[R] Correlation Matrix with a Covariate

David L Carlson dcarlson at tamu.edu
Tue Sep 2 16:36:16 CEST 2014


Look at your correlation matrices. Your variable names make it more difficult to so I’m abbreviating them:

> dat6 <- dat5
> colnames(dat6) <- abbreviate(colnames(dat5))
> round(cor(dat6[,1:6]), 4)
        c8_SM_7 c7_SM_7 c6_SM_7 c5_SM_7 c4_SM_7 c3_SM_7
c8_SM_7  1.0000 -1.0000 -0.1214  0.1214  0.6000 -0.7816
c7_SM_7 -1.0000  1.0000  0.1214 -0.1214 -0.6000  0.7816
c6_SM_7 -0.1214  0.1214  1.0000 -1.0000 -0.1313  0.0090
c5_SM_7  0.1214 -0.1214 -1.0000  1.0000  0.1313 -0.0090
c4_SM_7  0.6000 -0.6000 -0.1313  0.1313  1.0000 -0.0251
c3_SM_7 -0.7816  0.7816  0.0090 -0.0090 -0.0251  1.0000

col 1 is perfectly correlated with col 2
col 3 is perfectly correlated with col 4
col 5 is perfectly correlated with col 6

David C

From: Patzelt, Edward [mailto:patzelt at g.harvard.edu] 
Sent: Tuesday, September 2, 2014 9:21 AM
To: David L Carlson
Cc: R-help at r-project.org
Subject: Re: [R] Correlation Matrix with a Covariate

Even reducing the command to less variables than observations I still get:

set.cor(y = (66:76), x = c(1:6), z = 65, data = dat5)
Error in solve.default(x.matrix, xy.matrix) : 
  Lapack routine dgesv: system is exactly singular: U[2,2] = 0

On Mon, Sep 1, 2014 at 12:25 PM, David L Carlson <dcarlson at tamu.edu> wrote:
Thanks for including your data with dput(). I'm not familiar with set correlation, but altogether you are working with 76 variables (columns) and only 46 observations. Since the error message says "the system is exactly singlular," it is likely that you have too many variables for the number of observations or one of your columns is a linear combination of (can be predicted exactly from) the others.

-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352


-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Patzelt, Edward
Sent: Monday, September 1, 2014 7:47 AM
To: R-help at r-project.org
Subject: [R] Correlation Matrix with a Covariate

R Help -

I'm trying to run a correlation matrix with a covariate of "age" and will
at some point will also want to covary other variables concurrently.

I'm using the "psych" package and have tried other methods such as writing
a loop to extract semi-partial correlations, but it does not seem to be
working. How can I accomplish this?

library(psych)
> set.cor(y = (66:76), x = c(1:64), z = 65, data = dat5)
Error in solve.default(x.matrix, xy.matrix) :
  Lapack routine dgesv: system is exactly singular: U[54,54] = 0

