Question

我对summary.lm()输出有疑问。

首先，这里是我的数据集的可重现代码：

Cond_Per_Row_stats<-structure(list(Participant = structure(c(1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), .Label = c("21", "22", 
"23", "24", "25", "26", "27", "28", "29", "30"), class = "factor"), 
    Coherence = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L), .Label = c("P0.0", "P3", "P35", 
    "P4", "P45"), class = "factor"), PrimeType = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("fp", 
    "np", "tp"), class = "factor"), PrimeDuration = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1200ms", 
    "50ms"), class = "factor"), Condition = structure(c(21L, 
    21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 
    22L, 22L, 22L, 22L, 22L, 22L, 22L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 25L, 25L, 25L, 25L, 25L, 
    25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
    26L, 26L, 26L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 15L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
    16L, 16L, 16L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
    23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
    13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 29L, 
    29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 
    30L, 30L, 30L, 30L, 30L, 30L, 30L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 27L, 27L, 27L, 
    27L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 
    28L, 28L, 28L, 28L, 28L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 17L, 17L, 
    17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 
    18L, 18L, 18L, 18L, 18L, 18L), .Label = c("P0.0np1200.0", 
    "P0.0np50.0", "P3np1200.0", "P3np50.0", "P35np1200.0", "P35np50.0", 
    "P4np1200.0", "P4np50.0", "P45np1200.0", "P45np50.0", "P0.0tp1200.0", 
    "P0.0tp50.0", "P3tp1200.0", "P3tp50.0", "P35tp1200.0", "P35tp50.0", 
    "P4tp1200.0", "P4tp50.0", "P45tp1200.0", "P45tp50.0", "P0.0fp1200.0", 
    "P0.0fp50.0", "P3fp1200.0", "P3fp50.0", "P35fp1200.0", "P35fp50.0", 
    "P4fp1200.0", "P4fp50.0", "P45fp1200.0", "P45fp50.0"), class = "factor"), 
    Accuracy = c(0.785398163397448, 0.523598775598299, 0.785398163397448, 
    0.523598775598299, 0.785398163397448, 0.869122203007293, 
    0.955316618124509, 0.785398163397448, 0.615479708670387, 
    0.701674123787604, 1.15026199151093, 1.15026199151093, 0.869122203007293, 
    0.523598775598299, 0.701674123787604, 0.701674123787604, 
    0.955316618124509, 0.701674123787604, 0.955316618124509, 
    0.615479708670387, 0.955316618124509, 0.785398163397448, 
    0.701674123787604, 0.869122203007293, 0.785398163397448, 
    0.615479708670387, 0.615479708670387, 0.869122203007293, 
    0.701674123787604, 0.615479708670387, 1.0471975511966, 0.869122203007293, 
    0.615479708670387, 0.615479708670387, 0.869122203007293, 
    0.701674123787604, 0.701674123787604, 0.869122203007293, 
    0.785398163397448, 0.869122203007293, 1.0471975511966, 0.955316618124509, 
    0.523598775598299, 1.0471975511966, 0.615479708670387, 0.955316618124509, 
    0.615479708670387, 0.785398163397448, 0.955316618124509, 
    0.785398163397448, 0.701674123787604, 0.615479708670387, 
    0.615479708670387, 0.955316618124509, 0.869122203007293, 
    0.869122203007293, 1.0471975511966, 0.785398163397448, 0.701674123787604, 
