R: Generate the cumulative difference-plot

cumulative_difference_plot {RVCompare}

R Documentation

Generate the cumulative difference-plot

Description

Generate the cumulative difference-plot given the observed samples using the bootstrap method.

Usage

cumulative_difference_plot(
  X_A_observed,
  X_B_observed,
  isMinimizationProblem,
  labelA = "X_A",
  labelB = "X_B",
  alpha = 0.05,
  EPSILON = 1e-20,
  nOfBootstrapSamples = 1000,
  ignoreMinimumLengthCheck = FALSE
)

Arguments

`X_A_observed`	array of the observed samples (real values) of X_A.
`X_B_observed`	array of the observed samples (real values) of X_B, it needs to have the same length as X_A.
`isMinimizationProblem`	a boolean value where TRUE represents that lower values are preferred to larger values.
`labelA`	(optional, default value "X_A") the label corresponding to X_A.
`labelB`	(optional, default value "X_B") the label corresponding to X_B.
`alpha`	(optional, default value 0.05) the error of the confidence interval. If alpha = 0.05 then we have 95 percent confidence interval.
`EPSILON`	(optional, default value 1e-20) minimum difference between two values to be considered different.
`nOfBootstrapSamples`	(optional, default value 1e3) how many bootstrap samples to average. Increases computation time.
`ignoreMinimumLengthCheck`	(optional, default value FALSE) whether to skip the check for a minimum length of 100 in X_A_observed and X_A_observed.

Value

retunrs and shows the cumulative difference-plot

Examples


### Example 1 ###
X_A_observed <- rnorm(100, mean = 2, sd = 1)
X_B_observed <- rnorm(100, mean = 2.1, sd = 0.5)

 cumulative_difference_plot(X_A_observed, X_B_observed, TRUE, labelA="X_A", labelB="X_B")