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    cope3_StriatumMask_4_betas_mean = c(-4.475, 8.136, 20.85,
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    cope2_StriatumMask_4_betas_mean = c(2.537, -19.08, -18.83,
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    6.899, -2.527, -4.107, 6.232, 2.13, 0.2781, -27.52),
cope1_StriatumMask_4_betas_mean = c(-9.172,
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    -1.847, -17.35, -8.623, -5.062, -2.434, -8.646, 5.55, 0.654,
    -17.52, 6.386, -10.25, -10.51, -1.227, -4.453, 1.159, -50.89,
    -3.253, 16.37, -5.982, 9.533, 11.49, -13.87, 4.944, 6.463,
    -28.5, 5.955, 2.492, 8.083, 1.306, 7.27, -6.711, -13.84,
    -3.173, -6.582), cope8_StriatumMask_3_betas_mean = c(-9.827,
    -29.39, -5.072, -29.28, 18.69, 7.461, -25.71, 34.55, -56.11,
    -2.596, -33.97, 19.12, 28.57, 19.91, -16.04, 14.72, 68.99,
    50.07, 54.12, 38.88, 7.486, -10.9, -3.818, 13.97, 4.118,
    -7.899, -52.3, 0.5233, -3.36, -8.542, -26.68, 19.28, -14.09,
    -9.591, 6.136, -17.34, -13.37, -18.57, 7.236, -9.855, -21.91,
    4.76, 2.585), cope7_StriatumMask_3_betas_mean = c(9.827,
    29.39, 5.072, 29.28, -18.69, -7.461, 25.71, -34.55, 56.11,
    2.596, 33.97, -19.12, -28.57, -19.91, 16.04, -14.72, -68.99,
    -50.07, -54.12, -38.88, -7.486, 10.9, 3.818, -13.97, -4.118,
    7.899, 52.3, -0.5233, 3.36, 8.542, 26.68, -19.28, 14.09,
    9.591, -6.136, 17.34, 13.37, 18.57, -7.236, 9.855, 21.91,
    -4.76, -2.585), cope6_StriatumMask_3_betas_mean = c(22.87,
    -25.94, -11.68, -2.203, -10.23, -53.78, -83.96, 49.48, -11.44,
    -5.176, -52.12, 75.78, -4.78, 8.202, 36.8, -20.51, 41.93,
    21.93, -23.74, 13.54, 16.14, 0.104, -10.84, 1.206, -67.28,
    13.01, -39.97, 26.5, 29.88, -20.17, -17.02, -16.57, -31.55,
    -3.754, 13.22, -60.05, -18.23, -20.84, -32.5, -30.19, 17.32,
    -6.671, -68.85), cope5_StriatumMask_3_betas_mean = c(-22.87,
    25.94, 11.68, 2.203, 10.23, 53.78, 83.96, -49.48, 11.44,
    5.176, 52.12, -75.78, 4.78, -8.202, -36.8, 20.51, -41.93,
    -21.93, 23.74, -13.54, -16.14, -0.104, 10.84, -1.206, 67.28,
    -13.01, 39.97, -26.5, -29.88, 20.17, 17.02, 16.57, 31.55,
    3.754, -13.22, 60.05, 18.23, 20.84, 32.5, 30.19, -17.32,
    6.671, 68.85), cope4_StriatumMask_3_betas_mean = c(-16.05,
    2.658, -4.415, -12.2, 1.195, 13.03, -29.99, 33.07, -8.351,
    -14.77, -17.69, 30.84, 34.35, -6.265, -24.46, 31.91, 55.98,
    55.14, 10.99, 28.58, -0.6548, -22.82, -8.781, -4.039, 47.5,
    -10.17, -25.6, 9.067, -7.805, 4.68, -14.51, 30.94, -5.437,
    11.18, -4.785, -28.78, 7.116, -8.826, 28.71, -7.045, -15,
    6.487, 3.255), cope3_StriatumMask_3_betas_mean = c(-5.496,
    24.4, -6.178, 15.86, -14.12, 27.35, -9.234, 0.7806, 42.1,
    -14.02, 14.09, 10.54, 6.996, -11.21, -7.568, 20.19, -8.178,
