How to perform regression analysis by groups and get the estimated coefficients for each group separ-CodePudding

i have such data (the data is given as an example, so both groups have the same values)

    dat=structure(list(sku = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), period = c("30.09.2021", 
    "14.03.2019", "01.04.2022", "18.02.2022", "07.07.2021", "09.10.2020", 
    "17.01.2019", "10.11.2020", "14.07.2021", "10.09.2019", "31.01.2019", 
    "01.07.2021", "30.09.2021", "14.03.2019", "01.04.2022", "18.02.2022", 
    "07.07.2021", "09.10.2020", "17.01.2019", "10.11.2020", "14.07.2021", 
    "10.09.2019", "31.01.2019", "01.07.2021"), hist.prices = c(3728.16, 
    34899.84, 6126, 1789.44, 18098.4, 15633.6, 26174.88, 2401.56, 
    12668.88, 239500.8, 26174.88, 5429.52, 3728.16, 34899.84, 6126, 
    1789.44, 18098.4, 15633.6, 26174.88, 2401.56, 12668.88, 239500.8, 
    26174.88, 5429.52), hist.revenue = c(178951.68, 20102307.84, 
    367560, 42946.56, 4343616, 3752064, 11307548.16, 86456.16, 2128371.84, 
    965667225.6, 11307548.16, 390925.44, 178951.68, 20102307.84, 
    367560, 42946.56, 4343616, 3752064, 11307548.16, 86456.16, 2128371.84, 
    965667225.6, 11307548.16, 390925.44), hist.demand = c(254L, 276L, 
    272L, 250L, 299L, 297L, 291L, 260L, 270L, 275L, 295L, 279L, 254L, 
    276L, 272L, 250L, 299L, 297L, 291L, 260L, 270L, 275L, 295L, 279L
    ), hist.cost = c(12572.6698, 10498.9848, 14949.392, 13160.5, 
    14557.9512, 12443.3199, 10692.3294, 10893.116, 13145.976, 10222.6025, 
    10982.9975, 13584.1752, 12572.6698, 10498.9848, 14949.392, 13160.5, 
    14557.9512, 12443.3199, 10692.3294, 10893.116, 13145.976, 10222.6025, 
    10982.9975, 13584.1752), unity.cost = c(49.4987, 38.0398, 54.961, 
    52.642, 48.6888, 41.8967, 36.7434, 41.8966, 48.6888, 37.1731, 
    37.2305, 48.6888, 49.4987, 38.0398, 54.961, 52.642, 48.6888, 
    41.8967, 36.7434, 41.8966, 48.6888, 37.1731, 37.2305, 48.6888
    ), hist.profit = c(1336L, 1592L, 1128L, 1882L, 1387L, 1818L, 
    1357L, 1087L, 1253L, 1009L, 1092L, 1804L, 1336L, 1592L, 1128L, 
    1882L, 1387L, 1818L, 1357L, 1087L, 1253L, 1009L, 1092L, 1804L
    )), class = "data.frame", row.names = c(NA, -24L))

I need to do a regression analysis and calculate the coefficients for each sku(group variable) separately. The demand function is the same for all sku. Then i perform regression:

    # example of linear demand curve (first equation) 
    demand = function(p, alpha = -40, beta = 500, sd = 10) {
      error = rnorm(length(p), sd = sd)
      q = p*alpha   beta   error
      return(q)
    }

in this example, this is only for one sku, but it is necessary for all that are available.

    library(stargazer)
    model.fit = lm(hist.demand ~ hist.prices)
    stargazer(model.fit, type = 'html', header = FALSE) # output
    # estimated parameters
    beta = model.fit$coefficients[1]
    alpha = model.fit$coefficients[2]  
    p.revenue = -beta/(2*alpha) # estimated price for revenue
    p.profit = (alpha*unity.cost - beta)/(2*alpha) # estimated price for profit
    
    true.revenue = function(p) p*(-40*p   500) # Revenue with true parameters (chunck demand)
    true.profit = function(p) (p - unity.cost)*(-40*p   500) # price with true parameters
    # estimated curves
    estimated.revenue = function(p) p*(model.fit$coefficients[2]*p   model.fit$coefficients[1])
    estimated.profit = function(p) (p - unity.cost)*(model.fit$coefficients[2]*p   model.fit$coefficients[1])
    opt.revenue = true.revenue(p.revenue) # Revenue with estimated optimum price
    opt.profit = true.profit(p.profit) # Profit with estimated optimum price

how to execute this code for all sku separately, so that the desired output is something like this

    sku opt.profit  opt.revenue
    1   722.0413    1562.041
    2   722.0413    1562.041

thanks for any of your valuable help

CodePudding user response：

We could get the estimates from lm by group in this way:

library(tidyverse)
library(broom)
dat %>%
  group_split(sku) %>% 
  map_dfr(.f = function(df){
    lm(hist.demand ~ hist.prices, data = df) %>% 
      tidy() %>% 
      add_column(group = unique(df$sku), .before=1)
  })

  group term           estimate std.error statistic  p.value
  <int> <chr>             <dbl>     <dbl>     <dbl>    <dbl>
1     1 (Intercept) 276.        5.64         48.9   3.09e-13
2     1 hist.prices   0.0000198 0.0000793     0.249 8.08e- 1
3     2 (Intercept) 276.        5.64         48.9   3.09e-13
4     2 hist.prices   0.0000198 0.0000793     0.249 8.08e- 1

CodePudding user response：

If we want to do a group by approach, an option is to nest and then either loop over the nested data with map or use nest_ functions from nplyr

library(dplyr)
library(nplyr)
library(tidyr)
dat %>% 
  nest(data = -sku) %>% 
  nest_summarise(data, 
   model.fit = list(lm(hist.demand ~ hist.prices)), 
   beta = model.fit[[1]]$coefficients[1], 
   alpha = model.fit[[1]]$coefficients[2],
   p.revenue = -beta/(2*alpha),
   p.profit = (alpha*unity.cost - beta)/(2*alpha),
   opt.revenue = true.revenue(p.revenue), 
   opt.profit = true.profit(p.profit)) %>% 
  nest_select(data, opt.revenue, opt.profit) %>%
  unnest(data)