linear-regression

问题 Let us say I want to run the linear regression model on the mtcars dataset several times on different samples. The idea is, for each iteration in a for loop, to store the results of the predict() method every time the linear regression is run for a different sample. The small example follows for one run: ## Perform model once on a Sample and use model on full dataset: Sample_Size <- 10 Sample <- mtcars[sample(nrow(mtcars), Sample_Size), ] Model <- lm(formula = mpg ~ wt, data = Sample)

问题 I am trying to run this code from this post: looping with iterations over two lists of variables for a multiple regression in R with modified variable and data frame names, because it seems to do exactly what I want and uses a very similar dataset. However, it keeps giving me an error and I don't know why, so I would really appreciate if someone could help me to understand the error or the corresponding line of code so I could try to figure out what's wrong. for(i in 1:n) { vars = names

问题 This is a sample. df <- tibble( subject = rep(letters[1:7], c(5, 6, 7, 5, 2, 5, 2)), day = c(3:7, 2:7, 1:7, 3:7, 6:7, 3:7, 6:7), x1 = runif(32), x2 = rpois(32, 3), x3 = rnorm(32), x4 = rnorm(32, 1, 5)) df %>% group_by(subject) %>% summarise( coef_x1 = lm(x1 ~ day)$coefficients[2], coef_x2 = lm(x2 ~ day)$coefficients[2], coef_x3 = lm(x3 ~ day)$coefficients[2], coef_x4 = lm(x4 ~ day)$coefficients[2]) This data is small, so performance is not problem. But my data is so large, approximately 1,000

问题 In stats model it's easy to add interaction term. However not all of the interactions are significant. My question is how to drop those that are insignificant? For example airport at Kootenay. # -*- coding: utf-8 -*- import pandas as pd import statsmodels.formula.api as sm if __name__ == "__main__": # Read data census_subdivision_without_lower_mainland_and_van_island = pd.read_csv('../data/augmented/census_subdivision_without_lower_mainland_and_van_island.csv') # Fit all data fit = sm.ols

问题 I want to estimate two breakpoints of a function with the next data: df = data.frame (x = 1:180, y = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 2, 2, 4, 2, 2, 3, 2, 1, 2,0, 1, 0, 1, 4, 0, 1, 2, 3, 1, 1, 1, 0, 2, 0, 3, 2, 1, 1, 1, 1, 5, 4, 2, 1, 0, 2, 1, 1, 2, 0, 0, 2, 2, 1, 1, 1, 0, 0, 0, 0, 2, 3, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

问题 I want to write a loop in R to run multiple regressions with one dependent variables and two lists of independent variables (all continuous variables). The model is additive and the loop should run by iterating through the two lists of variables so that it takes the first column from the first list + the first column from the second list, then the same for the second column in the two lists etc. The problem is I can't get it to iterate through the lists properly, instead my loop runs more

问题 I'm looking to check trends for a number of entities (SysNr) I have data spanning 3 years (2014,2015,2016) I'm looking at a large quantity of variables, but will limit this question to one ('res_f_r') My DataFrame looks something like this d = [ {'RegnskabsAar': 2014, 'SysNr': 1, 'res_f_r': 350000}, {'RegnskabsAar': 2015, 'SysNr': 1, 'res_f_r': 400000}, {'RegnskabsAar': 2016, 'SysNr': 1, 'res_f_r': 450000}, {'RegnskabsAar': 2014, 'SysNr': 2, 'res_f_r': 350000}, {'RegnskabsAar': 2015, 'SysNr':

问题 I have a bunch of formulas, as strings, that I'd like to use, one at a time in a glm, preferably using tidyverse functions. Here's where I am at now. library(tidyverse) library(broom) mtcars %>% dplyr::select(mpg:qsec) %>% colnames -> targcols paste('vs ~ ', targcols) -> formulas formulas #> 'vs ~ mpg' 'vs ~ cyl' 'vs ~ disp' 'vs ~ hp' 'vs ~ drat' 'vs ~ wt' 'vs ~ qsec' I can run a general linear model with any one of these formulas as glm(as.formula(formulas[1]), family = 'binomial', data =

问题 I would like to build 2 linear regression models that are based on 2 subsets of the dataset and then to have one column that contains the prediction values per each subset. Here is my data frame example : dat <- read.table(text = " cats birds wolfs snakes 0 3 8 7 1 3 8 7 1 1 2 3 0 1 2 3 0 1 2 3 1 6 1 1 0 6 1 1 1 6 1 1 ",header = TRUE) First I have built two models: # one is for wolfs ~ snakes where cats=0 f0<-lm(wolfs~snakes,data=dat,subset=dat$cats==0) #the second model is for wolfs ~ snakes

问题 I am trying to find a trend in several datasets. The trends involve finding the best fit line, but if i imagine the procedure would not be too different for any other model (just possibly more time consuming). There are 3 conceivable scenarios: All good data where all the data fits a single trend with a low variability All bad data where all or most of the data exhibits tremendous variability and the entire dataset must be discarded. Partial good data where some of the data may be good while