    0.785398163397448, 1.0471975511966, 0.911738290968488, 1.00028587904971, 
    0.827113206702756, 0.785398163397448, 1.00028587904971, 1.09681145610345, 
    1.00028587904971, 1.0471975511966, 1.09681145610345, 1.0471975511966, 
    0.827113206702756, 1.0471975511966, 0.420534335283965, 0.659058035826409, 
    1.0471975511966, 0.869122203007293, 1.0471975511966, 0.869122203007293, 
    0.785398163397448, 1.09681145610345, 0.785398163397448, 0.955316618124509, 
    0.911738290968488, 0.911738290968488, 1.00028587904971, 1.20942920288819, 
    1.15026199151093, 1.00028587904971, 1.20942920288819, 1.09681145610345, 
    1.0471975511966, 0.911738290968488, 0.827113206702756, 1.00028587904971, 
    0.969532110115768, 1.09681145610345, 1.00028587904971, 0.785398163397448, 
    1.09681145610345, 1.09681145610345, 0.869122203007293, 0.743683120092141, 
    0.869122203007293, 0.869122203007293, 1.0471975511966, 1.00028587904971, 
    1.09681145610345, 1.36522739563372, 1.00028587904971, 1.15026199151093, 
    0.869122203007293, 0.570510447745185, 1.20942920288819, 1.0471975511966, 
    0.955316618124509, 0.827113206702756, 1.00028587904971, 1.00028587904971, 
    1.0471975511966, 0.955316618124509, 0.911738290968488, 0.911738290968488, 
    0.570510447745185, 0.869122203007293, 1.00028587904971, 0.869122203007293, 
    0.785398163397448, 0.911738290968488, 0.869122203007293, 
    0.785398163397448, 0.701674123787604, 1.00028587904971, 0.420534335283965, 
    0.570510447745185, 0.969532110115768, 0.869122203007293, 
    0.911738290968488, 1.0471975511966, 0.785398163397448, 0.955316618124509, 
    0.827113206702756, 0.827113206702756, 0.659058035826409, 
    0.955316618124509, 0.701674123787604, 0.785398163397448, 
    0.785398163397448, 1.09681145610345, 1.0471975511966, 0.869122203007293, 
    0.827113206702756, 0.911738290968488, 0.827113206702756, 
    0.785398163397448, 0.827113206702756, 1.00028587904971, 0.911738290968488, 
    1.09681145610345, 0.955316618124509, 0.955316618124509, 1.15026199151093, 
    0.785398163397448, 0.955316618124509, 0.911738290968488, 
    1.0471975511966, 0.869122203007293, 0.869122203007293, 0.911738290968488, 
    0.955316618124509, 0.955316618124509, 0.827113206702756, 
    0.785398163397448, 0.869122203007293, 0.955316618124509, 
    0.684719203002283, 0.827113206702756, 1.00028587904971, 0.785398163397448, 
    0.827113206702756, 1.27795355506632, 1.20942920288819, 1.27795355506632, 
    1.00028587904971, 0.869122203007293, 1.15026199151093, 1.36522739563372, 
    1.27795355506632, 1.5707963267949, 1.5707963267949, 1.5707963267949, 
    1.27795355506632, 1.20942920288819, 0.911738290968488, 0.659058035826409, 
    1.36522739563372, 1.20942920288819, 1.36522739563372, 1.36522739563372, 
    1.27795355506632, 1.20942920288819, 1.0471975511966, 1.15026199151093, 
    1.15026199151093, 0.869122203007293, 1.27795355506632, 1.36522739563372, 
    1.27795355506632, 1.09681145610345, 1.36522739563372, 1.27795355506632, 
    1.00028587904971, 1.27795355506632, 1.15026199151093, 1.00028587904971, 
    1.36522739563372, 1.09681145610345, 1.15026199151093, 1.15026199151093, 
    1.36522739563372, 1.5707963267949, 1.5707963267949, 0.869122203007293, 
    1.09681145610345, 1.20942920288819, 1.36522739563372, 1.27795355506632, 
    1.27795355506632, 1.36522739563372, 1.5707963267949, 1.5707963267949, 
