Q:

Rollapply for rolling regression with different window sizes, save coefficients in matrixes

I'm trying to perform a rolling linear regression, whit overlapping windows and for different window sizes.  I want to save the result in two tables, one with window size and slopes and one wiht window size a and intercepts.  
My data set is now saved as a xts object.  I'm trying to make a for-loop to generate the different window-sizes and then do a rolling regression between the two variables, with a overlapping window using rollapply.  Then I would like that the coefficients for every regression get saved as a row in a matrix, that can be built on, so that every window size corresponds to one row.  I have looked at a lot of previous questions, but I can not get it right. 
I have hourly data for 2 variables, this is the data for the x-variable:
> dput(Xq$x)
structure(c(339.76625, 176.7196875, 142.8063125, 118.5785625, 
102.0514375, 86.01156251, 86.99050001, 85.0089375, 380.0010626, 
2114.279375, 2157.76875, 2442.575, 2562.6375, 2537.7125, 2581.888125, 
1947.7575, 834.7918751, 765.1525001, 989.698125, 1433.2, 429.9081251, 
388.333125, 1500.995, 1523.816625, 1529.090126, 439.746875, 1029.03625, 
1470.07825, 1454.844813, 1448.8455, 413.7550001, 334.03125, 566.7362501, 
1609.184375, 1509.266876, 1403.1425, 1142.695625, 795.138125, 
1562.881875, 933.190625, 549.085625, 500.135, 535.83, 444.3493751, 
356.386875, 268.1875001, 141.1878125, 119.658875, 162.0425625, 
153.6944375, 161.0025, 151.37025, 155.9567501, 123.64825, 145.664125, 
141.4480625, 232.2009375, 913.6480001, 2227.08125, 1870.618751, 
2190.606251, 1357.81625, 824.555625, 699.99, 703.695, 620.74625, 
581.2375001, 393.391875, 349.96625, 188.5445625, 154.03125, 142.0825, 
339.7915625, 139.8941875, 154.6279376, 171.6016875, 151.1387501, 
159.7665, 168.62025, 180.7596875, 447.2746875, 1550.11625, 1887.275, 
1446.883125, 699.590625, 983.9925, 1319.62875, 769.505625, 812.713125, 
638.1437501, 585.8312501, 626.7875, 1924.293125, 629.855625, 
349.88375, 1038.54875, 1588.82225, 548.3475, 1053.368125, 964.825625, 
990.324375, 1327.160625, 395.5862501, 421.8154376, 1308.551875, 
1334.045, 1633.408125, 886.2662501, 1618.7975, 1325.59625, 847.0693751, 
667.501875, 594.0531251, 249.920625, 210.8869376, 224.3725001, 
209.6325, 213.64875, 210.3175001, 194.54, 90.1085625, 71.12575001, 
70.69412501, 69.6075, 68.404625, 67.88837501, 67.317125, 67.220375, 
67.696625, 273.2732501, 606.475625, 1277.3425, 913.24875, 612.5462501, 
571.42125, 503.27375, 316.68375, 208.7436251, 127.283, 126.12375, 
102.504625, 78.7938125, 70.0183125, 69.97900001, 77.7586875, 
76.60543751, 94.5235625, 103.0095625, 105.1938125, 103.8090625, 
104.005625, 104.544875, 105.2036875, 104.6611875, 375.7004375, 
793.7725001, 782.544375, 257.065625, 387.1487501, 571.1912501, 
356.6406251, 243.404375, 220.698125, 182.5680626, 161.060625, 
166.5178125, 137.9805, 129.6253125, 99.4291875, 75.88075001, 
65.769875, 62.62937501, 59.697875, 57.68675, 56.67275, 56.69425001, 
57.296, 638.95125, 1210.92875, 990.09, 907.895625, 1122.704375, 
569.139375, 295.6725, 263.21, 236.3885625, 153.935, 148.0899375, 
161.72675, 102.1535, 115.8895, 114.2579375, 95.0023125, 90.72425, 
84.2715, 71.06631251, 63.39812501, 75.2416875, 73.9420625, 72.26237501, 
402.8870625, 1148.9775, 1058.86875, 896.47875, 890.9687501, 833.645625, 
670.310625, 298.576875, 306.538125, 386.403125, 196.8343751, 
174.0815, 156.6158126, 117.9345625, 105.095, 90.24362501, 87.08800001, 
86.1628125, 105.3179375, 107.312625, 114.089, 104.85825, 95.9281875, 
126.2874375, 529.7325626, 1533.605625, 1089.700625, 760.47, 1905.04125, 
1744.460625, 1346.120625, 847.90625, 459.4625, 472.11, 474.5068751, 
481.83875, 937.3618751, 267.1339376, 137.7895625, 124.0425625, 
188.9461875, 108.8915, 111.67825, 84.319125, 127.1240625, 117.519125, 
104.16225, 105.2490625, 158.5540625, 1215.278063, 1275.396875, 
1301.14375, 1108.9225, 1280.390625, 1434.5225, 1038.926875, 599.3218751, 
665.22875, 646.935, 487.604375, 236.8148126, 300.6626876, 830.2973126, 
1286.612313, 403.0063751, 405.9081875, 156.5465625, 101.7661875, 
100.2301875, 144.424625, 126.781125, 143.940625, 365.5354376, 
849.313125, 1729.3175, 1762.6625, 1733.968751, 1275.4375, 1332.930625, 
620.5625, 504.115625, 538.4556251, 433.5725, 337.094375, 225.183125, 
184.7196251, 156.7545, 130.23325, 134.0796875, 145.196125, 153.243, 
141.096375, 414.8042501, 304.7252501, 204.1889375, 129.7909375, 
555.0276251, 1734.235625, 1780.209375, 1462.7825, 967.4743751, 
1248.791875, 1001.655625, 662.92875, 546.801875, 393.72375, 308.85125, 
259.985625, 280.439375, 274.1312501, 310.0375001, 270.3, 201.9288125, 
219.6061875, 208.372625, 156.2023125, 162.5355626, 167.823875, 
151.3928126, 151.42875, 293.7148125, 434.9101875, 943.9243751, 
995.955, 614.055, 1387.864375, 1752.330626, 1238.1725, 1260.93, 
565.383125, 253.179375, 275.66625, 280.4912501, 222.684375, 338.079875, 
201.9365625, 136.7436875, 146.5479375, 142.794625, 150.3969375, 
134.6478125, 88.22068751, 70.6445, 71.73737501, 243.176, 1559.0925, 
1527.626876, 1260.648125, 1251.564375, 714.1075001, 1124.58, 
901.6018751, 551.9075, 303.1025, 355.435625, 372.32375, 235.62125, 
185.39475, 161.7701876, 155.4823751), index = structure(c(1438380000, 
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1439658000, 1439661600, 1439665200, 1439668800, 1439672400), tzone = "UTC", tclass = c("chron", 
"dates", "times")), class = c("xts", "zoo"), .indexCLASS = c("chron", 
"dates", "times"), tclass = c("chron", "dates", "times"), .indexTZ = "UTC", tzone = "UTC", .Dim = c(360L, 
1L), .Dimnames = list(NULL, "x")) 

