在tidyverse中按组滚动回归？

Question

There are many questions about rolling regression in R, but here I am specifically looking for something that uses dplyr , broom and (if needed) purrr .

This is what makes this question different. I want to be tidyverse consistent. Is is possible to do a proper running regression with tidy tools such as purrr:map and dplyr ?

Please consider this simple example:

library(dplyr)
library(purrr)
library(broom)
library(zoo)
library(lubridate)

mydata = data_frame('group' = c('a','a', 'a','a','b', 'b', 'b', 'b'),
                     'y' = c(1,2,3,4,2,3,4,5),
                     'x' = c(2,4,6,8,6,9,12,15),
                     'date' = c(ymd('2016-06-01', '2016-06-02', '2016-06-03', '2016-06-04',
                                    '2016-06-03', '2016-06-04', '2016-06-05','2016-06-06')))

  group     y     x date      
  <chr> <dbl> <dbl> <date>    
1 a      1.00  2.00 2016-06-01
2 a      2.00  4.00 2016-06-02
3 a      3.00  6.00 2016-06-03
4 a      4.00  8.00 2016-06-04
5 b      2.00  6.00 2016-06-03
6 b      3.00  9.00 2016-06-04
7 b      4.00 12.0  2016-06-05
8 b      5.00 15.0  2016-06-06

For each group (in this example, a or b ):

1. compute the rolling regression of y on x over the last 2 observations .
2. store the coefficient of that rolling regression in a column of the dataframe.

Of course, as you can see, the rolling regression can only be computed for the last 2 rows in each group.

I have tried to use the following, but without success.

data %>% group_by(group) %>% 
  mutate(rolling_coef = do(tidy(rollapply(. ,
                    width=2, 
                    FUN = function(df) {t = lm(formula=y ~ x, 
                                              data = as.data.frame(df), 
                                              na.rm=TRUE); 
                    return(t$coef) },
                    by.column=FALSE, align="right"))))
Error in mutate_impl(.data, dots) : 
  Evaluation error: subscript out of bounds.
In addition: There were 21 warnings (use warnings() to see them)

Any ideas?

Expected output for the last two rows of the first a group is 0.5 and 0.5 (there is indeed a perfect linear correlation between y and x in this example)

More specifically:

mydata_1 <- mydata %>% filter(group == 'a',
                  row_number() %in% c(1,2))
# A tibble: 2 x 3
  group     y     x
  <chr> <dbl> <dbl>
1 a      1.00  2.00
2 a      2.00  4.00
> tidy(lm(y ~ x, mydata_1))['estimate'][2,]
[1] 0.5

and also

mydata_2 <- mydata %>% filter(group == 'a',
                              row_number() %in% c(2,3)) 
# A tibble: 2 x 3
  group     y     x
  <chr> <dbl> <dbl>
1 a      2.00  4.00
2 a      3.00  6.00
> tidy(lm(y ~ x, mydata_2))['estimate'][2,]
[1] 0.5

EDIT:

interesting follow-up to this question here rolling regression with confidence interval (tidyverse)

Answer 1

Define a function Coef whose argument is formed from cbind(y, x) and which regresses y on x with an intercept, returning the coefficients. Then apply rollapplyr using the current and prior rows over each group. If by last you meant the 2 prior rows to the current row, ie exclude the current row, then replace 2 with list(-seq(2)) as an argument to rollapplyr .

Coef <- . %>% as.data.frame %>% lm %>% coef

mydata %>% 
  group_by(group) %>% 
  do(cbind(reg_col = select(., y, x) %>% rollapplyr(2, Coef, by.column = FALSE, fill = NA),
           date_col = select(., date))) %>%
  ungroup

giving:

# A tibble: 8 x 4
  group `reg_col.(Intercept)` reg_col.x date      
  <chr>                 <dbl>     <dbl> <date>    
1 a      NA                      NA     2016-06-01
2 a       0                       0.500 2016-06-02
3 a       0                       0.500 2016-06-03
4 a       0                       0.500 2016-06-04
5 b      NA                      NA     2016-06-03
6 b       0.00000000000000126     0.333 2016-06-04
7 b     - 0.00000000000000251     0.333 2016-06-05
8 b       0                       0.333 2016-06-06

Variation

A variation of the above would be:

mydata %>% 
       group_by(group) %>% 
       do(select(., date, y, x) %>% 
          read.zoo %>% 
          rollapplyr(2, Coef, by.column = FALSE, fill = NA) %>%
          fortify.zoo(names = "date")
       ) %>% 
       ungroup