    1.15026199151093, 0.911738290968488, 1.20942920288819, 1.20942920288819, 
    1.28403977458335, 1.20942920288819, 1.36522739563372, 1.27795355506632, 
    1.36522739563372, 1.20942920288819, 0.911738290968488, 1.20942920288819, 
    1.0471975511966, 0.827113206702756, 1.5707963267949, 1.0471975511966, 
    1.0471975511966, 1.15026199151093, 1.27795355506632, 1.15026199151093, 
    1.00028587904971, 1.20942920288819, 0.659058035826409, 0.785398163397448, 
    1.09681145610345, 1.20942920288819, 0.827113206702756, 1.0471975511966, 
    1.20942920288819, 1.5707963267949, 0.955316618124509, 1.0471975511966, 
    1.0471975511966, 0.869122203007293, 1.20942920288819, 1.27795355506632, 
    1.09681145610345, 1.0471975511966, 1.5707963267949, 1.27795355506632, 
    0.869122203007293, 1.00028587904971, 0.911738290968488, 0.911738290968488, 
    1.00028587904971, 1.20942920288819, 1.20942920288819, 1.00028587904971, 
    1.36522739563372, 1.0471975511966, 1.09681145610345, 0.827113206702756, 
    1.15026199151093, 1.09681145610345, 1.27795355506632, 1.36522739563372, 
    1.36522739563372, 1.36522739563372, 1.15026199151093, 1.27795355506632, 
    0.955316618124509, 0.701674123787604, 1.09681145610345, 1.00028587904971, 
    1.20942920288819, 1.20942920288819, 1.20942920288819, 1.00028587904971, 
    1.36522739563372)), .Names = c("Participant", "Coherence", 
"PrimeType", "PrimeDuration", "Condition", "Accuracy"), row.names = c(20L, 
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 49L, 50L, 51L, 52L, 
53L, 54L, 55L, 56L, 57L, 58L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 
85L, 86L, 87L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 
115L, 116L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 
145L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 
194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 223L, 
224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 252L, 253L, 
254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 281L, 282L, 283L, 
284L, 285L, 286L, 287L, 288L, 289L, 290L, 310L, 311L, 312L, 313L, 
314L, 315L, 316L, 317L, 318L, 319L, 339L, 340L, 341L, 342L, 343L, 
344L, 345L, 346L, 347L, 348L, 368L, 369L, 370L, 371L, 372L, 373L, 
374L, 375L, 376L, 377L, 397L, 398L, 399L, 400L, 401L, 402L, 403L, 
404L, 405L, 406L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 
434L, 435L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 
464L, 484L, 485L, 486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 
513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L, 521L, 522L, 542L, 
543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 551L, 571L, 572L, 
573L, 574L, 575L, 576L, 577L, 578L, 579L, 580L, 600L, 601L, 602L, 
603L, 604L, 605L, 606L, 607L, 608L, 609L, 629L, 630L, 631L, 632L, 
633L, 634L, 635L, 636L, 637L, 638L, 658L, 659L, 660L, 661L, 662L, 
663L, 664L, 665L, 666L, 667L, 687L, 688L, 689L, 690L, 691L, 692L, 
693L, 694L, 695L, 696L, 716L, 717L, 718L, 719L, 720L, 721L, 722L, 
723L, 724L, 725L, 745L, 746L, 747L, 748L, 749L, 750L, 751L, 752L, 
753L, 754L, 774L, 775L, 776L, 777L, 778L, 779L, 780L, 781L, 782L, 
783L, 803L, 804L, 805L, 806L, 807L, 808L, 809L, 810L, 811L, 812L, 
832L, 833L, 834L, 835L, 836L, 837L, 838L, 839L, 840L, 841L, 861L, 
862L, 863L, 864L, 865L, 866L, 867L, 868L, 869L, 870L), class = "data.frame")

