roll_lm: Rolling Linear Models
In roll: Rolling and Expanding Statistics

Description Usage Arguments Value Examples

A function for computing the rolling and expanding linear models of time-series data.

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roll_lm(x, y, width, weights = rep(1, width), intercept = TRUE,
  min_obs = width, complete_obs = TRUE, na_restore = FALSE,
  online = TRUE)

`x`	vector or matrix. Rows are observations and columns are the independent variables.
`y`	vector or matrix. Rows are observations and columns are the dependent variables.
`width`	integer. Window size.
`weights`	vector. Weights for each observation within a window.
`intercept`	logical. Either `TRUE` to include or `FALSE` to remove the intercept.
`min_obs`	integer. Minimum number of observations required to have a value within a window, otherwise result is `NA`.
`complete_obs`	logical. If `TRUE` then rows containing any missing values are removed, if `FALSE` then pairwise is used.
`na_restore`	logical. Should missing values be restored?
`online`	logical. Process observations using an online algorithm.

A list containing the following components:

`coefficients`	A list of objects with the rolling and expanding coefficients for each `y`. An object is the same class and dimension (with an added column for the intercept) as `x`.
`r.squared`	A list of objects with the rolling and expanding r-squareds for each `y`. An object is the same class as `x`.
`std.error`	A list of objects with the rolling and expanding standard errors for each `y`. An object is the same class and dimension (with an added column for the intercept) as `x`.

n <- 15
x <- rnorm(n)
y <- rnorm(n)
weights <- 0.9 ^ (n:1)

# rolling regressions with complete windows
roll_lm(x, y, width = 5)

# rolling regressions with partial windows
roll_lm(x, y, width = 5, min_obs = 1)

# expanding regressions with partial windows
roll_lm(x, y, width = n, min_obs = 1)

# expanding regressions with partial windows and weights
roll_lm(x, y, width = n, min_obs = 1, weights = weights)

$coefficients
      (Intercept)          x1
 [1,]          NA          NA
 [2,]          NA          NA
 [3,]          NA          NA
 [4,]          NA          NA
 [5,]  0.48181967 -0.03210633
 [6,]  0.58380388 -0.28772883
 [7,]  0.58702575 -0.31186857
 [8,]  0.53768064 -0.21984641
 [9,]  0.18798353  0.28007195
[10,]  0.35163296  0.48258804
[11,]  0.25210781  0.53096163
[12,]  0.38315680  1.23390648
[13,] -0.23445004  0.25370529
[14,] -0.09791133 -0.21606419
[15,] -0.72736585 -0.06127493

$r.squared
        R-squared
 [1,]          NA
 [2,]          NA
 [3,]          NA
 [4,]          NA
 [5,] 0.006488938
 [6,] 0.271411214
 [7,] 0.377999253
 [8,] 0.215331765
 [9,] 0.085920350
[10,] 0.134303546
[11,] 0.164759550
[12,] 0.178815996
[13,] 0.031826221
[14,] 0.015762763
[15,] 0.013247295

$std.error
      (Intercept)        x1
 [1,]          NA        NA
 [2,]          NA        NA
 [3,]          NA        NA
 [4,]          NA        NA
 [5,]   0.2027786 0.2293663
 [6,]   0.1736307 0.2721762
 [7,]   0.1796377 0.2309729
 [8,]   0.1892132 0.2422968
 [9,]   0.4379829 0.5274156
[10,]   0.5528433 0.7073836
[11,]   0.5637318 0.6902127
[12,]   0.9268909 1.5266472
[13,]   0.6906703 0.8078914
[14,]   0.6750679 0.9857252
[15,]   0.3096515 0.3053253

$coefficients
      (Intercept)          x1
 [1,]          NA          NA
 [2,]  0.27305901  0.03898481
 [3,]  0.23268484  0.02074292
 [4,]  0.30735442  0.10851360
 [5,]  0.48181967 -0.03210633
 [6,]  0.58380388 -0.28772883
 [7,]  0.58702575 -0.31186857
 [8,]  0.53768064 -0.21984641
 [9,]  0.18798353  0.28007195
[10,]  0.35163296  0.48258804
[11,]  0.25210781  0.53096163
[12,]  0.38315680  1.23390648
[13,] -0.23445004  0.25370529
[14,] -0.09791133 -0.21606419
[15,] -0.72736585 -0.06127493