    -0.5523, -30.68, -20.66, -7.418, -10.89, -7.568, -12.52,
    30.39, 20.52, 32.13, 7.49, -1.385, 21.98, 11.6, 16.08, 8.176,
    20.77, -9.026, -9.19, 25.62, 11.64, 25.4, 11.66, 8.522, 4.707,
    -0.9193), cope2_StriatumMask_3_betas_mean = c(1.248, -20.2,
    -12.85, -10.37, -6.481, -38.99, -40.29, 50.83, -9.962, -11.15,
    -42.64, 76.49, 17.13, 12.02, 1.068, -1.049, 50.45, 8.55,
    -13.25, -2.733, 13.62, -1.994, -9.352, 11.84, -53.07, 15.24,
    -10.38, 9.065, 21.26, 7.247, -9.854, -18.25, -5.975, -21.84,
    9.668, -50.28, 0.1118, -18.12, -13.33, -30.13, -2.708, 7.863,
    -45.51), cope1_StriatumMask_3_betas_mean = c(-16.21, 2.177,
    -0.6828, 2.681, 6.963, 16.77, 27.42, -2.669, 2.728, 0.2155,
    15.99, 0.9732, 22.23, -4.741, -22.42, 15.15, 4.144, -13.74,
    13.43, -8.654, -3.702, -5.625, 1.369, 11.16, -29.82, 1.106,
    25.75, 0.3423, 6.333, 24.31, 2.151, -2.602, 12, -11.66, 2.765,
    11.51, 14.19, 3.124, 20.18, -1.8, -1.045, 5.62, 13.11),
cope8_StriatumMask_2_betas_mean = c(25.81,
    0.1008, -17.35, -20.65, 11.98, 22.12, 6.479, -8.857, 12.72,
    -4.564, -21.54, 13.93, 50.64, -19.73, -19.55, 30.95, 33.47,
    10.15, -12.42, 17.9, -17.26, -27.29, 4.29, 19.53, -9.623,
    -25.66, -41.7, 11.19, 12.26, 18.57, 3.319, 13.15, -19.85,
    8.024, -30.64, -3.4, -15.03, 11.73, 38.76, 60.92, -35.55,
    -25.83, 38.08), cope7_StriatumMask_2_betas_mean = c(-25.81,
    -0.1008, 17.35, 20.65, -11.98, -22.12, -6.479, 8.857, -12.72,
    4.564, 21.54, -13.93, -50.64, 19.73, 19.55, -30.95, -33.47,
    -10.15, 12.42, -17.9, 17.26, 27.29, -4.29, -19.53, 9.623,
    25.66, 41.7, -11.19, -12.26, -18.57, -3.319, -13.15, 19.85,
    -8.024, 30.64, 3.4, 15.03, -11.73, -38.76, -60.92, 35.55,
    25.83, -38.08), cope6_StriatumMask_2_betas_mean = c(32.23,
    -30.2, -14.07, 8.605, 3.412, -26.22, -33.54, 44.45, 26.12,
    6.825, 3.423, 82.44, 21.13, 7.608, 25.97, -32.8, 39.59, 20.19,
    -11.03, 22.94, 1.544, 12.38, -8.389, 16.52, -20.52, 14.38,
    -10.88, 47.39, 14.78, 6.086, 44.31, -16.1, -7.038, 1.967,
    -0.5604, -16.6, -4.619, -27.21, -21.32, 7.096, 1.417, -0.6048,
    -13.26), cope5_StriatumMask_2_betas_mean = c(-32.23, 30.2,
    14.07, -8.605, -3.412, 26.22, 33.54, -44.45, -26.12, -6.825,
    -3.423, -82.44, -21.13, -7.608, -25.97, 32.8, -39.59, -20.19,
    11.03, -22.94, -1.544, -12.38, 8.389, -16.52, 20.52, -14.38,
    10.88, -47.39, -14.78, -6.086, -44.31, 16.1, 7.038, -1.967,
    0.5604, 16.6, 4.619, 27.21, 21.32, -7.096, -1.417, 0.6048,
    13.26), cope4_StriatumMask_2_betas_mean = c(15.01, 22.46,
    -4.224, 15.49, 6.115, 30.69, -9.205, 11, 33.86, -11.47, 8.718,
    40.24, 32.94, 10.32, -21.59, 40.88, 42.59, 38.26, -11.98,
    10.31, -11.61, -11.29, 8.09, 16.8, 26.23, -13.6, -9.128,
    13.4, 11.02, 42.82, -7.533, 31.68, 11.7, 18.03, -28.57, -16.58,
