1
$\begingroup$

I tried the lars package with R and got the following result.

> # load the package
> library(lars)
> dput(datSel)
structure(list(oenb_dependent = c(142.8163942, 143.5711365, 145.3485827, 
142.0577145, 139.4326176, 140.1236581, 138.6560282, 136.405036, 
133.9337229, 133.8785538, 132.0608441, 130.0866307, 120.1320237, 
119.6368882, 114.3312943, 117.5084111, 114.4960017, 112.9124518, 
112.8185478, 112.3047916, 106.632639, 106.2107158, 106.8455028, 
106.3879556, 104.3451786, 102.9085952, 101.0967783, 101.7858278, 
101.0749044, 102.6441976, 102.0666152, 100, 97.14084104, 97.49972913, 
96.91453836, 96.05132443, 94.98057971, 92.78373451, 92.67526281, 
91.82430571, 91.4153859, 89.51740671, 89.01587176, 84.62259911, 
91.48598494, 89.12053042, 90.02364352, 90.92496121, 89.42963565, 
91.93886583, 88.83918306, 90.39513509, 87.54571761, 91.3386451, 
87.7836994, 91.79178376, 87.56903138, 87.77875755, 89.29938784, 
90.88084014), gdp = c(17703.7, 17599.8, 17328.2, 17044, 17078.3, 
16872.3, 16619.2, 16502.4, 16332.5, 16268.9, 16094.7, 15956.5, 
15785.3, 15587.1, 15460.9, 15238.4, 15230.2, 15057.7, 14888.6, 
14681.1, 14566.5, 14384.1, 14340.4, 14383.9, 14549.9, 14843, 
14813, 14668.4, 14685.3, 14569.7, 14422.3, 14233.2, 14066.4, 
13908.5, 13799.8, 13648.9, 13381.6, 13205.4, 12974.1, 12813.7, 
12562.2, 12367.7, 12181.4, 11988.4, 11816.8, 11625.1, 11370.7, 
11230.1, 11103.8, 11037.1, 10934.8, 10834.4, 10701.3, 10639.5, 
10638.4, 10508.1, 10472.3, 10357.4, 10278.3, 10031), employ = c(71.0619, 
70.9383, 71.162, 71.138, 71.2286, 71.5095, 71.565, 71.3246, 71.4963, 
71.3738, 71.4276, 71.3065, 71.0246, 71.3244, 71.0619, 70.9811, 
71.2149, 70.8342, 70.5568, 70.5444, 70.3286, 70.179, 70.2555, 
70.5103, 70.8038, 70.6748, 70.9769, 70.6988, 70.2125, 70.1661, 
69.6284, 69.5613, 68.9837, 68.8606, 68.4223, 67.963, 67.6293, 
67.5905, 67.1857, 67.1248, 66.7075, 66.5857, 66.4303, 66.2826, 
68.7514, 68.8897, 69.0824, 68.9718, 68.7927, 68.6387, 68.8053, 
68.7286, 68.4141, 68.2357, 68.4785, 68.4171, 68.4782, 68.3978, 
68.5344, 68.4772), atx = c(2160.080078, 2203.939941, 2500.850098, 
2523.820068, 2546.54, 2528.449951, 2223.97998, 2352.01001, 2401.21, 
2089.73999, 1975.349976, 2159.060059, 1891.68, 1947.849976, 2766.72998, 
2882.179932, 2947.24, 2541.629883, 2278.800049, 2634, 2495.56, 
2637.280029, 2098.649902, 1696.619995, 1750.83, 2767.76001, 3943.149902, 
3765.909912, 4512.98, 4527.299805, 4869.259766, 4645.5, 4463.47, 
3868.27002, 3745.719971, 4139.830078, 3667.03, 3457.449951, 3049.909912, 
2632.899902, 2431.38, 2042.869995, 1989.400024, 1866.76001, 1545.15, 
1351.890015, 1305.709961, 1163.109985, 1150.05, 1070.209961, 
1243.069946, 1289.16, 1140.36, 1084.069946, 1206.819946, 1186.540039, 