（注意：这里值得注意的是，在创建可重现代码之前，我将参与者更改为一个因素。这是为了确保aov的输出与类型的输出匹配III ezANOVA。这确实会影响aov的输出，使其与summary.lm()不兼容。但是，使用aov运行重复测量时，这似乎是不可避免的。请参阅{ {3}}对于某些情况。）

然后我在条件中更改因子级别，如下所示：

Cond_Per_Row_stats$Condition <- factor (Cond_Per_Row_stats$Condition, levels = c("P0.0np1200.0", "P0.0np50.0",
                                                                     "P3np1200.0", "P3np50.0",
                                                                     "P35np1200.0", "P35np50.0",
                                                                     "P4np1200.0", "P4np50.0",
                                                                     "P45np1200.0", "P45np50.0",

                                                                     "P0.0tp1200.0", "P0.0tp50.0",
                                                                     "P3tp1200.0", "P3tp50.0",
                                                                     "P35tp1200.0", "P35tp50.0",
                                                                     "P4tp1200.0", "P4tp50.0",
                                                                     "P45tp1200.0", "P45tp50.0",

                                                                     "P0.0fp1200.0", "P0.0fp50.0",
                                                                     "P3fp1200.0", "P3fp50.0",
                                                                     "P35fp1200.0", "P35fp50.0",
                                                                     "P4fp1200.0", "P4fp50.0",
                                                                     "P45fp1200.0", "P45fp50.0"
                                                                 ))
Cond_Per_Row_stats <- Cond_Per_Row_stats[order(Cond_Per_Row_stats$Condition),]

我重复测量aov：

    aovModel <- aov(Accuracy ~ (Coherence * PrimeDuration * PrimeType) + Error(Participant/(Coherence * PrimeDuration * PrimeType)), data = Cond_Per_Row_stats)
    summary(aovModel)

使用此输出：

Error: Participant
          Df Sum Sq Mean Sq F value Pr(>F)
Residuals  9  2.045  0.2272               

Error: Participant:Coherence
          Df Sum Sq Mean Sq F value   Pr(>F)    
Coherence  4  7.800  1.9499    66.3 4.18e-16 ***
Residuals 36  1.059  0.0294                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Error: Participant:PrimeDuration
              Df  Sum Sq Mean Sq F value  Pr(>F)   
PrimeDuration  1 0.10509 0.10509   10.91 0.00918 **
Residuals      9 0.08668 0.00963                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Error: Participant:PrimeType
          Df Sum Sq Mean Sq F value Pr(>F)
PrimeType  2  0.137 0.06850   0.763  0.481
Residuals 18  1.617 0.08981               

Error: Participant:Coherence:PrimeDuration
                        Df Sum Sq Mean Sq F value Pr(>F)  
Coherence:PrimeDuration  4 0.1355 0.03387   2.443 0.0643 .
Residuals               36 0.4992 0.01387                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Error: Participant:Coherence:PrimeType
                    Df Sum Sq Mean Sq F value Pr(>F)
Coherence:PrimeType  8 0.1439 0.01798   1.084  0.384
Residuals           72 1.1943 0.01659               

Error: Participant:PrimeDuration:PrimeType
                        Df Sum Sq Mean Sq F value Pr(>F)
PrimeDuration:PrimeType  2 0.0296 0.01481   0.563  0.579
Residuals               18 0.4733 0.02629               

Error: Participant:Coherence:PrimeDuration:PrimeType
                                  Df Sum Sq Mean Sq F value Pr(>F)
Coherence:PrimeDuration:PrimeType  8 0.0979 0.01223   0.884  0.534
Residuals                         72 0.9965 0.01384

接下来，我试图进行有计划的对比，以及我遇到问题的地方。首先我要使用：

summary.lm(aovModel)

但重复测量模型的输出不兼容：

Error in if (p == 0) { : argument is of length zero

当我只是想要模型的摘要时，这不是一个大问题，我可以使用summary(aovModel)并检查那里的SS，F值等。当我想使用summary.lm()总结计划的对比时，这是一个问题。

例如，正如您从数据框中看到的那样，有30个条件。这是我试图创建计划对比的代码，其中10 np条件是控件，剩余条件在contrast1中与它们进行比较，然后我将tp和fp条件与每个条件进行比较contrast2中的其他人：

contrast1<-c(-20,-20,-20,-20,-20,-20,-20,-20,-20,-20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10)
contrast2<-c(0,0,0,0,0,0,0,0,0,0,-10,-10,-10,-10,-10,-10,-10,-10,-10,-10,10,10,10,10,10,10,10,10,10,10)

contrasts(Cond_Per_Row_stats$Condition)<-cbind(contrast1, contrast2)

