I ran a lmer in 10 imputed data. Some predictors are categorical, so I need to get the overall p-value from anova or LRT. How to do that?
mice.fit <- lapply(imputed_ITT,
function(x)lmer(y ~ y0 + group*time + (1|ID),
REML=T, data=x))
mi.anova did not work
mi.anova(imputed_ITT,lmer(y ~ y0 + group*time + (1|ID), type=3))
I tried anova listwise but I dont know how to the pool the p-values.
lrt <- lapply(mice.fit, function(x)Anova(x, type=3))
I also tried using micombine.chisquare() for each predictor where dk is the vector of anova chi-sq test statistics from 10 imputed data. Surprisingly, for the same predictor, 5 out of 10 p-values were <0.05, the rest around 0.06-0.3, but the pooled p-value was 0.31. I wonder if it's correct.
micombine.chisquare(dk, df=df)
Example data: (Here are 3 of the 10 imputed data)
imputed_ITT <- list(sub1, sub2, sub3)
> dput(sub1)
structure(list(ID = c("L1", "L10", "L11", "L12", "L13", "L14",
"L15", "L16", "L17", "L18", "L19", "L2", "L20", "L21", "L22",
"L23", "L24", "L25", "L26", "L27", "L28", "L29", "L3", "L30",
"L31", "L32", "L33", "L34", "L35", "L36", "L37", "L38", "L39",
"L4", "L40", "L41", "L42", "L43", "L44", "L45", "L46", "L47",
"L48", "L49", "L5", "L50", "L51", "L52", "L6", "L7", "L8", "L9",
"M1", "M10", "M11", "M12", "M13", "M14", "M15", "M16", "M17",
"M18", "M19", "M2", "M21", "M22", "M23", "M24", "M25", "M26",
"M27", "M29", "M3", "M30", "M31", "M32", "M33", "M35", "M36",
"M37", "M38", "M39", "M4", "M40", "M41", "M42", "M43", "M44",
"M45", "M46", "M47", "M48", "M49", "M5", "M50", "M51", "M52",
"M53", "M6", "M7", "M8", "M9", "L1", "L10", "L11", "L12", "L13",
"L14", "L15", "L16", "L17", "L18", "L19", "L2", "L20", "L21",
"L22", "L23", "L24", "L25", "L26", "L27", "L28", "L29", "L3",
"L30", "L31", "L32", "L33", "L34", "L35", "L36", "L37", "L38",
"L39", "L4", "L40", "L41", "L42", "L43", "L44", "L45", "L46",
"L47", "L48", "L49", "L5", "L50", "L51", "L52", "L6", "L7", "L8",
"L9", "M1", "M10", "M11", "M12", "M13", "M14", "M15", "M16",
"M17", "M18", "M19", "M2", "M21", "M22", "M23", "M24", "M25",
"M26", "M27", "M29", "M3", "M30", "M31", "M32", "M33", "M35",
"M36", "M37", "M38", "M39", "M4", "M40", "M41", "M42", "M43",
"M44", "M45", "M46", "M47", "M48", "M49", "M5", "M50", "M51",
"M52", "M53", "M6", "M7", "M8", "M9"), time = 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, 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, 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, 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), .Label = c("6",
