001package org.hl7.fhir.instance.model.valuesets; 002 003/* 004 Copyright (c) 2011+, HL7, Inc. 005 All rights reserved. 006 007 Redistribution and use in source and binary forms, with or without modification, 008 are permitted provided that the following conditions are met: 009 010 * Redistributions of source code must retain the above copyright notice, this 011 list of conditions and the following disclaimer. 012 * Redistributions in binary form must reproduce the above copyright notice, 013 this list of conditions and the following disclaimer in the documentation 014 and/or other materials provided with the distribution. 015 * Neither the name of HL7 nor the names of its contributors may be used to 016 endorse or promote products derived from this software without specific 017 prior written permission. 018 019 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND 020 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 021 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. 022 IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, 023 INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT 024 NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 025 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 026 WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 027 ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 028 POSSIBILITY OF SUCH DAMAGE. 029 030*/ 031 032// Generated on Wed, Nov 11, 2015 10:54-0500 for FHIR v1.0.2 033 034 035public enum V3ProbabilityDistributionType { 036 037 /** 038 * The beta-distribution is used for data that is bounded on both sides and may or may not be skewed (e.g. occurs when probabilities are estimated.) Two parameters a and b are available to adjust the curve. The mean m and variance s2 relate as follows: m = a/ (a + b) and s2 = ab/((a + b)2 (a + b + 1)). 039 */ 040 B, 041 /** 042 * Used for data that describes extinction. The exponential distribution is a special form of g-distribution where a = 1, hence, the relationship to mean m and variance s2 are m = b and s2 = b2. 043 */ 044 E, 045 /** 046 * Used to describe the quotient of two c2 random variables. The F-distribution has two parameters n1 and n2, which are the numbers of degrees of freedom of the numerator and denominator variable respectively. The relationship to mean m and variance s2 are: m = n2 / (n2 - 2) and s2 = (2 n2 (n2 + n1 - 2)) / (n1 (n2 - 2)2 (n2 - 4)). 047 */ 048 F, 049 /** 050 * The gamma-distribution used for data that is skewed and bounded to the right, i.e. where the maximum of the distribution curve is located near the origin. The g-distribution has a two parameters a and b. The relationship to mean m and variance s2 is m = a b and s2 = a b2. 051 */ 052 G, 053 /** 054 * The logarithmic normal distribution is used to transform skewed random variable X into a normally distributed random variable U = log X. The log-normal distribution can be specified with the properties mean m and standard deviation s. Note however that mean m and standard deviation s are the parameters of the raw value distribution, not the transformed parameters of the lognormal distribution that are conventionally referred to by the same letters. Those log-normal parameters mlog and slog relate to the mean m and standard deviation s of the data value through slog2 = log (s2/m2 + 1) and mlog = log m - slog2/2. 055 */ 056 LN, 057 /** 058 * This is the well-known bell-shaped normal distribution. Because of the central limit theorem, the normal distribution is the distribution of choice for an unbounded random variable that is an outcome of a combination of many stochastic processes. Even for values bounded on a single side (i.e. greater than 0) the normal distribution may be accurate enough if the mean is "far away" from the bound of the scale measured in terms of standard deviations. 059 */ 060 N, 061 /** 062 * Used to describe the quotient of a normal random variable and the square root of a c2 random variable. The t-distribution has one parameter n, the number of degrees of freedom. The relationship to mean m and variance s2 are: m = 0 and s2 = n / (n - 2) 063 */ 064 T, 065 /** 066 * The uniform distribution assigns a constant probability over the entire interval of possible outcomes, while all outcomes outside this interval are assumed to have zero probability. The width of this interval is 2s sqrt(3). Thus, the uniform distribution assigns the probability densities f(x) = sqrt(2 s sqrt(3)) to values m - s sqrt(3) >= x <= m + s sqrt(3) and f(x) = 0 otherwise. 