Cond_Per_Row_stats$Condition

aovModelContrastCondition <- aov(Accuracy ~ (Coherence * PrimeDuration * PrimeType) + Error(Participant/(Coherence * PrimeDuration * PrimeType)), data = Cond_Per_Row_stats)

summary.lm(aovModelContrastCondition)

此处的summary.lm（）输出会产生与上述相同的错误。

但是，如果我运行以下代码直接调用一个部分：

summary.lm(aovModelContrastCondition$'Participant:Coherence:PrimeDuration:PrimeType')

我得到了这个输出：

Residuals:
     Min       1Q   Median       3Q      Max 
-0.23063 -0.08368 -0.02695  0.06902  0.27561 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)  
CoherenceP3:PrimeDuration50ms:PrimeTypenp   0.15288    0.10522   1.453   0.1506  
CoherenceP35:PrimeDuration50ms:PrimeTypenp  0.13600    0.10522   1.293   0.2003  
CoherenceP4:PrimeDuration50ms:PrimeTypenp   0.07323    0.10522   0.696   0.4887  
CoherenceP45:PrimeDuration50ms:PrimeTypenp  0.09476    0.10522   0.901   0.3708  
CoherenceP3:PrimeDuration50ms:PrimeTypetp   0.10329    0.10522   0.982   0.3296  
CoherenceP35:PrimeDuration50ms:PrimeTypetp  0.22469    0.10522   2.135   0.0361 *
CoherenceP4:PrimeDuration50ms:PrimeTypetp   0.17215    0.10522   1.636   0.1062  
CoherenceP45:PrimeDuration50ms:PrimeTypetp  0.10710    0.10522   1.018   0.3122  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1176 on 72 degrees of freedom
Multiple R-squared:  0.08646,   Adjusted R-squared:  -0.002361 
F-statistic: 0.9734 on 7 and 72 DF,  p-value: 0.4572

基本上我并不完全确定我在这里看到的内容（特别是考虑到我如何设置contrast1和contrast2）。我在主题设计之间看到的计划对比的例子，因此在进行重复测量方差分析时没有解决summary.lm()的问题。

是否有人在使用summary.lm（）重复测量计划对比时有任何经验或诀窍？或者是否有另一种方法可以使用aov在重复测量方差分析中查看计划对比的结果？

提前致谢。

Answer 1

emmeans包可以处理aovlist个对象（和many others）并计算您的自定义对比度。

首先，我们使用正交对比拟合重复测量方差分析。

library("emmeans")
# set orthogonal contrasts
options(contrasts = c("contr.sum", "contr.poly"))

aovModel <- aov(Accuracy ~ Coherence * PrimeDuration * PrimeType + 
                           Error(Participant / (Coherence * PrimeDuration * PrimeType)), 
                data = Cond_Per_Row_stats)