1073.3, 1161.160034, 1129.579956, 1130.069946), un.employ = c(5.7393, 
5.7072, 5.6126, 5.6411, 5.5114, 5.4551, 5.1613, 5.4087, 5.0227, 
5.2039, 4.9501, 4.5008, 4.9143, 4.1372, 4.5604, 4.7979, 4.5454, 
4.8863, 5.0496, 4.9757, 5.4705, 5.8403, 5.4328, 4.6986, 4.4481, 
4.1385, 3.8379, 4.2183, 4.5429, 5.03, 5.1821, 4.8269, 5.0469, 
5.1054, 5.3959, 5.5413, 5.8139, 5.8611, 5.8396, 5.1964, 5.6386, 
5.6615, 5.5751, 5.2251, 4.4682, 4.262, 4.3487, 4.1654, 3.9651, 
3.9105, 3.7954, 4.1595, 3.8174, 3.6349, 3.6119, 3.4004, 3.366, 
3.3953, 3.3621, 3.9338), carReg = c(88.548662, 90.58853576, 91.32289522, 
91.56290683, 108.4682322, 93.86541244, 100.3414441, 91.98328561, 
95.53905246, 102.6461104, 97.9505881, 108.912959, 114.4931447, 
108.0431511, 98.58118608, 107.9440773, 99.41777306, 104.868483, 
100.3338425, 98.06667712, 100.6353811, 100.6491181, 106.4241282, 
79.3180456, 80.40781739, 85.35716451, 102.9110831, 88.99947733, 
99.38928861, 87.57579615, 87.49264945, 90.29013182, 92.13878645, 
90.15141711, 83.90950016, 97.24552675, 93.38024804, 94.16745797, 
98.90106448, 94.73366108, 104.1079291, 98.20132446, 97.70974526, 
91.86162897, 101.5381154, 94.56938821, 86.91581151, 87.16428746, 
87.35114009, 85.0634706, 86.2179337, 82.34156437, 79.86840987, 
84.20717658, 85.29553997, 90.94079268, 92.84823122, 88.90113767, 
88.05502443, 92.38787475), cpi = c(363.81, 361.19, 362.35, 359.09, 
359.31, 355.8, 356.64, 353.83, 353.49, 348.92, 348.8, 344.85, 
343.48, 340.75, 341.1, 335.72, 331.29, 328.21, 328.95, 325.92, 
324.83, 322.83, 323.18, 321.66, 322.94, 323.14, 322.89, 318.34, 
315.85, 311.61, 311.3, 308.34, 306.1, 305.64, 305.58, 302.91, 
301.64, 300.24, 299.54, 298.58, 296.4, 293.87, 293.35, 291.61, 
289.43, 288.03, 287.69, 287.6, 285.95, 284.8, 284.63, 282.62, 
281.24, 280, 280.09, 277.65, 275.73, 273.12, 272.78, 272.25), 
    prodPrice = c(307.5, 308.6, 308.9, 309.7, 311.1, 311.6, 311.6, 
    313.9, 314.9, 314.8, 314.9, 314.5, 313.4, 313, 312.9, 309, 
    304.5, 302.76, 299.28, 293.44, 291.52, 291.71, 290.61, 294.17, 
    297.74, 300.02, 295.91, 292.9, 289.23, 287.49, 285.86, 283.84, 
    281.1, 280.37, 278.63, 275.44, 273.88, 273.24, 274.6, 275.15, 
    269.77, 267.66, 264.29, 262.27, 260.53, 260.52, 261.54, 263.27, 
    261.45, 261.81, 261.99, 261.35, 262.64, 264.74, 265.56, 265.47, 
    267.3, 265.47, 262.64, 260.72), productionConstr = c(103.3086091, 
    102.9085757, 103.6086341, 107.5089591, 107.9089924, 108.9090758, 
    104.3086924, 97.80815068, 104.8087341, 108.0090008, 103.4086174, 
    104.5087091, 105.8088174, 100.308359, 102.6085507, 100.4083674, 
    96.80806734, 99.50829236, 102.708559, 100.7083924, 103.0485874, 