### Example 2 ###
# Comparing the optimization algorithms PL-EDA and PL-GS
# with 400 samples each.
PL_EDA_fitness <- c(
52235, 52485, 52542, 52556, 52558, 52520, 52508, 52491, 52474, 52524,
52414, 52428, 52413, 52457, 52437, 52449, 52534, 52531, 52476, 52434,
52492, 52554, 52520, 52500, 52342, 52520, 52392, 52478, 52422, 52469,
52421, 52386, 52373, 52230, 52504, 52445, 52378, 52554, 52475, 52528,
52508, 52222, 52416, 52492, 52538, 52192, 52416, 52213, 52478, 52496,
52444, 52524, 52501, 52495, 52415, 52151, 52440, 52390, 52428, 52438,
52475, 52177, 52512, 52530, 52493, 52424, 52201, 52484, 52389, 52334,
52548, 52560, 52536, 52467, 52392, 51327, 52506, 52473, 52087, 52502,
52533, 52523, 52485, 52535, 52502, 52577, 52508, 52463, 52530, 52507,
52472, 52400, 52511, 52528, 52532, 52526, 52421, 52442, 52532, 52505,
52531, 52644, 52513, 52507, 52444, 52471, 52474, 52426, 52526, 52564,
52512, 52521, 52533, 52511, 52416, 52414, 52425, 52457, 52522, 52508,
52481, 52439, 52402, 52442, 52512, 52377, 52412, 52432, 52506, 52524,
52488, 52494, 52531, 52471, 52616, 52482, 52499, 52386, 52492, 52484,
52537, 52517, 52536, 52449, 52439, 52410, 52417, 52402, 52406, 52217,
52484, 52418, 52550, 52513, 52530, 51667, 52185, 52089, 51853, 52511,
52051, 52584, 52475, 52447, 52390, 52506, 52514, 52452, 52526, 52502,
52422, 52411, 52171, 52437, 52323, 52488, 52546, 52505, 52563, 52457,
52502, 52503, 52126, 52537, 52435, 52419, 52300, 52481, 52419, 52540,
52566, 52547, 52476, 52448, 52474, 52438, 52430, 52363, 52484, 52455,
52420, 52385, 52152, 52505, 52457, 52473, 52503, 52507, 52429, 52513,
52433, 52538, 52416, 52479, 52501, 52485, 52429, 52395, 52503, 52195,
52380, 52487, 52498, 52421, 52137, 52493, 52403, 52511, 52409, 52479,
52400, 52498, 52482, 52440, 52541, 52499, 52476, 52485, 52294, 52408,
52426, 52464, 52535, 52512, 52516, 52531, 52449, 52507, 52485, 52491,
52499, 52414, 52403, 52398, 52548, 52536, 52410, 52549, 52454, 52534,
52468, 52483, 52239, 52502, 52525, 52328, 52467, 52217, 52543, 52391,
52524, 52474, 52509, 52496, 52432, 52532, 52493, 52503, 52508, 52422,
52459, 52477, 52521, 52515, 52469, 52416, 52249, 52537, 52494, 52393,
52057, 52513, 52452, 52458, 52518, 52520, 52524, 52531, 52439, 52530,
52422, 52649, 52481, 52256, 52428, 52425, 52458, 52488, 52502, 52373,
52426, 52441, 52471, 52468, 52465, 52265, 52455, 52501, 52340, 52457,
52275, 52527, 52574, 52474, 52487, 52416, 52634, 52514, 52184, 52430,
52462, 52392, 52529, 52178, 52495, 52438, 52539, 52430, 52459, 52312,
52437, 52637, 52511, 52563, 52270, 52341, 52436, 52515, 52480, 52569,
52490, 52453, 52422, 52443, 52419, 52512, 52447, 52425, 52509, 52180,
52521, 52566, 52060, 52425, 52480, 52454, 52501, 52536, 52143, 52432,
52451, 52548, 52508, 52561, 52515, 52502, 52468, 52373, 52511, 52516,
52195, 52499, 52534, 52453, 52449, 52431, 52473, 52553, 52444, 52459,
52536, 52413, 52537, 52537, 52501, 52425, 52507, 52525, 52452, 52499
)
PL_GS_fitness <- c(
52476, 52211, 52493, 52484, 52499, 52500, 52476, 52483, 52431, 52483,
52515, 52493, 52490, 52464, 52478, 52440, 52482, 52498, 52460, 52219,
52444, 52479, 52498, 52481, 52490, 52470, 52498, 52521, 52452, 52494,
52451, 52429, 52248, 52525, 52513, 52489, 52448, 52157, 52449, 52447,
52476, 52535, 52464, 52453, 52493, 52438, 52489, 52462, 52219, 52223,
52514, 52476, 52495, 52496, 52502, 52538, 52491, 52457, 52471, 52531,
52488, 52441, 52467, 52483, 52476, 52494, 52485, 52507, 52224, 52464,
52503, 52495, 52518, 52490, 52508, 52505, 52214, 52506, 52507, 52207,
52531, 52492, 52515, 52497, 52476, 52490, 52436, 52495, 52437, 52494,
52513, 52483, 52522, 52496, 52196, 52525, 52490, 52506, 52498, 52250,
52524, 52469, 52497, 52519, 52437, 52481, 52237, 52436, 52508, 52518,
52490, 52501, 52508, 52476, 52520, 52435, 52463, 52481, 52486, 52489,
52482, 52496, 52499, 52443, 52497, 52464, 52514, 52476, 52498, 52496,
52498, 52530, 52203, 52482, 52441, 52493, 52532, 52518, 52474, 52498,
52512, 52226, 52538, 52477, 52508, 52243, 52533, 52463, 52440, 52246,
52209, 52488, 52530, 52195, 52487, 52494, 52508, 52505, 52444, 52515,
52499, 52428, 52498, 52244, 52520, 52463, 52187, 52484, 52517, 52504,
52511, 52530, 52519, 52514, 52532, 52203, 52485, 52439, 52496, 52443,
52503, 52520, 52516, 52478, 52473, 52505, 52480, 52196, 52492, 52527,
52490, 52493, 52252, 52470, 52493, 52533, 52506, 52496, 52519, 52492,
52509, 52530, 52213, 52499, 52492, 52528, 52499, 52526, 52521, 52488,
52485, 52502, 52515, 52470, 52207, 52494, 52527, 52442, 52200, 52485,
52489, 52499, 52488, 52486, 52232, 52477, 52485, 52490, 52524, 52470,
52504, 52501, 52497, 52489, 52152, 52527, 52487, 52501, 52504, 52494,
52484, 52213, 52449, 52490, 52525, 52476, 52540, 52463, 52200, 52471,
52479, 52504, 52526, 52533, 52473, 52475, 52518, 52507, 52500, 52499,
52512, 52478, 52523, 52453, 52488, 52523, 52240, 52505, 52532, 52504,
52444, 52194, 52514, 52474, 52473, 52526, 52437, 52536, 52491, 52523,
52529, 52535, 52453, 52522, 52519, 52446, 52500, 52490, 52459, 52467,
52456, 52490, 52521, 52484, 52508, 52451, 52231, 52488, 52485, 52215,
52493, 52475, 52474, 52508, 52524, 52477, 52514, 52452, 52491, 52473,
52441, 52520, 52471, 52466, 52475, 52439, 52483, 52491, 52204, 52500,
52488, 52489, 52519, 52495, 52448, 52453, 52466, 52462, 52489, 52471,
52484, 52483, 52501, 52486, 52494, 52473, 52481, 52502, 52516, 52223,
52490, 52447, 52222, 52469, 52509, 52194, 52490, 52484, 52446, 52487,
52476, 52509, 52496, 52459, 52474, 52501, 52516, 52223, 52487, 52468,
52534, 52522, 52474, 52227, 52450, 52506, 52193, 52429, 52496, 52493,
52493, 52488, 52190, 52509, 52434, 52469, 52510, 52481, 52520, 52504,
52230, 52500, 52487, 52517, 52473, 52488, 52450, 52203, 52215, 52490,
52479, 52515, 52210, 52485, 52516, 52504, 52521, 52499, 52503, 52526)
# Considering that the LOP is a maximization problem, we need isMinimizationProblem=FALSE.

 cumulative_difference_plot(PL_EDA_fitness,
                            PL_GS_fitness,
                            isMinimizationProblem=FALSE,
                            labelA="PL-EDA",
                            labelB="PL-GS")

[Package RVCompare version 0.1.8 Index]