现在，我们创建一个emmGrid对象，并使用emmeans()函数查看估计的边际均值（EMM）。

emm <- emmeans(aovModel, ~ Coherence * PrimeDuration * PrimeType)
emm
## Coherence PrimeDuration PrimeType    emmean         SE    df  lower.CL  upper.CL
## P0.0      1200ms        fp        0.7330383 0.05433093 91.44 0.6251235 0.8409531
## P3        1200ms        fp        0.8654093 0.05433093 91.44 0.7574945 0.9733241
## P35       1200ms        fp        0.9813125 0.05433093 91.44 0.8733977 1.0892273
## P4        1200ms        fp        1.1298314 0.05433093 91.44 1.0219167 1.2377462
## P45       1200ms        fp        1.2569780 0.05433093 91.44 1.1490632 1.3648928
## P0.0      50ms          fp        0.8324380 0.05433093 91.44 0.7245232 0.9403528
## P3        50ms          fp        0.8061391 0.05433093 91.44 0.6982243 0.9140539
## P35       50ms          fp        0.8619138 0.05433093 91.44 0.7539990 0.9698286
## P4        50ms          fp        1.0194414 0.05433093 91.44 0.9115266 1.1273562
## P45       50ms          fp        1.2212040 0.05433093 91.44 1.1132892 1.3291188
## P0.0      1200ms        np        0.7514145 0.05433093 91.44 0.6434997 0.8593293
## P3        1200ms        np        0.8640397 0.05433093 91.44 0.7561249 0.9719545
## P35       1200ms        np        1.0230695 0.05433093 91.44 0.9151547 1.1309843
## P4        1200ms        np        1.1691818 0.05433093 91.44 1.0612670 1.2770966
## P45       1200ms        np        1.1809446 0.05433093 91.44 1.0730298 1.2888594
## P0.0      50ms          np        0.7943392 0.05433093 91.44 0.6864244 0.9022540
## P3        50ms          np        0.9011751 0.05433093 91.44 0.7932603 1.0090898
## P35       50ms          np        0.9831985 0.05433093 91.44 0.8752838 1.0911133
## P4        50ms          np        1.0755496 0.05433093 91.44 0.9676348 1.1834644
## P45       50ms          np        1.1834531 0.05433093 91.44 1.0755383 1.2913679
## P0.0      1200ms        tp        0.8285699 0.05433093 91.44 0.7206552 0.9364847
## P3        1200ms        tp        0.9410529 0.05433093 91.44 0.8331381 1.0489676
## P35       1200ms        tp        0.9957669 0.05433093 91.44 0.8878521 1.1036817
## P4        1200ms        tp        1.1742093 0.05433093 91.44 1.0662945 1.2821241
## P45       1200ms        tp        1.3174114 0.05433093 91.44 1.2094966 1.4253262
## P0.0      50ms          tp        0.7945863 0.05433093 91.44 0.6866715 0.9025010
## P3        50ms          tp        0.8516896 0.05433093 91.44 0.7437749 0.9596044
## P35       50ms          tp        0.9676721 0.05433093 91.44 0.8597573 1.0755868
## P4        50ms          tp        1.1025843 0.05433093 91.44 0.9946695 1.2104990
## P45       50ms          tp        1.2553532 0.05433093 91.44 1.1474384 1.3632680

您的对比等同于以下假设：

考虑所有因素水平及其在emmGrid对象中的顺序，我们可以将该假设等效表达为：

由此我们可以看到contrast1和contrast2所需的对比度权重：

contrast1 <- rep(c(-0.5, 1, -0.5) / 10, each = 10)
contrast2 <- rep(c(-1, 0, 1) / 10, each = 10)

我们现在可以使用contrast()函数来计算您的自定义对比度并获取 p 值。

contrast(emm, list(c1 = contrast1, 
                   c2 = contrast2))
## contrast     estimate         SE df t.ratio p.value
## c1       -0.004193526 0.03670287 18  -0.114  0.9103
## c2        0.052118996 0.04238082 18   1.230  0.2346

如果您只对与因子PrimeType相关的对比度感兴趣，则按如下所示构造emmGrid对象甚至更容易：

emm <- emmeans(aovModel, ~ PrimeType)

这对Coherence和PrimeDuration的水平进行隐式平均（这也由输出指示）。

emm
## PrimeType    emmean         SE    df  lower.CL upper.CL
## fp        0.9707706 0.03682466 21.98 0.8943978 1.047143
## np        0.9926366 0.03682466 21.98 0.9162638 1.069009
## tp        1.0228896 0.03682466 21.98 0.9465168 1.099262
##
## Results are averaged over the levels of: Coherence, PrimeDuration

然后我们可以通过以下方式为contrast1和contrast2指定对比度权重：

contrast1 <- c(-0.5, 1, -0.5)
contrast2 <- c(-1, 0, 1)

结果等于我们使用“更复杂”方法获得的结果。

在R中使用aov时的summary.lm输出

1 个答案: