of the number you’re getting. The unified scoring API always maximizes the score, so scores which need to be minimized … Read more
Yes, you will have to convert everything to numbers. That requires thinking about what these attributes represent. Usually there are three possibilities: One-Hot encoding for categorical data Arbitrary numbers for ordinal data Use something like group means for categorical data (e. g. mean prices for city districts). You have to be carefull to not infuse … Read more
A summary.lm object stores these values in a matrix called ‘coefficients’. So the value you are after can be accessed with: a2Pval <- summary(mg)$coefficients[2, 4] Or, more generally/readably, coef(summary(mg))[“a2″,”Pr(>|t|)”]. See here for why this method is preferred.
Since 2009, dplyr has been released which actually provides a very nice way to do this kind of grouping, closely resembling what SAS does. library(dplyr) d <- data.frame(state=rep(c(‘NY’, ‘CA’), c(10, 10)), year=rep(1:10, 2), response=c(rnorm(10), rnorm(10))) fitted_models = d %>% group_by(state) %>% do(model = lm(response ~ year, data = .)) # Source: local data frame [2 … Read more
I think you can almost do exactly what you thought would be ideal, using the statsmodels package which was one of pandas‘ optional dependencies before pandas‘ version 0.20.0 (it was used for a few things in pandas.stats.) >>> import pandas as pd >>> import statsmodels.formula.api as sm >>> df = pd.DataFrame({“A”: [10,20,30,40,50], “B”: [20, 30, … Read more
See the relevel() function. Here is an example: set.seed(123) x <- rnorm(100) DF <- data.frame(x = x, y = 4 + (1.5*x) + rnorm(100, sd = 2), b = gl(5, 20)) head(DF) str(DF) m1 <- lm(y ~ x + b, data = DF) summary(m1) Now alter the factor b in DF by use of the … Read more
In general, to provide your own formula you should use arguments x and y that will correspond to values you provided in ggplot() – in this case x will be interpreted as x.plot and y as y.plot. You can find more information about smoothing methods and formula via the help page of function stat_smooth() as … Read more