067 */ 068 U, 069 /** 070 * Used to describe the sum of squares of random variables which occurs when a variance is estimated (rather than presumed) from the sample. The only parameter of the c2-distribution is n, so called the number of degrees of freedom (which is the number of independent parts in the sum). The c2-distribution is a special type of g-distribution with parameter a = n /2 and b = 2. Hence, m = n and s2 = 2 n. 071 */ 072 X2, 073 /** 074 * added to help the parsers 075 */ 076 NULL; 077 public static V3ProbabilityDistributionType fromCode(String codeString) throws Exception { 078 if (codeString == null || "".equals(codeString)) 079 return null; 080 if ("B".equals(codeString)) 081 return B; 082 if ("E".equals(codeString)) 083 return E; 084 if ("F".equals(codeString)) 085 return F; 086 if ("G".equals(codeString)) 087 return G; 088 if ("LN".equals(codeString)) 089 return LN; 090 if ("N".equals(codeString)) 091 return N; 092 if ("T".equals(codeString)) 093 return T; 094 if ("U".equals(codeString)) 095 return U; 096 if ("X2".equals(codeString)) 097 return X2; 098 throw new Exception("Unknown V3ProbabilityDistributionType code '"+codeString+"'"); 099 } 100 public String toCode() { 101 switch (this) { 102 case B: return "B"; 103 case E: return "E"; 104 case F: return "F"; 105 case G: return "G"; 106 case LN: return "LN"; 107 case N: return "N"; 108 case T: return "T"; 109 case U: return "U"; 110 case X2: return "X2"; 111 default: return "?"; 112 } 113 } 114 public String getSystem() { 115 return "http://hl7.org/fhir/v3/ProbabilityDistributionType"; 116 } 117 public String getDefinition() { 118 switch (this) { 119 case B: return "The beta-distribution is used for data that is bounded on both sides and may or may not be skewed (e.g. occurs when probabilities are estimated.) Two parameters a and b are available to adjust the curve. The mean m and variance s2 relate as follows: m = a/ (a + b) and s2 = ab/((a + b)2 (a + b + 1))."; 120 case E: return "Used for data that describes extinction. The exponential distribution is a special form of g-distribution where a = 1, hence, the relationship to mean m and variance s2 are m = b and s2 = b2."; 121 case F: return "Used to describe the quotient of two c2 random variables. The F-distribution has two parameters n1 and n2, which are the numbers of degrees of freedom of the numerator and denominator variable respectively. The relationship to mean m and variance s2 are: m = n2 / (n2 - 2) and s2 = (2 n2 (n2 + n1 - 2)) / (n1 (n2 - 2)2 (n2 - 4))."; 122 case G: return "The gamma-distribution used for data that is skewed and bounded to the right, i.e. where the maximum of the distribution curve is located near the origin. The g-distribution has a two parameters a and b. The relationship to mean m and variance s2 is m = a b and s2 = a b2."; 123 case LN: return "The logarithmic normal distribution is used to transform skewed random variable X into a normally distributed random variable U = log X. The log-normal distribution can be specified with the properties mean m and standard deviation s. Note however that mean m and standard deviation s are the parameters of the raw value distribution, not the transformed parameters of the lognormal distribution that are conventionally referred to by the same letters. Those log-normal parameters mlog and slog relate to the mean m and standard deviation s of the data value through slog2 = log (s2/m2 + 1) and mlog = log m - slog2/2."; 124 case N: return "This is the well-known bell-shaped normal distribution. Because of the central limit theorem, the normal distribution is the distribution of choice for an unbounded random variable that is an outcome of a combination of many stochastic processes. Even for values bounded on a single side (i.e. greater than 0) the normal distribution may be accurate enough if the mean is \"far away\" from the bound of the scale measured in terms of standard deviations."; 125 case T: return "Used to describe the quotient of a normal random variable and the square root of a c2 random variable. The t-distribution has one parameter n, the number of degrees of freedom. The relationship to mean m and variance s2 are: m = 0 and s2 = n / (n - 2)"; 126 case U: return "The uniform distribution assigns a constant probability over the entire interval of possible outcomes, while all outcomes outside this interval are assumed to have zero probability. The width of this interval is 2s sqrt(3). Thus, the uniform distribution assigns the probability densities f(x) = sqrt(2 s sqrt(3)) to values m - s sqrt(3) >= x <= m + s sqrt(3) and f(x) = 0 otherwise."; 127 case X2: return "Used to describe the sum of squares of random variables which occurs when a variance is estimated (rather than presumed) from the sample. The only parameter of the c2-distribution is n, so called the number of degrees of freedom (which is the number of independent parts in the sum). The c2-distribution is a special type of g-distribution with parameter a = n /2 and b = 2. Hence, m = n and s2 = 2 n."; 128 default: return "?"; 129 } 130 } 131 public String getDisplay() { 132 switch (this) { 133 case B: return "beta"; 134 case E: return "exponential"; 135 case F: return "F"; 136 case G: return "(gamma)"; 137 case LN: return "log-normal"; 138 case N: return "normal (Gaussian)"; 139 case T: return "T"; 140 case U: return "uniform"; 141 case X2: return "chi square"; 142 default: return "?"; 143 } 144 } 145 146 147} 148