"12"), class = "factor"), group = structure(c(1L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 1L,
2L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 3L, 1L, 2L, 1L, 1L, 1L), .Label = c("0", "1", "2"), class = "factor"),
y = c(1.246, 0.941, 0.845, 0.899, 1.023, 1.131, 0.986, 0.999,
1.062, 1.173, 0.925, 0.962, 1.008, 1.045, 0.917, 0.933, 1.026,
0.933, 1.008, 1.019, 1.045, 1.092, 0.942, 1.052, 1.121, 1.138,
0.959, 1.087, 0.937, 0.928, 0.986, 0.912, 1.154, 0.903, 0.983,
1.031, 0.937, 1.096, 1.004, 0.949, 1.149, 1.062, 1.138, 0.972,
1.033, 0.951, 1.019, 1.085, 0.979, 1.121, 1.092, 1.041, 1.094,
0.954, 0.95, 1.036, 0.986, 1.012, 1.004, 1.079, 0.937, 1.058,
1.061, 0.906, 1.192, 1.05, 1.079, 1.011, 1.092, 1.054, 1.032,
0.947, 0.933, 1.02, 0.953, 0.937, 0.973, 1.056, 0.991, 1.023,
0.92, 0.926, 1.024, 0.927, 1.096, 1.012, 1.036, 0.999, 1.115,
0.924, 1.031, 1.012, 0.962, 0.906, 1.031, 1.02, 0.93, 1.008,
0.959, 0.878, 0.924, 1.046, NA, 0.953, 0.895, NA, 1.032,
1.065, 1.032, 0.822, NA, NA, 0.959, 1.092, NA, NA, 0.895,
0.941, 1.029, 0.891, 1.037, 1.011, 1.016, 1.108, 1.104, 1.118,
NA, 1.092, 1.13, 0.973, 1.117, 0.97, 0.974, 0.953, 1.027,
0.912, 0.924, 1.056, 0.97, 0.966, 1.011, 1.096, 1.149, 1.133,
1.008, 0.954, 0.965, 0.907, 1.002, 1.09, 0.986, 1.117, 1.092,
NA, 1.115, 0.95, 0.937, NA, 0.983, 0.982, 0.833, 0.983, 0.949,
0.891, 1.053, 0.874, 1.154, 0.978, 0.886, 1.031, NA, NA,
0.974, 1.05, NA, 0.937, 1.027, NA, 1.079, 1.04, 0.979, 1.007,
1.046, 0.887, 1.049, NA, 1.11, 1.102, 0.974, 1.04, 1.217,
0.824, NA, 0.986, 0.892, NA, 0.995, 0.962, 0.912, 1.069,
NA, 0.892, 0.974, 1.05), y0 = c(1.173, 0.916, 0.797, 0.962,
0.928, 1.029, 0.949, 0.793, 1.062, 1.153, 0.922, 0.994, 0.921,
1.109, 0.904, 0.998, 0.853, 0.92, 1.003, 1.04, 1.036, 1.113,
0.974, 1.025, 1.134, 1.049, 1.005, 1.016, 1.1, 0.918, 0.965,
0.998, 1.125, 0.994, 0.97, 1.071, 1.092, 1.04, 0.874, 0.947,
1.112, 1.134, 1.087, 0.913, 0.999, 0.891, 1.079, 1.101, 1.046,
1.066, 1.092, 0.995, 1.024, 0.9, 1.078, 0.985, 0.986, 1.043,
0.916, 1.062, 0.994, 0.895, 1.009, 0.945, 1.173, 0.861, 1.138,
1.079, 1.035, 0.962, 1.075, 1.019, 0.928, 1.028, 1.143, 0.996,
1.007, 1.052, 1.104, 1.002, 1.019, 0.932, 0.98, 0.914, 1.125,
1.163, 1.044, 1.045, 1.102, 0.953, 1.222, 1.138, 0.995, 0.937,
1.112, 1.075, 1.002, 0.928, 0.856, 0.903, 1.032, 1.057, 1.173,
0.916, 0.797, 0.962, 0.928, 1.029, 0.949, 0.793, 1.062, 1.153,
0.922, 0.994, 0.921, 1.109, 0.904, 0.998, 0.853, 0.92, 1.003,
1.04, 1.036, 1.113, 0.974, 1.025, 1.134, 1.049, 1.005, 1.016,