    103.9186599, 104.7887324, 105.0787566, 103.3386116, 104.0186682, 
    102.5685474, 112.4193683, 105.8488207, 104.5987166, 107.3989499, 
    108.6490541, 107.2989416, 106.2388532, 101.3084424, 98.02816901, 
    102.1785149, 97.83815318, 98.70822569, 88.85740478, 92.66772231, 
    95.36794733, 91.4076173, 87.54729561, 89.66747229, 87.73731144, 
    87.34727894, 90.9275773, 78.26652221, 80.29669139, 79.90665889, 
    77.68647387, 77.59646637, 78.46653888, 77.68647387, 77.01641803, 
    84.45703809, 77.97649804, 76.72639387, 77.88649054), constrPriceIndex = c(109.1, 
    109.1, 108.8, 108.2, 107.6, 107.2, 107.3, 106.7, 106.4, 106, 
    105.9, 104.9, 103.8, 103.5, 103, 102.3, 101.3, 100.5, 99.6, 
    98.6, 97.43314, 96.68301, 95.84954, 95.18276, 94.76602, 94.01589, 
    92.84903, 91.18208, 89.76517, 89.18174, 88.51496, 87.76484, 
    86.68132, 85.93119, 85.18107, 84.51429, 83.76416, 83.43077, 
    83.26407, 82.93068, 82.46215, 82.14979, 81.83744, 81.05654, 
    80.43183, 80.35374, 80.27565, 79.9633, 79.72903, 79.57285, 
    79.57285, 79.26049, 79.02623, 79.10432, 79.02623, 78.71387, 
    78.4796, 78.24534, 77.93298, 77.69871), constrCostTotal = c(108.26667, 
    107.96667, 107.46667, 106.76667, 106.66667, 106.6, 106.43333, 
    105.83333, 105, 104.8, 104.46667, 103.46667, 102.4, 102.56667, 
    102.2, 101.96667, 100.77774, 100.47032, 100.41443, 98.48607, 
    97.47997, 97.22844, 96.55771, 96.52976, 96.58566, 98.2066, 
    96.58566, 94.0704, 92.00231, 92.03026, 91.86257, 90.40932, 
    89.26348, 88.84427, 87.19538, 85.32292, 84.28887, 83.61814, 
    83.72993, 83.59019, 83.22324, 82.61167, 82.09794, 80.36107, 
    78.86882, 78.42849, 77.93923, 77.05856, 76.39806, 76.34913, 
    76.22682, 75.39507, 75.05259, 75.24829, 75.12598, 74.34316, 
    74.04961, 73.60927, 73.21786, 72.67968), primConstTot = c(108.56667, 
    108.56667, 108.23333, 107.3, 107.13333, 106.8, 106.63333, 
    105.76667, 105.46667, 105.06667, 104.8, 103.23333, 102.5, 
    102.6, 102.36667, 102.1, 100.5226, 100.32976, 100.71544, 
    98.29121, 97.35458, 97.43723, 96.80362, 96.85872, 96.36285, 
    98.75953, 97.05155, 93.6907, 91.12874, 91.29403, 91.29403, 
    89.44831, 88.07091, 87.57505, 85.86707, 83.96626, 83.4153, 
    82.64396, 82.47867, 82.17564, 82.00498, 81.76645, 81.12244, 
    79.59587, 78.02161, 77.73538, 77.18677, 76.11341, 75.39783, 
    75.42168, 75.04004, 73.94283, 73.94283, 74.08594, 73.7043, 
    72.67864, 72.2493, 71.89151, 71.43831, 70.62732), baumeisterarbeit = c(57844L, 
    57844L, 57667L, 57168L, 57080L, 56904L, 56813L, 56353L, 56193L, 
    55980L, 55838L, 55003L, 54612L, 54666L, 54541L, 54398L, 53567L, 
    53465L, 53670L, 52379L, 51878L, 51923L, 51585L, 51615L, 51351L, 
