在R中使用aov时的summary.lm输出
相关赏金: 我有一个关于在R中使用aov时的summary.lm输出,r,comparison,anova,R,Comparison,Anova,相关赏金: 我有一个关于summary.lm()输出的问题 首先,这里是我的数据集的可复制代码: 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,
summary.lm()
输出的问题
首先,这里是我的数据集的可复制代码:
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,
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862L, 863L, 864L, 865L, 866L, 867L, 868L, 869L, 870L), class = "data.frame")
我运行了一个重复测量aov:
aovModel <- aov(Accuracy ~ (Coherence * PrimeDuration * PrimeType) + Error(Participant/(Coherence * PrimeDuration * PrimeType)), data = Cond_Per_Row_stats)
summary(aovModel)
接下来,我尝试进行有计划的对比,这就是我遇到问题的地方。首先,我想使用:
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
这不是一个大问题,当我只需要模型的摘要时,我可以使用summary(aovModel)
检查SS、F值等。当我想使用summary.lm()
总结计划对比时,这是一个问题
例如,从dataframe中可以看到有30个条件。这是我试图创建计划对比的代码,其中10个np条件为控制,其余条件在contrast1
中与它们进行比较,然后我在contrast2
中比较tp和fp条件:
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)
我得到这个输出:
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
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,我不完全确定我在这里看到了什么(特别是考虑到我如何设置contrast1
和contrast2
)。我所看到的受试者设计之间的计划对比示例,因此在进行重复测量方差分析时,没有使用summary.lm()
解决该问题
在将summary.lm()用于重复测量计划对比时,是否有人有任何经验或诀窍?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
提前感谢。emmeans包可以处理
aovlist
对象(和),并计算自定义对比度
首先,我们使用正交对比法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)
你的对比等同于以下假设:
考虑到emmGrid
对象中的所有因素水平及其顺序,我们可以将这些假设等价地表示为:
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)
如果您只对与因子
PrimeType
相关的对比感兴趣,则更容易构建emmGrid
对象,如下所示:
emm <- emmeans(aovModel, ~ PrimeType)
然后,我们可以通过以下方式指定contrast1
和contrast2
的对比度权重:
contrast1 <- c(-0.5, 1, -0.5)
contrast2 <- c(-1, 0, 1)
1summary.lm
是lm
对象的摘要方法。如果您确实想使用它,请使用lm
函数而不是anova
<在本例中,code>aovModel
不会继承lm
类,因为您使用的是Error
函数。我不确定Error
实际上做了什么,但解决问题的一种方法是找到一种方法,用lm
中可用的东西替换Error
。后者应该是可能的,因为aov
只是lm
@coffeinjunky的包装器。非常感谢您的建议。我最近问了一个关于在计划对比中使用ezANOVA输出(也可以输出aov)的问题&这很快就会给它带来好处。我怀疑一定有一种方法可以使用与计划对比度同时生成的数据。您介意稍微扩展一下计划对比度是什么吗?许多不同的领域使用不同的术语,我以前从未听说过这个名称,但如果我能更好地理解它的含义,也许我能帮上忙。@coffeinjunky计划对比(又名计划比较)是一种先验测试,只测试特定的成对比较,与测试所有可能组合的事后测试相比。它们基于对假设的预测,因此是有针对性的比较。正交对比是首选方法。这是我发现的一点理论背景:那么,计划对比基本上是指选定组的组平均差异?这就是为什么要使用summary.lm
,因为它更直接地给出了单个组与总体平均值的差异?
contrast1 <- rep(c(-0.5, 1, -0.5) / 10, each = 10)
contrast2 <- rep(c(-1, 0, 1) / 10, each = 10)
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
emm <- emmeans(aovModel, ~ PrimeType)
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
contrast1 <- c(-0.5, 1, -0.5)
contrast2 <- c(-1, 0, 1)