1.1, 0.918, 0.965, 0.998, 1.125, 0.994, 0.97, 1.071, 1.092,
1.04, 0.874, 0.947, 1.112, 1.134, 1.087, 0.913, 0.999, 0.891,
1.079, 1.101, 1.046, 1.066, 1.092, 0.995, 1.024, 0.9, 1.078,
0.985, 0.986, 1.043, 0.916, 1.062, 0.994, 0.895, 1.009, 0.945,
1.173, 0.861, 1.138, 1.079, 1.035, 0.962, 1.075, 1.019, 0.928,
1.028, 1.143, 0.996, 1.007, 1.052, 1.104, 1.002, 1.019, 0.932,
0.98, 0.914, 1.125, 1.163, 1.044, 1.045, 1.102, 0.953, 1.222,
1.138, 0.995, 0.937, 1.112, 1.075, 1.002, 0.928, 0.856, 0.903,
1.032, 1.057)), row.names = c(NA, -204L), class = "data.frame")
> dput(sub2)
structure(list(ID = c("L1", "L10", "L11", "L12", "L13", "L14",
"L15", "L16", "L17", "L18", "L19", "L2", "L20", "L21", "L22",
"L23", "L24", "L25", "L26", "L27", "L28", "L29", "L3", "L30",
"L31", "L32", "L33", "L34", "L35", "L36", "L37", "L38", "L39",
"L4", "L40", "L41", "L42", "L43", "L44", "L45", "L46", "L47",
"L48", "L49", "L5", "L50", "L51", "L52", "L6", "L7", "L8", "L9",
"M1", "M10", "M11", "M12", "M13", "M14", "M15", "M16", "M17",
"M18", "M19", "M2", "M21", "M22", "M23", "M24", "M25", "M26",
"M27", "M29", "M3", "M30", "M31", "M32", "M33", "M35", "M36",
"M37", "M38", "M39", "M4", "M40", "M41", "M42", "M43", "M44",
"M45", "M46", "M47", "M48", "M49", "M5", "M50", "M51", "M52",
"M53", "M6", "M7", "M8", "M9", "L1", "L10", "L11", "L12", "L13",
"L14", "L15", "L16", "L17", "L18", "L19", "L2", "L20", "L21",
"L22", "L23", "L24", "L25", "L26", "L27", "L28", "L29", "L3",
"L30", "L31", "L32", "L33", "L34", "L35", "L36", "L37", "L38",
"L39", "L4", "L40", "L41", "L42", "L43", "L44", "L45", "L46",
"L47", "L48", "L49", "L5", "L50", "L51", "L52", "L6", "L7", "L8",
"L9", "M1", "M10", "M11", "M12", "M13", "M14", "M15", "M16",
"M17", "M18", "M19", "M2", "M21", "M22", "M23", "M24", "M25",
"M26", "M27", "M29", "M3", "M30", "M31", "M32", "M33", "M35",
"M36", "M37", "M38", "M39", "M4", "M40", "M41", "M42", "M43",
"M44", "M45", "M46", "M47", "M48", "M49", "M5", "M50", "M51",
"M52", "M53", "M6", "M7", "M8", "M9"), time = 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, 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, 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, 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), .Label = c("6",
"12"), class = "factor"), group = structure(c(1L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 1L,
2L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 3L, 1L, 1L, 1L, 1L, 1L), .Label = c("0", "1", "2"), class = "factor"),
y = c(1.246, 0.941, 0.845, 0.899, 1.023, 1.131, 0.986, 0.999,