    52629L, 51718L, 49927L, 48562L, 48649L, 48640L, 47666L, 46932L, 
    46668L, 45758L, 44745L, 44428L, 44046L, 43944L, 43779L, 43690L, 
    43563L, 43219L, 42407L, 41567L, 41416L, 41123L, 40551L, 40170L, 
    40182L, 39979L, 39395L, 39394L, 39471L, 39267L, 38721L, 38514L, 
    38309L, 38061L, 37617L), gesamtbaukost = c(59373L, 59209L, 
    58935L, 58551L, 58496L, 58458L, 58368L, 58039L, 57582L, 57472L, 
    57289L, 56742L, 56156L, 56248L, 56046L, 55919L, 55243L, 55075L, 
    55045L, 53988L, 53436L, 53298L, 52930L, 52915L, 52947L, 53834L, 
    52946L, 51567L, 50433L, 50449L, 50357L, 49557L, 48932L, 48671L, 
    47722L, 46772L, 46213L, 45865L, 45919L, 45826L, 45612L, 45276L, 
    44994L, 44041L, 43225L, 42983L, 42715L, 42232L, 41870L, 41843L, 
    41777L, 41321L, 41132L, 41240L, 41172L, 40743L, 40587L, 40352L, 
    40127L, 39814L), lohn = c(96819L, 96819L, 96090L, 94632L, 
    94632L, 94632L, 93727L, 91917L, 91917L, 91917L, 90779L, 88503L, 
    88416L, 88416L, 88270L, 87978L, 87996L, 87996L, 87566L, 86706L, 
    86706L, 86706L, 85794L, 83970L, 83970L, 83970L, 83007L, 81081L, 
    81081L, 81081L, 80423L, 79107L, 79107L, 79107L, 78321L, 76749L, 
    76533L, 76533L, 75983L, 74883L, 74883L, 74883L, 74575L, 73959L, 
    73959L, 73959L, 73167L, 71583L, 71583L, 71583L, 70858L, 69408L, 
    69408L, 69408L, 68594L, 66966L, 66831L, 66342L, 65853L, 64875L
    ), resProp.Dwell = c(144.5, 146.5, 147.3, 143.3, 140.1, 142.8, 
    141.2, 140.2, 137.8, 137.4, 136.6, 137.6, 125.5, 125.7, 120.5, 
    124.2, 121.5, 119.8, 121.3, 122, 114.1, 114.4, 114.7, 116.1, 
    112.8, 111.8, 110.2, 111.7, 112.2, 113.7, 112.7, 110.5, 107, 
    107.5, 108, 107.1, 106.7, 103.3, 104.2, 104.3, 104.1, 101.3, 
    100.5, 94.3, 105.6, 101, 102, 103.1, 101.4, 105.5, 100.5, 
    102.8, 100.5, 105.1, 98.8, 105.1, 98.2, 98.2, 100.6, 103), 
    resProp.Dwell.1 = c(132.2, 133.9, 133.5, 126, 125, 122.6, 
    122.6, 123.8, 124.5, 120.2, 120.2, 123.5, 105.2, 116.4, 111.5, 
    116.4, 116.1, 114.3, 117, 117.9, 107.1, 104.5, 110.6, 110.5, 
    104.2, 105.4, 106.2, 110.3, 106.8, 111.4, 111.2, 108.5, 93.5, 
    101.5, 101.4, 101.3, 101.7, 96.8, 97.3, 100, 97.5, 99.4, 
    94.8, 93.8, 101.9, 97.4, 97.7, 98.4, 100.6, 100.1, 96.3, 
    98.1, 93.4, 99.3, 97.3, 99.6, 99.2, 97.8, 100.1, 102.9), 
    resProp.Dwell.2 = c(149.8, 151.9, 153.2, 150.7, 146.5, 151.5, 
    149.2, 147.3, 143.6, 144.8, 143.6, 143.7, 134.1, 129.7, 124.3, 
    127.5, 123.7, 122.2, 123.1, 123.8, 117.1, 118.6, 116.4, 118.4, 
    116.4, 114.6, 111.9, 112.2, 114.5, 114.6, 113.4, 111.3, 112.8, 
    110.1, 110.8, 109.5, 108.8, 106.1, 107.1, 106.1, 107, 102.1, 
    103, 94.5, 107.2, 102.5, 103.9, 105.1, 101.7, 107.8, 102.4, 