    8.403, 16.38, 50.65, 52.36, -33.33, -3.843, 40.4),
cope3_StriatumMask_2_betas_mean = c(-8.702,
    17.62, 7.219, 28.2, -1.924, 25.78, -19.31, 22.12, 22.83,
    -8.054, 28.44, 23.53, -16.75, 5.805, -4.127, 6.258, 14.48,
    13.64, -0.4828, -13.26, 4.439, 12.69, 0.6518, 7.227, 26.09,
    23.32, 36.05, 12.2, -0.9937, 33.69, -11.5, 21.97, 30.37,
    9.786, 5.107, -10.34, 22.97, 13.41, 16.56, 6.332, 5.224,
    21.78, -2.798), cope2_StriatumMask_2_betas_mean = c(12.04,
    -27.28, -12.81, 3.426, 6.537, -20.93, -3.958, 32.95, 31.27,
    3.16, 1.62, 84.87, 17.59, 16.39, 7.736, -19.22, 37.3, 10.39,
    -10.59, 4.182, -1.269, 13.05, -9.96, 22, -31.62, 19.31, 7.048,
    27.36, 16.38, 25.42, 27.44, -10.14, 11.67, -23.71, 1.572,
    -10.1, -0.7817, -14.73, -10.15, -1.167, -16.26, 4.545, -10.31
    ), cope1_StriatumMask_2_betas_mean = c(-11.59, -0.5801, 2.253,
    2.92, 3.628, 9.823, 16.41, -7.863, 5.275, -1.153, 2.206,
    -4.593, -1.623, 1.228, -2.079, 10.64, -3.458, -11.28, 4.056,
    -6.751, -5.711, -1.179, -2.362, 4.503, -45.23, 4.45, 15.2,
    0.3556, 7.904, 17.05, -14.17, 3.913, 8.457, -16.94, 3.676,
    6.673, 2.067, 8.915, 12.39, -10.32, -5.923, -0.4391, 0.09663
    ), cope8_StriatumMask_1_betas_mean = c(-7.671, 0.7324, 18.97,
    -28.64, 13.36, 26.06, 12.31, 45.68, -19.4, -15.43, -21.18,
    10.45, 40.84, 3.266, 28.25, 10.6, 50.66, 53.76, 26.57, 47.64,
    5.642, 5.603, -10.04, 37.04, 27.35, -41.75, -26.86, -11.13,
    13.09, 44.36, -1.843, 2.302, 0.6855, 14.22, -25.71, -9.987,
    -3.158, -39.82, 5.276, -12.38, -31.06, -4.346, 52.18),
cope7_StriatumMask_1_betas_mean = c(7.671,
    -0.7324, -18.97, 28.64, -13.36, -26.06, -12.31, -45.68, 19.4,
    15.43, 21.18, -10.45, -40.84, -3.266, -28.25, -10.6, -50.66,
    -53.76, -26.57, -47.64, -5.642, -5.603, 10.04, -37.04, -27.35,
    41.75, 26.86, 11.13, -13.09, -44.36, 1.843, -2.302, -0.6855,
    -14.22, 25.71, 9.987, 3.158, 39.82, -5.276, 12.38, 31.06,
    4.346, -52.18), cope6_StriatumMask_1_betas_mean = c(-10.78,
    -25.07, -17.08, 2.81, -6.211, -41.83, -9.084, 62.16, -32.2,
    -22.2, -28.83, 47.89, 17.46, 25.87, 52.77, -12.9, 11.86,
    9.416, -5.891, 16.54, 19.86, -7.199, -16.48, -13.35, -20.79,
    13.97, -8.254, -9.45, 10.29, -3.625, 15.06, -47.86, -35.86,
    8.605, -0.989, -20.42, -30.45, -54.43, -44.19, -32.61, 14.31,
    0.9104, -14.45), cope5_StriatumMask_1_betas_mean = c(10.78,
    25.07, 17.08, -2.81, 6.211, 41.83, 9.084, -62.16, 32.2, 22.2,
    28.83, -47.89, -17.46, -25.87, -52.77, 12.9, -11.86, -9.416,
    5.891, -16.54, -19.86, 7.199, 16.48, 13.35, 20.79, -13.97,
    8.254, 9.45, -10.29, 3.625, -15.06, 47.86, 35.86, -8.605,
    0.989, 20.42, 30.45, 54.43, 44.19, 32.61, -14.31, -0.9104,
    14.45), cope4_StriatumMask_1_betas_mean = c(4.84, 8.677,
    12.27, -7.647, 16.95, 11.6, -4.777, 36.14, -0.6954, -16.71,