1.062, 1.173, 0.925, 0.962, 1.008, 1.045, 0.917, 0.933, 1.026,
0.933, 1.008, 1.019, 1.045, 1.092, 0.942, 1.052, 1.121, 1.138,
0.959, 1.087, 0.937, 0.928, 0.986, 0.912, 1.154, 0.903, 0.983,
1.031, 0.937, 1.096, 1.004, 0.949, 1.149, 1.062, 1.138, 0.972,
1.033, 0.951, 1.019, 1.085, 0.979, 1.121, 1.092, 1.041, 1.094,
0.954, 0.95, 1.033, 0.986, 1.012, 1.004, 1.079, 0.937, 1.058,
1.061, 0.906, 1.192, 1.05, 1.079, 1.011, 1.079, 1.054, 1.032,
0.947, 0.983, 1.02, 0.953, 0.937, 0.973, 1.056, 0.991, 1.023,
0.92, 0.926, 1.024, 0.927, 1.096, 1.012, 1.036, 0.999, 1.115,
0.924, 0.912, 1.012, 0.962, 0.925, 1.031, 1.02, 0.93, 1.008,
0.962, 0.878, 0.924, 1.046, NA, 0.953, 0.895, NA, 1.032,
1.065, 1.032, 0.822, NA, NA, 0.959, 1.092, NA, NA, 0.895,
0.941, 1.029, 0.891, 1.037, 1.011, 1.016, 1.108, 1.104, 1.118,
NA, 1.092, 1.13, 0.973, 1.117, 0.97, 0.974, 0.953, 1.027,
0.912, 0.924, 1.056, 0.97, 0.966, 1.011, 1.096, 1.149, 1.133,
1.008, 0.954, 0.965, 0.907, 1.002, 1.09, 0.986, 1.117, 1.092,
NA, 1.115, 0.95, 0.937, NA, 0.983, 0.982, 0.833, 0.983, 0.949,
0.891, 1.053, 0.874, 1.154, 0.978, 0.886, 1.031, NA, NA,
0.974, 1.05, NA, 0.937, 1.027, NA, 1.079, 1.04, 0.979, 1.007,
1.046, 0.887, 1.049, NA, 1.11, 1.102, 0.974, 1.04, 1.217,
0.824, NA, 0.986, 0.892, NA, 0.995, 0.962, 0.912, 1.069,
NA, 0.892, 0.974, 1.05), y0 = c(1.173, 0.916, 0.797, 0.962,
0.928, 1.029, 0.949, 0.793, 1.062, 1.153, 0.922, 0.994, 0.921,
1.109, 0.904, 0.998, 0.853, 0.92, 1.003, 1.04, 1.036, 1.113,
0.974, 1.025, 1.134, 1.049, 1.005, 1.016, 1.1, 0.918, 0.965,
0.998, 1.125, 0.994, 0.97, 1.071, 1.092, 1.04, 0.874, 0.947,
1.112, 1.134, 1.087, 0.913, 0.999, 0.891, 1.079, 1.101, 1.046,
1.066, 1.092, 0.995, 1.024, 0.9, 1.078, 0.985, 0.986, 1.043,
0.916, 1.062, 0.994, 0.895, 1.009, 0.945, 1.173, 0.861, 1.138,
1.079, 1.035, 0.962, 1.075, 1.019, 0.928, 1.028, 1.143, 0.996,
1.007, 1.052, 1.104, 1.002, 1.019, 0.932, 0.98, 0.914, 1.125,
1.163, 1.044, 1.045, 1.102, 0.953, 1.222, 1.138, 0.995, 0.937,
1.112, 1.075, 1.002, 0.928, 0.856, 0.903, 1.032, 1.057, 1.173,
0.916, 0.797, 0.962, 0.928, 1.029, 0.949, 0.793, 1.062, 1.153,
0.922, 0.994, 0.921, 1.109, 0.904, 0.998, 0.853, 0.92, 1.003,
1.04, 1.036, 1.113, 0.974, 1.025, 1.134, 1.049, 1.005, 1.016,
1.1, 0.918, 0.965, 0.998, 1.125, 0.994, 0.97, 1.071, 1.092,
1.04, 0.874, 0.947, 1.112, 1.134, 1.087, 0.913, 0.999, 0.891,
1.079, 1.101, 1.046, 1.066, 1.092, 0.995, 1.024, 0.9, 1.078,
0.985, 0.986, 1.043, 0.916, 1.062, 0.994, 0.895, 1.009, 0.945,
