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author stevecassidy
date Sat, 11 Mar 2017 21:35:38 -0500
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6c1e450b02c3 planemo upload commit 72cee9103c0ae4acb5794afaed179bea2c729f2c-dirty
stevecassidy
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1 parseEval <- function (c1, c2, c3, cipi12, cipi23, voweld, anchor=15, var_anchor=3, types, words=12, simN=1000, RE_rsd, CC_rsd, LE_rsd) {
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2 # basic validy-check ... input parameters
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3 c1<-as.numeric(c1)
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4 c2<-as.numeric(c2)
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5 c3<-as.numeric(c3)
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6 cipi12<-as.numeric(cipi12)
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7 cipi23<-as.numeric(cipi23)
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8 voweld<-as.numeric(voweld)
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9 anchor<-as.integer(anchor)
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10 var_anchor<-as.integer(var_anchor)
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11 types<-as.integer(types)
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12 words<-as.integer(words)
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13 simN<-as.integer(simN)
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14 RE_rsd<-as.numeric(RE_rsd)
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15 CC_rsd<-as.numeric(CC_rsd)
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16 LE_rsd<-as.numeric(LE_rsd)
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17 # phonetic parameters
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18 C1p<-c3[1] # plateau duration of the first consonant in triconsonantal clusters
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19 C1stdv<-c3[2] # standard deviation of the first consonant in triconsonantal clusters
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20 C2p<-c2[1] # plateau duration of the second consonant in triconsonantal clusters and the first consonant in bi-consonantal clusters
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21 C2stdv<-c2[2] # standard deviation of the second consonant in triconsonantal clusters and the first consonant in bi-consonantal clusters
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22 C3p<-c1[1] # plateau duration of the immediately prevocalic consonant
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23 C3stdv<-c1[2] # standard deviation of the immediately prevocalic consonant
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24 C12ipi<-cipi12[1] # the duration of the interval between the plateaus of the first two consonants in triconsonantal clusters
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25 C12stdv<-cipi12[2] # the standard deviation of the interval between the plateaus of the first two consonants in triconsonantal clusters
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26 C23ipi<-cipi23[1] # the duration of the interval between the plateaus of the first two consonants in biconsonantal clusters and between the second two consonants in triconsonantal clusters
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27 C23stdv<-cipi23[2] # the standard deviation of the interval between the plateaus of the first two consonants in biconsonantal clusters and between the second two consonants in triconsonantal clusters
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28 vowel_duration<-voweld # the duration of the vowel
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29 variability_range<-anchor # number of stepwise increases in variability simulated by the model
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30 variability_resolution<-var_anchor # size of each stepwise increase in variability
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31 words_per_word_type<-words # the number of words (stimuli) per word type, aka "little n"
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32 word_types<-types # number of different word types, e.g. #CV-, #CCV-, #CCCV-
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33 data_RSD<-c(RE_rsd, LE_rsd, CC_rsd) # lumps RSD measures into single array
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34 #creating matrices for later use
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35 A_simp <- matrix(nrow=variability_range, ncol=words_per_word_type)
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36 A_comp <- matrix(nrow=variability_range, ncol=words_per_word_type)
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37 # creating matrices to hold the SD values
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38 LE_SD_simp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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39 LE_SD_comp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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40 RE_SD_simp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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41 RE_SD_comp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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42 CC_SD_simp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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43 CC_SD_comp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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44 # creating matrices to hold the RSD values
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45 LE_RSD_simp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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46 LE_RSD_comp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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47 RE_RSD_simp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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48 RE_RSD_comp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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49 CC_RSD_simp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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50 CC_RSD_comp<-matrix(nrow=simN, ncol=variability_range, byrow=TRUE)
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51 if (word_types==3) {
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52 tepa<-c("Testing Triads")
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53 # print(c("Simulating Data (Triads)"), quote=F)
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54 # pb <- txtProgressBar(min = 0, max = simN, style = 3)
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55 for (count in 1:simN) {