And my y-variable:
> dput(Xq$y)
structure(c(-0.274050833, -0.236638333, -0.1994325, -0.174091667, 
-0.153273333, -0.136978333, -0.124748333, -0.117348333, -0.147061667, 
-0.346170833, -0.517939167, -0.575585833, -0.595914167, -0.563639167, 
-0.53403, -0.5500525, -0.505650833, -0.471713333, -0.472485833, 
-0.547393333, -0.390174167, -0.321545, -0.29781, -0.26912, -0.345084167, 
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1438891200, 1438894800, 1438898400, 1438902000, 1438905600, 1438909200, 
1438912800, 1438916400, 1438920000, 1438923600, 1438927200, 1438930800, 
1438934400, 1438938000, 1438941600, 1438945200, 1438948800, 1438952400, 
1438956000, 1438959600, 1438963200, 1438966800, 1438970400, 1438974000, 
1438977600, 1438981200, 1438984800, 1438988400, 1438992000, 1438995600, 
1438999200, 1439002800, 1439006400, 1439010000, 1439013600, 1439017200, 
1439020800, 1439024400, 1439028000, 1439031600, 1439035200, 1439038800, 
1439042400, 1439046000, 1439049600, 1439053200, 1439056800, 1439060400, 
1439064000, 1439067600, 1439071200, 1439074800, 1439078400, 1439082000, 
1439085600, 1439089200, 1439092800, 1439096400, 1439100000, 1439103600, 
1439107200, 1439110800, 1439114400, 1439118000, 1439121600, 1439125200, 
1439128800, 1439132400, 1439136000, 1439139600, 1439143200, 1439146800, 
1439150400, 1439154000, 1439157600, 1439161200, 1439164800, 1439168400, 
1439172000, 1439175600, 1439179200, 1439182800, 1439186400, 1439190000, 
1439193600, 1439197200, 1439200800, 1439204400, 1439208000, 1439211600, 
1439215200, 1439218800, 1439222400, 1439226000, 1439229600, 1439233200, 
1439236800, 1439240400, 1439244000, 1439247600, 1439251200, 1439254800, 
1439258400, 1439262000, 1439265600, 1439269200, 1439272800, 1439276400, 
1439280000, 1439283600, 1439287200, 1439290800, 1439294400, 1439298000, 
1439301600, 1439305200, 1439308800, 1439312400, 1439316000, 1439319600, 
1439323200, 1439326800, 1439330400, 1439334000, 1439337600, 1439341200, 
1439344800, 1439348400, 1439352000, 1439355600, 1439359200, 1439362800, 
1439366400, 1439370000, 1439373600, 1439377200, 1439380800, 1439384400, 
1439388000, 1439391600, 1439395200, 1439398800, 1439402400, 1439406000, 
1439409600, 1439413200, 1439416800, 1439420400, 1439424000, 1439427600, 
1439431200, 1439434800, 1439438400, 1439442000, 1439445600, 1439449200, 
1439452800, 1439456400, 1439460000, 1439463600, 1439467200, 1439470800, 
1439474400, 1439478000, 1439481600, 1439485200, 1439488800, 1439492400, 
1439496000, 1439499600, 1439503200, 1439506800, 1439510400, 1439514000, 
1439517600, 1439521200, 1439524800, 1439528400, 1439532000, 1439535600, 
1439539200, 1439542800, 1439546400, 1439550000, 1439553600, 1439557200, 
1439560800, 1439564400, 1439568000, 1439571600, 1439575200, 1439578800, 
1439582400, 1439586000, 1439589600, 1439593200, 1439596800, 1439600400, 
1439604000, 1439607600, 1439611200, 1439614800, 1439618400, 1439622000, 
1439625600, 1439629200, 1439632800, 1439636400, 1439640000, 1439643600, 
1439647200, 1439650800, 1439654400, 1439658000, 1439661600, 1439665200, 
1439668800, 1439672400), tzone = "UTC", tclass = c("chron", "dates", 
"times")), class = c("xts", "zoo"), .indexCLASS = c("chron", 
"dates", "times"), tclass = c("chron", "dates", "times"), .indexTZ = "UTC", tzone = "UTC", .Dim = c(360L, 
1L), .Dimnames = list(NULL, "y"))