    104.8, 103.6, 107.6, 99.5, 107.4, 97.8, 98.4, 100.8, 103), 
    resProp.Dwell.3 = c(155.2, 157.6, 159, 156.5, 151.4, 155, 
    152, 149, 146.4, 147.9, 146.6, 146.3, 137.1, 131.1, 124.5, 
    127.5, 123.1, 121.9, 123, 123.5, 116.4, 117.7, 116.4, 118.1, 
    116.5, 113.7, 110.2, 111, 113.9, 113.9, 113.6, 110.9, 113.2, 
    109.9, 111.7, 109.7, 110.1, 106.3, 107.4, 105.9, 107.2, 101.6, 
    103.8, 94.1, 108.4, 102.7, 104.1, 105.1, 101.5, 108.8, 102.3, 
    105.4, 103, 107.2, 99.3, 107.6, 97.4, 97.6, 101.2, 103.9), 
    resProp.Dwell.4 = c(112.6, 112.7, 113.6, 110.7, 113.4, 127.1, 
    130.1, 135.7, 123.7, 123.2, 123, 125.5, 113.5, 120.2, 123.3, 
    128, 128.2, 124.6, 124, 125.8, 122.2, 124.8, 116.6, 120.4, 
    115.9, 120.6, 124, 120.6, 119, 120.1, 111.6, 114, 110.2, 
    111.6, 104.5, 107.9, 100.4, 104.7, 105, 106.9, 105.1, 105.8, 
    97.3, 96.6, 99.1, 101.1, 102.5, 105.2, 103, 101, 102.7, 100.5, 
    107.4, 110.1, 101.3, 105.7, 100.3, 104.1, 98.4, 97.2)), .Names = c("oenb_dependent", 
"gdp", "employ", "atx", "un.employ", "carReg", "cpi", "prodPrice", 
"productionConstr", "constrPriceIndex", "constrCostTotal", "primConstTot", 
"baumeisterarbeit", "gesamtbaukost", "lohn", "resProp.Dwell", 
"resProp.Dwell.1", "resProp.Dwell.2", "resProp.Dwell.3", "resProp.Dwell.4"
), row.names = c(NA, -60L), class = "data.frame")
> 
> x <- as.matrix(datSel[,2:20])
> y <- as.matrix(datSel[,1])
> # fit model
> fit <- lars(x, y, type="lasso")
> # summarize the fit
> summary(fit)
LARS/LASSO
Call: lars(x = x, y = y, type = "lasso")
   Df     Rss         Cp
0   1 20131.2 18858.2870
1   2  1225.5  1095.5502
2   3  1062.7   944.6018
3   4   753.0   655.5314
4   5   695.3   603.3284
5   4   123.4    63.9448
6   5    67.9    13.8219
7   4    66.6    10.5936
8   5    66.2    12.1786
9   6    65.3    13.3891
10  7    64.8    14.8569
11  6    64.2    12.3215
12  7    63.2    13.3932
13  6    61.9    10.2001
14  7    61.6    11.8816
15  8    61.5    13.8049
16  9    61.1    15.4052
17 10    60.5    16.8312
18 11    52.9    11.7031
19 10    52.6     9.4063
20 11    52.4    11.2809
21 12    52.3    13.1369
22 13    51.7    14.6186
23 14    51.6    16.4985
24 13    51.4    14.3144
25 14    51.0    15.9684
26 15    49.7    16.7012
27 16    48.7    17.7799
28 17    48.3    19.3969
29 18    47.5    20.6044
30 19    46.7    21.8417
31 18    46.3    19.5363
32 19    43.2    18.5498
33 20    42.6    20.0000
> # select a step with a minimum error
> best_step <- fit$df[which.min(fit$RSS)]
> # make predictions
> predictions <- predict(fit, x, s=best_step, type="fit")$fit
> # summarize accuracy
> rmse <- mean((y - predictions)^2)
> print(rmse)
[1] 0.8763255
> plot(fit)