Slope Only

If only the slope is needed there are further simplifications possible. We use the fact that the slope equals cov(x, y) / var(x) .

slope <- . %>% { cov(.[, 2], .[, 1]) / var(.[, 2])}
mydata %>%
       group_by(group) %>%
       mutate(slope = rollapplyr(cbind(y, x), 2, slope, by.column = FALSE, fill = NA)) %>%
       ungroup

Answer 2

Does this do what you're after?

data %>% 
  group_by(group) %>% 
  do(data.frame(., rolling_coef = c(NA, rollapply(data = ., width = 2, FUN = function(df_) {
    d = data.frame(df_)
    d[, 2:3] <- apply(d[,2:3], MARGIN = 2, FUN = as.numeric)
    mod = lm(y ~ x, data = d)
    return(coef(mod)[2])
  }, by.column = FALSE, align = "right"))))

Giving:

# A tibble: 8 x 4
# Groups:   group [2]
  group     y     x rolling_coef
  <chr> <dbl> <dbl>        <dbl>
1 a        1.    2.       NA    
2 a        2.    4.        0.500
3 a        3.    6.        0.500
4 a        4.    8.        0.500
5 b        2.    6.       NA    
6 b        3.    9.        0.333
7 b        4.   12.        0.333
8 b        5.   15.        0.333

Edit: Slightly modified code, but data_frame will not accept the . group placeholder as an argument- not sure how to fix that.

data %>% 
  group_by(group) %>% 
  do(data.frame(., rolling_coef = c(NA, rollapplyr(data = ., width = 2, FUN = function(df_) {
    mod = lm(y ~ x, data = .)
    return(coef(mod)[2])
  }, by.column = FALSE))))

Edit 2: Using fill = NA rather than using c(NA, ...) achieves the same result.

data %>% 
  group_by(group) %>% 
  do(data.frame(., rolling_coef = rollapplyr(data = ., width = 2, FUN = function(df_) {
    mod = lm(y ~ x, data = .)
    return(coef(mod)[2])
  }, by.column = FALSE, fill = NA)))

Answer 3

Here is a solution similar to G. Grothendieck's answer but using the rollRegres package. I have to increase the width argument to 3 to avoid an error (by the way, why do you want a regression with so few observations?)

library(rollRegres)
Coef <- . %>% { roll_regres.fit(x = cbind(1, .$x), y = .$y, width = 2L)$coefs }

mydata %>%
  group_by(group) %>%
  do(cbind(reg_col = select(., y, x) %>% Coef,
           date_col = select(., date))) %>%
  ungroup
#R  Error in mydata %>% group_by(group) %>% do(cbind(reg_col = select(., y,  :
#R    Assertion on 'width' failed: All elements must be >= 3.

# change width to avoid error
Coef <- . %>% { roll_regres.fit(x = cbind(1, .$x), y = .$y, width = 3L)$coefs }
mydata %>%
  group_by(group) %>%
  do(cbind(reg_col = select(., y, x) %>% Coef,
           date_col = select(., date))) %>%
    ungroup
#R # A tibble: 8 x 4
#R group  reg_col.1 reg_col.2 date
#R <chr>      <dbl>     <dbl> <date>
#R   1 a      NA           NA     2016-06-01
#R 2 a      NA           NA     2016-06-02
#R 3 a       1.54e-15     0.500 2016-06-03
#R 4 a      -5.13e-15     0.5   2016-06-04
#R 5 b      NA           NA     2016-06-03
#R 6 b      NA           NA     2016-06-04
#R 7 b      -3.08e-15     0.333 2016-06-05
#R 8 b      -4.62e-15     0.333 2016-06-06
#R Warning messages:
#R 1: In evalq((function (..., call. = TRUE, immediate. = FALSE, noBreaks. = FALSE,  :
#R    low sample size relative to number of parameters
#R 2: In evalq((function (..., call. = TRUE, immediate. = FALSE, noBreaks. = FALSE,  :
#R    low sample size relative to number of parameters

Answer 4

This is more of an idea than an answer but maybe instead of using group_by try using map and your list of groups:

FUN <- function(g, df = NULL) {
  tmp <- tidy(rollapply(
    zoo(filter(df, group == g)),
    width = 2,
    FUN = function(z) {
      t <- lm(y ~ x, data = as.data.frame(z)) ; return(t$coef)
    },
    by.column = FALSE,
    align = "right"
    ))
  tmp$series <- c(rep('intercept', nrow(tmp) / 2), rep('slope', nrow(tmp) / 2))
  spread(tmp, series, value) %>% mutate(group = g)
}

map_dfr(list('a', 'b'), FUN, df = data)