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56 # setTxtProgressBar(pb, count)
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57 # generate CCC tokens
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58 # generate timestamps for C3 (the prevocalic consonant)
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59 # generate general error-term for C3
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60 e <- C3stdv*(rnorm(1))
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61 # generate R(ight plateau edge = Release) of prevocalic consonant
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62 # generate words_per_word_type/word_types Gaussian distributed numbers (for CCC tokens only)
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63 # with mean 500, variance 10
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64 CCCR3 <- rnorm(words_per_word_type/word_types, mean=500, sd=sqrt(20))
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65 # generate L(eft plateau edge = Target) of prevocalic consonant
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66 CCCL3 <- CCCR3 - C3p + e #generate L3 corresponding to R3 by assuming a plateau duration of C3p
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67 # calculate midpoint of prevocalic consonant plateau
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68 CCCM3 <- ((CCCR3 + CCCL3) / 2)
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69 # generate timestamps for C2
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70 # generate general error-term for C2
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71 e1 <- C23stdv * (rnorm(1)) #normally distributed random error mean=0, sd=1
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72 e2 <- C2stdv * (rnorm(1)) #normally distributed random error mean=0, sd=1
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73 # Generate right edge of C2
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74 CCCR2 <- CCCL3 - C23ipi + e1 # generate right edge of C2 from left edge of C3 assuming an ipi of C23ipi
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75 # Generate left edge of C2
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76 CCCL2 <- CCCR2 - C2p + e2 # generate left edge from right edge by assuming a plateau duration
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77 # Calculate midpoint of C2
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78 CCCM2 <- ((CCCR2+CCCL2)/2)
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79 # generate timestamps for C1
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80 # generate general error-term for C1
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81 e1 <- C12stdv * (rnorm(1)) # normally distributed random error
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82 e2 <- C3stdv * (rnorm(1))
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83 # Generate right edge
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84 CCCR1 <- CCCL2 - C12ipi + e1 # generate right edge of C1 from left edge of C2 assuming ipi of 40ms
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85 # generate L(eft plateau edge = Target) of C1
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86 CCCL1 <- CCCR1 - C3p + e2 # generate L2 corresponding to CR1 by assuming a plateau of 10ms
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87 # calculate midpoint of prevocalic consonant
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88 CCCM1 <- ((CCCR1 + CCCL1)/2) # right edge of C1
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89 #generate CC tokens
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90 #generate timestamps for C3 (prevocalic consonant)
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91 # generate general error-term for C3
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92 e <- C3stdv * (rnorm(1)) # normally distributed random error
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93 # generate R(ight plateau edge = Release) of prevocalic consonant
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94 CCR3 <- rnorm(words_per_word_type/word_types, mean=500, sd=sqrt(20)) # generate N Gaussian distributed numbers with mean 500, variance 10
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95 # generate L(eft plateau edge = Target) of prevocalic consonant
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96 CCL3 <- CCR3 - C3p + e # generate L3 corresponding to R3 by assuming a plateau duration of C3p
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97 # calculate midpoint of prevocalic consonant plateau
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98 CCM3 <- ((CCR3 + CCL3) / 2)
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99 #generate timestamps for C2
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100 # generate general error-term for C2
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101 e1 <- C23stdv * (rnorm(1))
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102 e2 <- C2stdv * (rnorm(1))
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103 # Generate right edge of C2
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104 CCR2 <- CCL3 - C23ipi + e1 # generate right edge of C2 from left edge of C3 assuming an ipi of C23ipi
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105 # Generate left edge of C2
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106 CCL2 <- CCR2 - C2p + e2 # generate left edge from right edge by assuming a plateau duration
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107 # Calculate midpoint of C2
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108 CCM2 <- ((CCR2 + CCL2) / 2)
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109 # generate C tokens
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110 # generate timestamps for C3 (the prevocalic consonant)
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111 # generate general error-term for C3
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112 e <- C3stdv * (rnorm(1))
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113 # Generate R(ight plateau edge = Release) of prevocalic consonant
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114 CR3 <- rnorm(words_per_word_type/word_types, mean=500, sd=sqrt(20)) # generate N Gaussian distributed numbers with mean 500, variance 10
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115 # generate L(eft plateau edge = Target) of prevocalic consonant
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116 CL3 <- CR3 - C3p + e # generate L3 corresponding to R3 by assuming a plateau duration of C3p