    6.837, 24.48, 33.52, 16.08, 2.818, 33.76, 40.46, 49.53, 12.67,
    17.85, 4.385, -2.631, -6.774, 19.03, 24.32, -20.09, -10.17,
    -9.06, 8.567, 32.12, -2.03, -1.62, 0.146, 37.04, -22.95,
    -23.3, 8.028, -17.19, 20.37, 16.84, -22.3, -6.429, 51.04),
    cope3_StriatumMask_1_betas_mean = c(15.35, 5.55, -18.41,
    15.07, 3.556, 7.953, -14.82, -9.422, 16.4, -0.3186, 24.63,
    12.67, -6.266, 2.59, -20.88, 19.75, -6.401, -2.396, -5.993,
    -35.32, 2.134, -4.257, 2.744, -9.988, -11.43, 32.36, 18.16,
    1.64, -6.096, -2.82, -1.367, -2.237, -0.7249, 22.74, 2.451,
    -9.7, 20.11, 29.75, 16.12, 33.47, 11.92, -2.012, -1.725),
    cope2_StriatumMask_1_betas_mean = c(-22.73, -15.83, -19.77,
    -8.785, -1.132, -28.61, -8.759, 55.94, -23.65, -11.41, -25.9,
    55.99, 23.15, 20.83, 1.789, -8.291, 18.53, -3.007, 3.943,
    -6.776, 6.329, -9.923, -15.03, -6.602, -28.07, 15.85, 4.005,
    -9.185, 7.425, 15.21, 14.91, -29.58, -32.79, -8.068, 9.733,
    -14.88, -6.852, -45.85, -28.66, -33.56, -0.5571, 9.592, -19.33
    ), cope1_StriatumMask_1_betas_mean = c(-8.128, 5.758, -1.077,
    -2.914, 7.331, 17.59, 7.21, -1.398, 9.864, 9.548, 7.269,
    -1.59, 4.966, -2.957, -33.94, 4.149, 4.05, -12.29, 9.097,
    -12.87, -15.36, -2.254, 1.258, 6.453, -29.28, 0.6771, 7.286,
    12.21, 7.805, 16.56, -0.8941, 13.69, 1.07, -9.846, 10.8,
    9.538, 21.36, 6.965, 14.37, -2.649, 0.8941, 2.795, -7.913
    ), cope8_StriatumMask_betas_mean = c(19.19, 8.326, -14.14,
    -47.34, 21.49, -12.56, -16.33, 20.01, 1.212, 10.1, -35.27,
    -3.354, 17.25, 9.637, -4.914, 6.339, 41.29, 38.17, 23.69,
    10.55, -9.964, -7.758, -15.25, 32.33, -26.52, 2.892, -61.29,
    -5.893, 5.116, -1.717, 8.98, -3.727, -29.21, 0.6858, -19.31,
    5.567, -11.3, -11.28, 21.09, 27.76, -41.58, -22.77, 23.96
    ), cope7_StriatumMask_betas_mean = c(-19.19, -8.326, 14.14,
    47.34, -21.49, 12.56, 16.33, -20.01, -1.212, -10.1, 35.27,
    3.354, -17.25, -9.637, 4.914, -6.339, -41.29, -38.17, -23.69,
    -10.55, 9.964, 7.758, 15.25, -32.33, 26.52, -2.892, 61.29,
    5.893, -5.116, 1.717, -8.98, 3.727, 29.21, -0.6858, 19.31,
    -5.567, 11.3, 11.28, -21.09, -27.76, 41.58, 22.77, -23.96
    ), cope6_StriatumMask_betas_mean = c(-3.001, -19.78, -17.55,
    -13.93, -26.77, -33.2, -41.41, 0.257, -8.959, -1.035, 4.296,
    34.65, -9.697, -26.18, 56.74, -26.94, 14.28, 12.58, -34.56,
    -3.228, 11.26, -16.65, -10.86, -6.784, -24.45, 23.86, -8.12,
    18.23, 21.56, -17.01, 36.51, -34.84, -34.06, -0.2884, 8.737,
    -20.99, -15.66, -17.38, -34.47, -24.96, 49.42, -2.499, -52.5
    ), cope5_StriatumMask_betas_mean = c(3.001, 19.78, 17.55,
    13.93, 26.77, 33.2, 41.41, -0.257, 8.959, 1.035, -4.296,
    -34.65, 9.697, 26.18, -56.74, 26.94, -14.28, -12.58, 34.56,
    3.228, -11.26, 16.65, 10.86, 6.784, 24.45, -23.86, 8.12,