My code looks like this: 
library(zoo)

mat_slope=matrix(nrow=i)
mat_intercept=matrix(nrow=i)
for(i in seq(from=24, to=240, by=24)){
  mov.reg<- rollapplyr(Xq,
                       width= i,
                       by=24,
                       FUN = function(y,x) 
                       { 
                         coefficients(lm(formula=y~x, data = Xq))
                       },
                       by.column=FALSE) 
  mat_slope[i] <- coefficients(mov.reg)[2]
  mat_intercept[i] <-coefficients(mov.reg)[1]
  mat_intercept=rbind(i,mat_intercept)
  mat_slope[n,i]=rbind(i, mat_slope)
}

I either get an empty matrix as result, or the following error message:
 Error in merge.xts(res, xts(, idx, attr(data, "frequency"))) : 
  (list) object cannot be coerced to type 'double'

If anyone have some inputs or ideas it would be very appreciated!  Thank's a lot! 

A:

mov.reg is not an lm objct, so it does not make sense to take coefficients of it.   Also there is a reference to chron in the dput output so we need to load the chron package. 
Xq is not specified but rather two separate xts objects are shown so we have assumed that those two are called x and y.  Then create a single zoo object z from them.  Now define a Coef function which takes a matrix with x and y columns and performs the indicated regression returning the coefficients.   Also define roll which takes the width as input and runs rollapplyr with the desired arguments.  Now use lapply to run roll with each width producing a list of zoo objects.   This list has one component for each element in widths consisting of a zoo object with intercept and slope columns.  Finally extract the intercepts from each component in the list and form a zoo object and do the same for the slopes.   Note that if L is a list of zoo objects then do.call("merge", L) will produce a single zoo object from it. 
library(xts) # this also loads zoo
library(chron)

# inputs are xts objects x and y
z <- cbind(x = as.zoo(x)[, 1], y = as.zoo(y)[, 1])

Coef <- function(m) coef(lm(y ~ x, as.data.frame(m)))
roll <- function(w) rollapplyr(z, w, Coef, by = 24, by.column = FALSE)
widths <- seq(24, 240, 24)
names(widths) <- widths # lapply will use these names for its output

L <- lapply(widths, roll)
intercepts <- do.call("merge", lapply(L, "[", TRUE, 1)) # extract 1st columns
slopes <- do.call("merge", lapply(L, "[", TRUE, 2)) # 2nd columns

Alternately do the rollapplyr twice replacing the last 3 lines with:
intercepts <- do.call("merge", lapply(widths, function(w) roll(w)[, 1]))
slopes <- do.call("merge", lapply(widths, function(w) roll(w)[, 2]))

