001package org.hl7.fhir.r4.model.codesystems; 002 003/*- 004 * #%L 005 * org.hl7.fhir.r4 006 * %% 007 * Copyright (C) 2014 - 2019 Health Level 7 008 * %% 009 * Licensed under the Apache License, Version 2.0 (the "License"); 010 * you may not use this file except in compliance with the License. 011 * You may obtain a copy of the License at 012 * 013 * http://www.apache.org/licenses/LICENSE-2.0 014 * 015 * Unless required by applicable law or agreed to in writing, software 016 * distributed under the License is distributed on an "AS IS" BASIS, 017 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 018 * See the License for the specific language governing permissions and 019 * limitations under the License. 020 * #L% 021 */ 022 023 024/* 025 Copyright (c) 2011+, HL7, Inc. 026 All rights reserved. 027 028 Redistribution and use in source and binary forms, with or without modification, 029 are permitted provided that the following conditions are met: 030 031 * Redistributions of source code must retain the above copyright notice, this 032 list of conditions and the following disclaimer. 033 * Redistributions in binary form must reproduce the above copyright notice, 034 this list of conditions and the following disclaimer in the documentation 035 and/or other materials provided with the distribution. 036 * Neither the name of HL7 nor the names of its contributors may be used to 037 endorse or promote products derived from this software without specific 038 prior written permission. 039 040 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND 041 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 042 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. 043 IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, 044 INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT 045 NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 046 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 047 WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 048 ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 049 POSSIBILITY OF SUCH DAMAGE. 050 051*/ 052 053// Generated on Wed, Jan 30, 2019 16:19-0500 for FHIR v4.0.0 054 055 056import org.hl7.fhir.exceptions.FHIRException; 057 058public enum V3ProbabilityDistributionType { 059 060 /** 061 * 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)). 062 */ 063 B, 064 /** 065 * 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. 066 */ 067 E, 068 /** 069 * 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)). 070 */ 071 F, 072 /** 073 * 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. 074 */ 075 G, 076 /** 077 * 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. 078 */ 079 LN, 080 /** 081 * 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. 082 */ 083 N, 084 /** 085 * 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) 086 */ 087 T, 088 /** 089 * 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. 090 */ 091 U, 092 /** 093 * 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. 094 */ 095 X2, 096 /** 097 * added to help the parsers 098 */ 099 NULL; 100 public static V3ProbabilityDistributionType fromCode(String codeString) throws FHIRException { 101 if (codeString == null || "".equals(codeString)) 102 return null; 103 if ("B".equals(codeString)) 104 return B; 105 if ("E".equals(codeString)) 106 return E; 107 if ("F".equals(codeString)) 108 return F; 109 if ("G".equals(codeString)) 110 return G; 111 if ("LN".equals(codeString)) 112 return LN; 113 if ("N".equals(codeString)) 114 return N; 115 if ("T".equals(codeString)) 116 return T; 117 if ("U".equals(codeString)) 118 return U; 119 if ("X2".equals(codeString)) 120 return X2; 121 throw new FHIRException("Unknown V3ProbabilityDistributionType code '"+codeString+"'"); 122 } 123 public String toCode() { 124 switch (this) { 125 case B: return "B"; 126 case E: return "E"; 127 case F: return "F"; 128 case G: return "G"; 129 case LN: return "LN"; 130 case N: return "N"; 131 case T: return "T"; 132 case U: return "U"; 133 case X2: return "X2"; 134 default: return "?"; 135 } 136 } 137 public String getSystem() { 138 return "http://terminology.hl7.org/CodeSystem/v3-ProbabilityDistributionType"; 139 } 140 public String getDefinition() { 141 switch (this) { 142 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))."; 143 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."; 144 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))."; 145 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."; 146 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."; 147 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."; 148 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)"; 149 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."; 150 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."; 151 default: return "?"; 152 } 153 } 154 public String getDisplay() { 155 switch (this) { 156 case B: return "beta"; 157 case E: return "exponential"; 158 case F: return "F"; 159 case G: return "(gamma)"; 160 case LN: return "log-normal"; 161 case N: return "normal (Gaussian)"; 162 case T: return "T"; 163 case U: return "uniform"; 164 case X2: return "chi square"; 165 default: return "?"; 166 } 167 } 168 169 170} 171