r - Rolling regression with dplyr and lsfit -

i'm trying run rolling regression dplyr. i'm using rollapplyr package zoo , lsfit i'm interested in beta of regression. here's i've tried:

library(dplyr); library(zoo)  df1 = expand.grid(site = seq(10),                     year = 2000:2004,                     day = 1:50)  df1 %>% group_by(year) %>% mutate(beta1 = rollapplyr(data = site,                             width = 5,                             fun = lsfit,                             x=day)) 

i'm getting error: error: not arguments have same length

i think rollapplyr accepts non-zoo objects may wrong. piping (%>%) not play rollapplyr requires data object in function.

any idea?

edit question different from: rolling regression dplyr want use pipes in order use group_by

the function not cycle through multiple vectors. sliced site vector being compared full vector day. can write our own rolling apply function map go through groups of our vector:

rollapplydf <- function(xx, width) {   l <- length(xx)   sq <- map(':', 1:(l-width+1), width:l)   lst <- lapply(sq, function(i) lm(xx[i] ~ seq(length(xx[i])))$coeff[2] )   do.call('rbind', c(rep(na, width-1l), lst)) } 

so can add pipe:

library(dplyr) df1 %>%    group_by(year) %>%    mutate(beta1 = rollapplydf(xx = site, width = 5) )  # source: local data frame [2,500 x 4] # groups: year [5] #  #     site  year   day beta1 #    (int) (int) (int) (dbl) # 1      1  2000     1    na # 2      2  2000     1    na # 3      3  2000     1    na # 4      4  2000     1    na # 5      5  2000     1     1 # 6      6  2000     1     1 # 7      7  2000     1     1 # 8      8  2000     1     1 # 9      9  2000     1     1 # 10    10  2000     1     1 # ..   ...   ...   ...   ...