    -18.23, -21.56, 17.01, -36.51, 34.84, 34.06, 0.2884, -8.737,
    20.99, 15.66, 17.38, 34.47, 24.96, -49.42, 2.499, 52.5),
    cope4_StriatumMask_betas_mean = c(15.33, 19, 2.13, 0.6255,
    3.965, -1.875, -11.55, 31.71, 10.09, 6.487, 11.98, 22.06,
    18.12, 14.37, -12.51, 15.88, 43.94, 42.62, 28.01, 5.568,
    7.426, -9.192, -2.169, 2.482, 15.11, -12.14, -29.41, -0.2639,
    19.3, 15.57, 0.9526, 11.34, -3.821, 24.48, -6.627, -8.762,
    9.8, -6.399, 37.83, 25.74, -14.33, 4.295, 21.01),
cope3_StriatumMask_betas_mean = c(-3.592,
    6.864, 7.348, 44, -11.51, 24.63, 1.817, 13.9, 9.993, -2.448,
    46.96, 24.46, 1.184, 2.201, -9.854, 8.554, 11.19, 5.276,
    6.545, -10.3, 18.45, 0.09225, 14.79, -21.75, 26.66, -5.553,
    31.19, 0.8526, 10.5, 20.36, -8.67, 16.04, 25.48, 24.09, 7.305,
    -12.65, 25.91, 15.63, 15.62, 4.822, 29.16, 26.04, -6.085),
    cope2_StriatumMask_betas_mean = c(-13.48, -17.98, -22.68,
    -9.708, -22.45, -23.12, -13.93, 12.69, -4.801, -1.965, -6.992,
    46.79, -6.076, -17.57, 13.97, -10.84, 23.23, 4.383, -21.55,
    -22.44, 4.44, -10.1, -9.803, 3.025, -28.17, 22, 4.17, 9.131,
    19.87, 5.414, 22.37, -25.66, -7.043, -30.76, 11.91, -14.59,
    -2.368, -8.886, -16.82, -21.95, 18.63, 2.53, -49.32),
cope1_StriatumMask_betas_mean = c(-5.115,
    -1.257, -4.329, 17.92, 5.458, 15.17, 16.84, 7.652, 3.981,
    0.3726, -10.51, -5.246, 4.09, 6.008, -20.68, 12.2, 9.368,
    -6.345, 14.45, -6.155, -10.31, 2.61, 0.6008, 7.258, -48.2,
    -2.675, 8.916, 1.677, 8.255, 18.35, -13.18, 8.82, 9.647,
    -21.62, 6.05, 7.721, 10.41, 6.71, 16.76, 2.002, -13.56, 2.499,
    -6.206), age = c(34L, 32L, 34L, 22L, 27L, 33L, 33L, 41L,
    21L, 20L, 32L, 30L, 29L, 37L, 29L, 25L, 31L, 20L, 32L, 22L,
    24L, 26L, 20L, 22L, 34L, 34L, 44L, 40L, 43L, 36L, 41L, 22L,
    41L, 26L, 41L, 45L, 36L, 31L, 31L, 27L, 40L, 23L, 27L), hare2f1 = c(3L,
    10L, 7L, 5L, 4L, 3L, 5L, 6L, 13L, 13L, 7L, 9L, 12L, 7L, 4L,
    13L, 12L, 11L, 14L, 13L, 10L, 6L, 6L, 13L, 11L, 10L, 13L,
    16L, 6L, 8L, 8L, 11L, 9L, 13L, 10L, 9L, 5L, 9L, 5L, 12L,
    13L, 9L, 8L), hare2f2 = c(14, 18, 3, 10, 6, 6, 9, 7, 18.75,
    16, 15, 12, 14, 18, 14, 18, 18, 19, 18, 15.555556, 14.444444,
    12, 1.111111, 17, 12, 17, 14, 13, 16, 7.777778, 6.666667,
    13, 14, 13, 11, 16, 4, 12, 10, 13, 14, 17, 12), hare4 = c(10,
    8, 2, 3, 2, 3, 5, 2, 9, 8, 6, 4, 6, 10, 6, 8, 9, 9, 9, 7.5,
    7, 5, 0, 9, 4, 10, 10, 9, 8, 1.25, 0, 7, 7, 7, 5, 9, 0, 3,
    4, 7, 4, 9, 3), hare = c(20, 32, 10, 16, 11, 11, 15, 15,
    35.294118, 33, 26, 22, 27, 27, 22, 33, 33, 32, 34, 29.473684,
    27.368421, 20, 8.421053, 32, 27, 30, 30, 31, 24, 16.842105,
    15.789474, 25, 26, 30, 23, 29, 10, 22, 16, 29, 30, 27, 24
    ), ext_t = c(193L, 374L, 127L, 181L, 198L, 112L, 208L, 194L,
    185L, 224L, 275L, 204L, 235L, 257L, 149L, 279L, 320L, 322L,