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117 # calculate midpoint of prevocalic consonant plateau
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118 CM3 <- ((CR3 + CL3) / 2)
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119 # generate timestamps for CCglobal
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120 # for CCC clusters
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121 CCglobal <- apply(cbind(CCCM1, CCCM2, CCCM3), 1, mean) #mean of consonant plateaux midpoints
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122 # for CC clusters
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123 CCglobal <- append(CCglobal, apply(cbind(CCM2, CCM3), 1, mean)) # mean of consonant plateaux midpoints
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124 # for C clusters
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125 CCglobal <- append(CCglobal, CM3)
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126 # populate a single array with the midpoint of the pre-vocalic
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127 # consonant of every word type; this array will be used to generate anchors
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128 # for CCC clusters
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129 Global_CM3 <- CCCM3 # mean of consonant plateaux midpoints
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130 # for CC clusters
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131 Global_CM3 <- append(Global_CM3, CCM3) # mean of consonant plateaux midpoints
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132 # for C clusters
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133 Global_CM3 <- append(Global_CM3, CM3)
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134 # populate a single array with the Left_edge of the consonant cluster for every token
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135 # this array will be used to calculate SD and RSD for EDGE to Anchor intervals
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136 # for CCC clusters
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137 Global_CL1 <- CCCL1 # Assigns the left edge of tri-consonantal tokens to the first third of Global_Cl1
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138 # for CC clusters
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139 Global_CL1 <- append(Global_CL1, CCL2) # Assigns the left edge of bi-consonantal tokens to the second third of Global_Cl1
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140 # for C clusters
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141 Global_CL1 <- append(Global_CL1, CL3) # Assigns the left edge of mono-consonantal tokens to the last third of Global_Cl1
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142 # populate a single array with the Right_edge of the consonant cluster for every token
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143 # this array is used to calculate SD and RSD for EDGE to Anchor intervals
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144 # for CCC clusters
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145 Global_CR3 <- CCCR3 # mean of consonant plateaux midpoints
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146 # for CC clusters
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147 Global_CR3 <- append(Global_CR3, CCR3) # mean of consonant plateaux midpoints
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148 # for C clusters
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149 Global_CR3 <- append(Global_CR3, CR3) # CCglobal synchronous with prevocalic consonant's plateau midpoint
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150 # generate series of anchor points increasing in variability and/or distance from
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151 # the prevocalic consonant reset the anchor array to zero
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152 # one row for each anchor and one column for each token
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153
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154 # loop produces data/anchor for each token based on Simplex Hypothesis
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155 stdv <- 0 # reset the value of the anchor stdev to zero
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156 Ae <- NULL # reset anchor-error-term
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157 for (cycle in 1:variability_range){ #creates multiple anchor points for each token
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158 for (m in 1:words_per_word_type){ #creates anchor point for each token from the right edge of the token
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159 Ae<-stdv*(rnorm(n=1)) #normally distributed random error, assuming mean of 0
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160 A_simp[cycle, m] <- Global_CM3[m] + vowel_duration + Ae #generate anchor A according to the simplex onset hypothesis
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161 }
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162 stdv<-stdv+variability_resolution #creates new anchor point
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163 }
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164 # loop produces data/anchor for each token based on Complex Hypothesis
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165 stdv <- 0 # reset the value of the anchor stdev to zero
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166 Ae <- NULL # reset anchor-error-term
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167 for (cycle in 1:variability_range) { #creates multiple anchor points for each token
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168 for (m in 1:words_per_word_type) { #creates anchor point for each token from the right edge of the token
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169 Ae<-stdv*(rnorm(1)) #normally distributed random error, assuming mean of 0
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170 A_comp[cycle, m]<-CCglobal[m]+vowel_duration+Ae #generate anchor A according to the complex onset hypothesis
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171 }
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172 stdv<-stdv+variability_resolution #creates new anchor point
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173 }
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174 # Note about consonantal landmarks:
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175 # they are replaced with each cycle of the simulation
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176 # in constrast, RSD values for each landmark are stored across simulations.