1.173, 0.861, 1.138, 1.079, 1.035, 0.962, 1.075, 1.019, 0.928,
1.028, 1.143, 0.996, 1.007, 1.052, 1.104, 1.002, 1.019, 0.932,
0.98, 0.914, 1.125, 1.163, 1.044, 1.045, 1.102, 0.953, 1.222,
1.138, 0.995, 0.937, 1.112, 1.075, 1.002, 0.928, 0.856, 0.903,
1.032, 1.057)), row.names = c(NA, -204L), class = "data.frame")
> dput(sub3)
structure(list(ID = c("L1", "L10", "L11", "L12", "L13", "L14",
"L15", "L16", "L17", "L18", "L19", "L2", "L20", "L21", "L22",
"L23", "L24", "L25", "L26", "L27", "L28", "L29", "L3", "L30",
"L31", "L32", "L33", "L34", "L35", "L36", "L37", "L38", "L39",
"L4", "L40", "L41", "L42", "L43", "L44", "L45", "L46", "L47",
"L48", "L49", "L5", "L50", "L51", "L52", "L6", "L7", "L8", "L9",
"M1", "M10", "M11", "M12", "M13", "M14", "M15", "M16", "M17",
"M18", "M19", "M2", "M21", "M22", "M23", "M24", "M25", "M26",
"M27", "M29", "M3", "M30", "M31", "M32", "M33", "M35", "M36",
"M37", "M38", "M39", "M4", "M40", "M41", "M42", "M43", "M44",
"M45", "M46", "M47", "M48", "M49", "M5", "M50", "M51", "M52",
"M53", "M6", "M7", "M8", "M9", "L1", "L10", "L11", "L12", "L13",
"L14", "L15", "L16", "L17", "L18", "L19", "L2", "L20", "L21",
"L22", "L23", "L24", "L25", "L26", "L27", "L28", "L29", "L3",
"L30", "L31", "L32", "L33", "L34", "L35", "L36", "L37", "L38",
"L39", "L4", "L40", "L41", "L42", "L43", "L44", "L45", "L46",
"L47", "L48", "L49", "L5", "L50", "L51", "L52", "L6", "L7", "L8",
"L9", "M1", "M10", "M11", "M12", "M13", "M14", "M15", "M16",
"M17", "M18", "M19", "M2", "M21", "M22", "M23", "M24", "M25",
"M26", "M27", "M29", "M3", "M30", "M31", "M32", "M33", "M35",
"M36", "M37", "M38", "M39", "M4", "M40", "M41", "M42", "M43",
"M44", "M45", "M46", "M47", "M48", "M49", "M5", "M50", "M51",
"M52", "M53", "M6", "M7", "M8", "M9"), time = 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, 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, 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, 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), .Label = c("6",
"12"), class = "factor"), group = structure(c(1L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 1L,
2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 3L, 1L, 2L, 1L, 1L, 1L), .Label = c("0", "1", "2"), class = "factor"),
y = c(1.246, 0.941, 0.845, 0.899, 1.023, 1.131, 0.986, 0.999,
1.062, 1.173, 0.925, 0.962, 1.008, 1.045, 0.917, 0.933, 1.026,
0.933, 1.008, 1.019, 1.045, 1.092, 0.942, 1.052, 1.121, 1.138,
0.959, 1.087, 0.937, 0.928, 0.986, 0.912, 1.154, 0.903, 0.983,
1.031, 0.937, 1.096, 1.004, 0.949, 1.149, 1.062, 1.138, 0.972,