    188L, 220L, 273L, 206L, 129L, 296L, 223L, 277L, 156L, 195L,
    219L, 155L, 167L, 176L, 220L, 227L, 224L, 195L, 150L, 277L,
    134L, 285L, 255L, 225L, 176L), total_barrat_11_imputed = c(52,
    49.65517241, 49, 50, 59, 33, 80, 59, 49, 88.96551724, 46,
    69, 55, 61, 55, 81, 63, 78, 63, 77, 74, 56, 66, 65, 64, 80,
    47, 41, 55, 61, 69, 65, 54, 60, 75, 48, 55, 92, 55, 80, 61,
    74, 75), mcq_k = c(0.026255008, 0.001003094, 0.005960345,
    0.015726049, 0.010265035, 0.001003162, 0.0000562, 0.001264102,
    0.101500481, 0.000371013, 0.041432153, 0.002816398, 0.000610156,
    0.002515929, 0.013917576, 0.041429705, 0.001003197, 0.015963745,
    0.004571462, 0.066152274, 0.001264173, 0.013917914, 0.0000145,
    0.000158281, 0.025279846, 0.007219305, 0.001003151, 0.000158275,
    0.040812627, 0.015218595, 0.0000234, 0.008376655, 0.003841158,
    0.007218929, 0.011170246, 0.001264155, 0.002183796, 0.041432825,
    0.018248241, 0.025279435, 0.00728945, 0.015218596, 0.000139047
    ), ppitots = c(127L, 144L, 123L, 139L, 132L, 104L, 124L,
    101L, 139L, 143L, 125L, 118L, 143L, 136L, 101L, 155L, 121L,
    163L, 144L, 137L, 161L, 132L, 129L, 172L, 130L, 149L, 130L,
    127L, 131L, 100L, 134L, 140L, 130L, 156L, 129L, 105L, 104L,
    144L, 131L, 155L, 141L, 148L, 122L), ppi_1_corrected = c(0.790903075,
    -0.699393955, -2.327819799, 1.081391412, 1.312482037, -1.237538376,
    -2.908572705, -1.431192482, 0.849643836, -0.164898186, -2.41690919,
    -2.180646599, 2.448811184, -0.690782751, -3.013794372, -0.299602118,
    -2.681787682, 1.644921908, -0.813003057, 1.123342946, 5.134178123,
    0.079542428, -0.261508648, 3.433661529, -0.273992274, 2.158546617,
    1.906361161, 1.422218718, -0.391906978, -3.50632425, -1.107140046,
    2.554466033, -0.185769247, 2.924775604, -1.911895686, -4.074384118,
    -3.514935455, -1.132749891, 3.135218938, 2.554032851, 1.165085067,
    1.001346409, -0.06377271), ppi_2_corrected = c(-2.126526342,
    2.11252134, 0.555050342, -1.00112002, 0.340187762, -5.601376296,
    1.123969936, -4.711504227, 0.689931234, 0.579906262, 1.063194228,
    -0.20079187, -0.692661735, 1.762800655, -3.876379279, 3.649272316,
    1.919311154, 6.385631495, 2.52094701, -0.266955763, 0.107879822,
    0.040699835, -0.035262664, 4.854103646, -0.699493009, 1.446789539,
    -2.783996549, -3.166278249, 0.253753447, -2.916502162, 0.483071268,
    -1.851965471, -0.491096031, 0.403290401, 0.807272843, -0.914685705,
    -2.568897775, 4.595852557, -3.588214202, 2.506562905, 0.802072504,
    1.563208978, -2.362131731), total_buss_perry = c(55L, 79L,
    64L, 79L, 86L, 37L, 85L, 69L, 76L, 109L, 80L, 62L, 46L, 85L,
    48L, 119L, 124L, 131L, 94L, 56L, 95L, 62L, 78L, 106L, 93L,
    76L, 46L, 54L, 61L, 61L, 76L, 94L, 98L, 85L, 94L, 70L, 65L,