$r.squared
        R-squared
 [1,]          NA
 [2,] 1.000000000
 [3,] 0.119143159
 [4,] 0.356475230
 [5,] 0.006488938
 [6,] 0.271411214
 [7,] 0.377999253
 [8,] 0.215331765
 [9,] 0.085920350
[10,] 0.134303546
[11,] 0.164759550
[12,] 0.178815996
[13,] 0.031826221
[14,] 0.015762763
[15,] 0.013247295

$std.error
      (Intercept)         x1
 [1,]          NA         NA
 [2,]          NA         NA
 [3,]  0.05144435 0.05640117
 [4,]  0.09648270 0.10309489
 [5,]  0.20277859 0.22936629
 [6,]  0.17363072 0.27217617
 [7,]  0.17963772 0.23097287
 [8,]  0.18921318 0.24229683
 [9,]  0.43798287 0.52741562
[10,]  0.55284335 0.70738363
[11,]  0.56373180 0.69021274
[12,]  0.92689091 1.52664717
[13,]  0.69067033 0.80789145
[14,]  0.67506788 0.98572524
[15,]  0.30965147 0.30532527

$coefficients
      (Intercept)          x1
 [1,]          NA          NA
 [2,]  0.27305901  0.03898481
 [3,]  0.23268484  0.02074292
 [4,]  0.30735442  0.10851360
 [5,]  0.48181967 -0.03210633
 [6,]  0.46937863 -0.02850295
 [7,]  0.43416160 -0.09055689
 [8,]  0.39877941 -0.07215886
 [9,]  0.22554281  0.20647138
[10,]  0.40109307  0.19719202
[11,]  0.35252164  0.23524932
[12,]  0.20274443  0.21851776
[13,]  0.14066242  0.09656704
[14,]  0.08476906  0.07244389
[15,]  0.01930207 -0.10275539

$r.squared
        R-squared
 [1,]          NA
 [2,] 1.000000000
 [3,] 0.119143159
 [4,] 0.356475230
 [5,] 0.006488938
 [6,] 0.005137248
 [7,] 0.060483920
 [8,] 0.037108825
 [9,] 0.094678500
[10,] 0.045742995
[11,] 0.062841017
[12,] 0.037429765
[13,] 0.009158892
[14,] 0.004918882
[15,] 0.011027957

$std.error
      (Intercept)         x1
 [1,]          NA         NA
 [2,]          NA         NA
 [3,]  0.05144435 0.05640117
 [4,]  0.09648270 0.10309489
 [5,]  0.20277859 0.22936629
 [6,]  0.16121519 0.19832442
 [7,]  0.14094790 0.15961315
 [8,]  0.12493089 0.15005969
 [9,]  0.21208155 0.24131625
[10,]  0.26569342 0.31843055
[11,]  0.24669515 0.30282530
[12,]  0.27334722 0.35042446
[13,]  0.25475290 0.30283993
[14,]  0.24238344 0.29744535
[15,]  0.24244217 0.26988416

$coefficients
       (Intercept)          x1
 [1,]           NA          NA
 [2,]  0.273059011  0.03898481
 [3,]  0.229640192  0.02048184
 [4,]  0.308857222  0.12159357
 [5,]  0.529244023 -0.07249779
 [6,]  0.501263001 -0.06274834
 [7,]  0.461275933 -0.14238899
 [8,]  0.401941498 -0.10635594
 [9,]  0.154299205  0.30648235
[10,]  0.437029559  0.30384378
[11,]  0.365785877  0.36190493
[12,]  0.108951599  0.30705733
[13,]  0.007216511  0.09344320
[14,] -0.075366009  0.06269046
[15,] -0.150697011 -0.18713920

$r.squared
        R-squared
 [1,]          NA
 [2,] 1.000000000
 [3,] 0.104592070
 [4,] 0.372765443
 [5,] 0.028132131
 [6,] 0.021049972
 [7,] 0.132402681
 [8,] 0.071622634
 [9,] 0.161290269
[10,] 0.072772400
[11,] 0.099919320
[12,] 0.046410956
[13,] 0.006861132
[14,] 0.002953586
[15,] 0.033867942

$std.error
      (Intercept)         x1
 [1,]          NA         NA
 [2,]          NA         NA
 [3,]  0.05248646 0.05992804
 [4,]  0.10203536 0.11153028
 [5,]  0.21040018 0.24601773
 [6,]  0.16398263 0.21395713
 [7,]  0.14221308 0.16300544
 [8,]  0.12532103 0.15632314
 [9,]  0.23139830 0.26415462
[10,]  0.30942927 0.38345568
[11,]  0.28175006 0.36206724
[12,]  0.31780851 0.44013898
[13,]  0.28479542 0.33896781
[14,]  0.26328192 0.33250160
[15,]  0.26323060 0.27721514