1.033, 0.951, 1.019, 1.085, 0.979, 1.121, 1.092, 1.041, 1.094,
0.954, 0.95, 1.033, 0.986, 1.012, 1.004, 1.079, 0.937, 1.058,
1.061, 0.906, 1.192, 1.05, 1.079, 1.011, 1.012, 1.054, 1.032,
0.947, 1.149, 1.02, 0.953, 0.937, 0.973, 1.056, 0.991, 1.023,
0.92, 0.926, 1.024, 0.927, 1.096, 1.012, 1.036, 0.999, 1.115,
0.924, 1.062, 1.012, 0.962, 0.986, 1.031, 1.02, 0.93, 1.008,
0.953, 0.878, 0.924, 1.046, NA, 0.953, 0.895, NA, 1.032,
1.065, 1.032, 0.822, NA, NA, 0.959, 1.092, NA, NA, 0.895,
0.941, 1.029, 0.891, 1.037, 1.011, 1.016, 1.108, 1.104, 1.118,
NA, 1.092, 1.13, 0.973, 1.117, 0.97, 0.974, 0.953, 1.027,
0.912, 0.924, 1.056, 0.97, 0.966, 1.011, 1.096, 1.149, 1.133,
1.008, 0.954, 0.965, 0.907, 1.002, 1.09, 0.986, 1.117, 1.092,
NA, 1.115, 0.95, 0.937, NA, 0.983, 0.982, 0.833, 0.983, 0.949,
0.891, 1.053, 0.874, 1.154, 0.978, 0.886, 1.031, NA, NA,
0.974, 1.05, NA, 0.937, 1.027, NA, 1.079, 1.04, 0.979, 1.007,
1.046, 0.887, 1.049, NA, 1.11, 1.102, 0.974, 1.04, 1.217,
0.824, NA, 0.986, 0.892, NA, 0.995, 0.962, 0.912, 1.069,
NA, 0.892, 0.974, 1.05), y0 = c(1.173, 0.916, 0.797, 0.962,
0.928, 1.029, 0.949, 0.793, 1.062, 1.153, 0.922, 0.994, 0.921,
1.109, 0.904, 0.998, 0.853, 0.92, 1.003, 1.04, 1.036, 1.113,
0.974, 1.025, 1.134, 1.049, 1.005, 1.016, 1.1, 0.918, 0.965,
0.998, 1.125, 0.994, 0.97, 1.071, 1.092, 1.04, 0.874, 0.947,
1.112, 1.134, 1.087, 0.913, 0.999, 0.891, 1.079, 1.101, 1.046,
1.066, 1.092, 0.995, 1.024, 0.9, 1.078, 0.985, 0.986, 1.043,
0.916, 1.062, 0.994, 0.895, 1.009, 0.945, 1.173, 0.861, 1.138,
1.079, 1.035, 0.962, 1.075, 1.019, 0.928, 1.028, 1.143, 0.996,
1.007, 1.052, 1.104, 1.002, 1.019, 0.932, 0.98, 0.914, 1.125,
1.163, 1.044, 1.045, 1.102, 0.953, 1.222, 1.138, 0.995, 0.937,
1.112, 1.075, 1.002, 0.928, 0.856, 0.903, 1.032, 1.057, 1.173,
0.916, 0.797, 0.962, 0.928, 1.029, 0.949, 0.793, 1.062, 1.153,
0.922, 0.994, 0.921, 1.109, 0.904, 0.998, 0.853, 0.92, 1.003,
1.04, 1.036, 1.113, 0.974, 1.025, 1.134, 1.049, 1.005, 1.016,
1.1, 0.918, 0.965, 0.998, 1.125, 0.994, 0.97, 1.071, 1.092,
1.04, 0.874, 0.947, 1.112, 1.134, 1.087, 0.913, 0.999, 0.891,
1.079, 1.101, 1.046, 1.066, 1.092, 0.995, 1.024, 0.9, 1.078,
0.985, 0.986, 1.043, 0.916, 1.062, 0.994, 0.895, 1.009, 0.945,
1.173, 0.861, 1.138, 1.079, 1.035, 0.962, 1.075, 1.019, 0.928,
1.028, 1.143, 0.996, 1.007, 1.052, 1.104, 1.002, 1.019, 0.932,
0.98, 0.914, 1.125, 1.163, 1.044, 1.045, 1.102, 0.953, 1.222,
1.138, 0.995, 0.937, 1.112, 1.075, 1.002, 0.928, 0.856, 0.903,
1.032, 1.057)), row.names = c(NA, -204L), class = "data.frame")