    70L, 45L, 114L, 99L, 92L, 61L)), .Names =
c("cope8_StriatumMask_7_betas_mean",
"cope7_StriatumMask_7_betas_mean", "cope6_StriatumMask_7_betas_mean",
"cope5_StriatumMask_7_betas_mean", "cope4_StriatumMask_7_betas_mean",
"cope3_StriatumMask_7_betas_mean", "cope2_StriatumMask_7_betas_mean",
"cope1_StriatumMask_7_betas_mean", "cope8_StriatumMask_6_betas_mean",
"cope7_StriatumMask_6_betas_mean", "cope6_StriatumMask_6_betas_mean",
"cope5_StriatumMask_6_betas_mean", "cope4_StriatumMask_6_betas_mean",
"cope3_StriatumMask_6_betas_mean", "cope2_StriatumMask_6_betas_mean",
"cope1_StriatumMask_6_betas_mean", "cope8_StriatumMask_5_betas_mean",
"cope7_StriatumMask_5_betas_mean", "cope6_StriatumMask_5_betas_mean",
"cope5_StriatumMask_5_betas_mean", "cope4_StriatumMask_5_betas_mean",
"cope3_StriatumMask_5_betas_mean", "cope2_StriatumMask_5_betas_mean",
"cope1_StriatumMask_5_betas_mean", "cope8_StriatumMask_4_betas_mean",
"cope7_StriatumMask_4_betas_mean", "cope6_StriatumMask_4_betas_mean",
"cope5_StriatumMask_4_betas_mean", "cope4_StriatumMask_4_betas_mean",
"cope3_StriatumMask_4_betas_mean", "cope2_StriatumMask_4_betas_mean",
"cope1_StriatumMask_4_betas_mean", "cope8_StriatumMask_3_betas_mean",
"cope7_StriatumMask_3_betas_mean", "cope6_StriatumMask_3_betas_mean",
"cope5_StriatumMask_3_betas_mean", "cope4_StriatumMask_3_betas_mean",
"cope3_StriatumMask_3_betas_mean", "cope2_StriatumMask_3_betas_mean",
"cope1_StriatumMask_3_betas_mean", "cope8_StriatumMask_2_betas_mean",
"cope7_StriatumMask_2_betas_mean", "cope6_StriatumMask_2_betas_mean",
"cope5_StriatumMask_2_betas_mean", "cope4_StriatumMask_2_betas_mean",
"cope3_StriatumMask_2_betas_mean", "cope2_StriatumMask_2_betas_mean",
"cope1_StriatumMask_2_betas_mean", "cope8_StriatumMask_1_betas_mean",
"cope7_StriatumMask_1_betas_mean", "cope6_StriatumMask_1_betas_mean",
"cope5_StriatumMask_1_betas_mean", "cope4_StriatumMask_1_betas_mean",
"cope3_StriatumMask_1_betas_mean", "cope2_StriatumMask_1_betas_mean",
"cope1_StriatumMask_1_betas_mean", "cope8_StriatumMask_betas_mean",
"cope7_StriatumMask_betas_mean", "cope6_StriatumMask_betas_mean",
"cope5_StriatumMask_betas_mean", "cope4_StriatumMask_betas_mean",
"cope3_StriatumMask_betas_mean", "cope2_StriatumMask_betas_mean",
"cope1_StriatumMask_betas_mean", "age", "hare2f1", "hare2f2",
"hare4", "hare", "ext_t", "total_barrat_11_imputed", "mcq_k",
"ppitots", "ppi_1_corrected", "ppi_2_corrected", "total_buss_perry"
), row.names = c(NA, -43L), class = "data.frame")


--
*Edward H Patzelt | Clinical Science PhD StudentPsychology | Harvard
University *

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-- 
Edward H Patzelt | Clinical Science PhD Student
Psychology | Harvard University 

SNPLab http://scholar.harvard.edu/buckholtz
 


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