structure(list(Estimate = c(0.178, 0.177, 0.09, 0.009, -0.054, 
-0.094, -0.16, -0.168, -0.139, -0.141, -0.154, -0.144, -0.1, 
-0.076, -0.115, -0.083, -0.032, 0.018, 0.077, 0.066, 0.07, 0.051, 
0.004, 0.005, -0.046, -0.065, -0.083, -0.083, -0.107, -0.135, 
-0.187, -0.15, -0.138, -0.15, -0.137, -0.125, -0.092, -0.077, 
-0.097, -0.074, -0.075, -0.071, -0.096, -0.111, -0.085, -0.044, 
-0.012, 0.038, 0.073, 0.08, 0.049, 0, -0.012, -0.099, -0.117), 
    StdErr = c(0.106, 0.108, 0.115, 0.101, 0.089, 0.071, 0.087, 
    0.084, 0.072, 0.092, 0.095, 0.09, 0.084, 0.089, 0.088, 0.08, 
    0.087, 0.085, 0.087, 0.082, 0.086, 0.093, 0.09, 0.087, 0.095, 
    0.095, 0.097, 0.102, 0.099, 0.099, 0.105, 0.11, 0.114, 0.109, 
    0.106, 0.104, 0.108, 0.118, 0.112, 0.105, 0.106, 0.108, 0.113, 
    0.115, 0.122, 0.121, 0.124, 0.124, 0.116, 0.114, 0.121, 0.117, 
    0.114, 0.112, 0.104), Statistic = c(1.81, 1.769, 0.824, 0.092, 
    -0.634, -1.401, -1.952, -2.105, -2.016, -1.606, -1.697, -1.693, 
    -1.25, -0.905, -1.402, -1.098, -0.384, 0.227, 0.924, 0.849, 
    0.852, 0.571, 0.05, 0.06, -0.513, -0.724, -0.895, -0.859, 
    -1.14, -1.431, -1.869, -1.438, -1.273, -1.455, -1.365, -1.268, 
    -0.906, -0.693, -0.914, -0.749, -0.751, -0.698, -0.897, -1.013, 
    -0.736, -0.38, -0.104, 0.328, 0.664, 0.735, 0.43, 0, -0.106, 
    -0.932, -1.182), Prob = c(0.088, 0.095, 0.418, 0.928, 0.532, 
    0.175, 0.064, 0.046, 0.055, 0.121, 0.102, 0.103, 0.224, 0.375, 
    0.176, 0.284, 0.704, 0.823, 0.365, 0.404, 0.402, 0.573, 0.96, 
    0.952, 0.613, 0.476, 0.38, 0.399, 0.267, 0.165, 0.074, 0.163, 
    0.215, 0.158, 0.185, 0.217, 0.375, 0.496, 0.371, 0.463, 0.461, 
    0.492, 0.378, 0.321, 0.468, 0.707, 0.918, 0.746, 0.513, 0.469, 
    0.671, 1, 0.916, 0.36, 0.248), DF = c(17.325, 16.731, 23.204, 
    23.527, 23.469, 23.071, 22.064, 23.194, 24.983, 24.731, 24.873, 
    23.738, 23.236, 21.696, 20.531, 22.171, 22.755, 22.397, 24.125, 
    24.652, 24.988, 24.865, 24.748, 24.388, 24.869, 24.232, 23.621, 
    22.401, 21.653, 24.231, 23.794, 24.472, 24.82, 24.578, 24.663, 
    23.486, 21.042, 20.947, 22.055, 19.052, 20.707, 21.942, 23.887, 
    24.475, 24.827, 24.709, 24.779, 24.566, 24.971, 24.954, 24.211, 
    24.712, 24.897, 24.55, 24.523), Time = c(15500, 15600, 15700, 
    15800, 15900, 16000, 16100, 16200, 16300, 16400, 16500, 16600, 
    16700, 16800, 16900, 17000, 17100, 17200, 17300, 17400, 17500, 
    17600, 17700, 17800, 17900, 18000, 18100, 18200, 18300, 18400, 
    18500, 18600, 18700, 18800, 18900, 19000, 19100, 19200, 19300, 
    19400, 19500, 19600, 19700, 19800, 19900, 20000, 20100, 20200, 
    20300, 20400, 20500, 20600, 20700, 20800, 20900), CritStatisticPos = c(2.107, 
    2.112, 2.068, 2.066, 2.066, 2.068, 2.074, 2.068, 2.06, 2.061, 
    2.06, 2.065, 2.067, 2.076, 2.083, 2.073, 2.07, 2.072, 2.063, 
    2.061, 2.06, 2.06, 2.061, 2.062, 2.06, 2.063, 2.066, 2.072, 
    2.076, 2.063, 2.065, 2.062, 2.06, 2.061, 2.061, 2.066, 2.079, 
    2.08, 2.074, 2.093, 2.081, 2.074, 2.064, 2.062, 2.06, 2.061, 
    2.06, 2.061, 2.06, 2.06, 2.063, 2.061, 2.06, 2.061, 2.062
    ), CritStatisticNeg = c(-2.107, -2.112, -2.068, -2.066, -2.066, 
    -2.068, -2.074, -2.068, -2.06, -2.061, -2.06, -2.065, -2.067, 
    -2.076, -2.083, -2.073, -2.07, -2.072, -2.063, -2.061, -2.06, 
    -2.06, -2.061, -2.062, -2.06, -2.063, -2.066, -2.072, -2.076, 
    -2.063, -2.065, -2.062, -2.06, -2.061, -2.061, -2.066, -2.079, 
    -2.08, -2.074, -2.093, -2.081, -2.074, -2.064, -2.062, -2.06, 
    -2.061, -2.06, -2.061, -2.06, -2.06, -2.063, -2.061, -2.06, 
    -2.061, -2.062), PositiveRuns = c(NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_), NegativeRuns = c(NA, 
    NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA)), .Names = c("Estimate", 
"StdErr", "Statistic", "Prob", "DF", "Time", "CritStatisticPos", 
"CritStatisticNeg", "PositiveRuns", "NegativeRuns"), eyetrackingR = structure(list(
    data_options = structure(list(participant_column = "ParticipantName", 
        trackloss_column = "TrackLoss", time_column = "TimeFromTrialOnset", 
        trial_column = "Trial", item_columns = NULL, aoi_columns = c("Animate", 
        "Inanimate"), treat_non_aoi_looks_as_missing = TRUE), .Names = c("participant_column", 
    "trackloss_column", "time_column", "trial_column", "item_columns", 
    "aoi_columns", "treat_non_aoi_looks_as_missing")), summarized_by = "ParticipantName", 
    time_bin_size = 100, formula = Prop ~ Sex, p_adjust_method = "none", 
    alpha = 0.05, threshold = NULL, test = "t.test", predictor = "Sex", 
    positive_runs = list(), negative_runs = list(structure(list(
        start_time = 16200, stop_time = 16300), .Names = c("start_time", 
    "stop_time")))), .Names = c("data_options", "summarized_by", 
"time_bin_size", "formula", "p_adjust_method", "alpha", "threshold", 
"test", "predictor", "positive_runs", "negative_runs")), row.names = c(NA, 
55L), class = c("bin_analysis", "data.frame"))