lars image

I looked up the summary object in the documentation and the parameters are:

Df        Estimated degree of freedom
Rss       The Residual sum of Squares
Cp        The Cp statistic

However, I am still unsure how to correctly interpret the output? In the documentation is written that it is an anove type summary.

Any recommendtion, which variables should now really be included in the model and which not? How to interprete Df, Rss and Cp correctly?

I appreciate your replies!

UPDATE

After going through the recommended book I came up with this approach: # list coefficients coef(fit) # cv.lars() uses crossvalidation to estimate optimal position in path cvLasso <- cv.lars(y=y, x= x, type="lasso")

# Use the best Cp value to find best model: 
a <- summary(fit)
# Print out coefficients at optimal s.(CP is better than CV for smaller sample size)
coef(fit, s=which.min(a$Cp), mode="step")

Any suggestions for my approach?

$\endgroup$
  • 1
    $\begingroup$ You've only gone part way with deciding how many variables to include. Typically one performs cross-validation at different levels of the penalty parameter to find the best tradeoff between variance and bias, and uses the variables included at the best choice of penalty parameter. See An Introduction to Statistical Learning for examples and exercises. $\endgroup$ – EdM Jul 8 '15 at 16:26
  • $\begingroup$ @EdM Any recommendation, how I could add crossvalidation to my R code? I appreciate if you could add somecode snippets! $\endgroup$ – Kare Jul 8 '15 at 16:28
  • 1
    $\begingroup$ The cv.lars function does this. There are also related functions in the glmnet package. There is a good example in chapter 6 of the freely available text I linked in my previous comment, using glmnet functions. Work through the example in the text and you will know how to proceed for your case; it's better than any snippets I could provide, and it's pretty much how I learned it myself. $\endgroup$ – EdM Jul 8 '15 at 16:42
  • $\begingroup$ @EdM Thx for your book recommendation. I would appreciate if you could take a look at my update and make further suggestions or recommendations! Thx in advance! $\endgroup$ – Kare Jul 8 '15 at 19:06
  • 1
    $\begingroup$ I've used glmnet and not lars so I can't be completely sure. The choice of optimal penalty is often based on minimizing cross-validation error rather than $C_p$; if $C_p$ is better for your sample size, OK. It might help to plot the cross-validation error and variability as a function of the penalty, as a double-check; I think plotCVlars is the appropriate function. You are dealing with a tradeoff between bias and variance, so thinking about what you want to do with the model once you develop it might help inform your choice. $\endgroup$ – EdM Jul 8 '15 at 23:04
2
$\begingroup$

The answers to the original question are fairly straightforward. For a LASSO fit, the degrees of freedom remaining after the fit are supposed take into account the parameters estimated in the model, but there may be some disagreement about just how to determine Df for LASSO and similar regression approaches. Residual sum of squares, as far as I know, is as with any regression. The $C_p$ statistic is an estimate of the mean-square error in a model based on a selected subset of predictors, corrected for the number of predictors. For a Gaussian linear regression, it's equivalent to the AIC.

For variable selection in a way that reliably generalizes beyond your particular data sample, however, you need to do more than perform a single LASSO fit. Typically cross-validation is performed to estimate the prediction error of the model when applied to the underlying population. Performing this at multiple values of the penalty parameter shows the prediction error as a function of the penalty parameter, which in LASSO is effectively the number of predictor variables maintained in the model.

You then have to use your knowledge of the subject matter, along with the results of the cross-validations, to select the final model. A plot of cross-validation error versus the penalty parameter is very informative. Typically the model that minimizes the cross-validation error is selected. You indicate that minimizing $C_p$ might be a better choice for the number of cases you have in your example; I haven't thought about this issue.

Rather than just relying on a formula, think about other considerations you might have, such as the types of errors you can tolerate and the costs of obtaining information when using your model for predictions. For example, you might have particular considerations for the bias-variance tradeoff inherent in this type of model building. Would you rather have a prediction model that on the average gives the true population value but might have high errors in individual samples (low bias, high variance), or one that has less variability from sample to sample but might have a consistent difference on average from the true population value (higher bias, lower variance)? If you want to minimize the number of variables in the model, you might consider choosing the "least complex model within one standard error of the best" (Fig. 3.7, The Elements of Statistical Learning). If you have two correlated predictors with one much more expensive to obtain, should the relative costs enter into your variable-selection criteria? Only you can make those types of judgments.

Extending this approach to time series, as your last comment indicates, is a bit more difficult and beyond my expertise. Cross-validating time series is not straightforward. See this and this Cross Validated page, or search for lasso time series on this site, for introduction to the issues and some guidance.

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