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177 # creating matrices to hold the SD values
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178 x <- function(x) {sd(x-Global_CL1)}
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179 y <- function(y) {sd(y-Global_CR3)}
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180 z <- function(z) {sd(z-CCglobal)}
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181 # computing the SD values
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182 LE_SD_simp[count,] <- apply(A_simp, 1, x)
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183 LE_SD_comp[count,] <- apply(A_comp, 1, x)
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184 RE_SD_simp[count,] <- apply(A_simp, 1, y)
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185 RE_SD_comp[count,] <- apply(A_comp, 1, y)
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186 CC_SD_simp[count,] <- apply(A_simp, 1, z)
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187 CC_SD_comp[count,] <- apply(A_comp, 1, z)
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188 # computing the RSD values
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189 LE_RSD_simp[count,] <- (apply(A_simp, 1, x))/((apply(A_simp, 1, mean))-mean(Global_CL1))
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190 LE_RSD_comp[count,] <- (apply(A_comp, 1, x))/((apply(A_comp, 1, mean))-mean(Global_CL1))
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191 RE_RSD_simp[count,] <- (apply(A_simp, 1, y))/((apply(A_simp, 1, mean))-mean(Global_CR3))
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192 RE_RSD_comp[count,] <- (apply(A_comp, 1, y))/((apply(A_comp, 1, mean))-mean(Global_CR3))
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193 CC_RSD_simp[count,] <- (apply(A_simp, 1, z))/(apply(A_simp, 1, mean)-mean(CCglobal))
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194 CC_RSD_comp[count,] <- (apply(A_comp, 1, z))/(apply(A_comp, 1, mean)-mean(CCglobal))
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195 }
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196 # close(pb)
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197 }
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198 if (word_types==2) {
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199 tepa<-c("Testing Dyads")
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200 # print(c("Simulating Data (Dyads)"), quote=F)
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201 # pb <- txtProgressBar(min = 0, max = simN, style = 3)
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202 for (count in 1:simN) {
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203 # setTxtProgressBar(pb, count)
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204 # generate CCC tokens
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205 # generate timestamps for C3 (the prevocalic consonant)
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206 # generate general error-term for C3
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207 e <- C3stdv * (rnorm(1))
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208 # generate R(ight plateau edge = Release) of prevocalic consonant
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209 # generate words_per_word_type/word_types Gaussian distributed numbers (for CCC tokens only)
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210 # with mean 500, variance 10
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211 CCCR3 <- rnorm(words_per_word_type/word_types, mean=500, sd=sqrt(20))
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212 # generate L(eft plateau edge = Target) of prevocalic consonant
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213 CCCL3 <- abs(CCCR3 - C3p + e) #generate L3 corresponding to R3 by assuming a plateau duration of C3p
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214 # calculate midpoint of prevocalic consonant plateau
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215 CCCM3 <- abs((CCCR3 + CCCL3) / 2)
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216 # generate timestamps for C2
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217 # generate general error-term for C2
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218 e1 <- C23stdv * (rnorm(1)) #normally distributed random error
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219 e2 <- C2stdv * (rnorm(1)) #normally distributed random error
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220 # Generate right edge of C2
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221 CCCR2 <- abs(CCCL3 - C23ipi + e1) # generate right edge of C2 from left edge of C3 assuming an ipi of C23ipi
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222 # Generate left edge of C2
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223 CCCL2 <- abs(CCCR2 - C2p + e2) # generate left edge from right edge by assuming a plateau duration
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224 # Calculate midpoint of C2
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225 CCCM2 <- abs((CCCR2 + CCCL2) / 2)
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226 # generate timestamps for C1
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227 # generate general error-term for C1
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228 e1 <- C12stdv * (rnorm(1)) # normally distributed random error
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229 e2<-C3stdv * (rnorm(1))
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230 # Generate right edge
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231 CCCR1 <- abs(CCCL2 - C12ipi + e1) # generate right edge of C1 from left edge of C2 assuming ipi of 40ms
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232 # generate L(eft plateau edge = Target) of C1
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233 CCCL1 <- abs(CCCR1 - C3p + e2) # generate L2 corresponding to CR1 by assuming a plateau of 10ms
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234 # calculate midpoint of prevocalic consonant
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235 CCCM1 <- abs((CCCR1 + CCCL1) / 2) # right edge of C1
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236 #generate CC tokens
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237 #generate timestamps for C3 (prevocalic consonant)
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238 # generate general error-term for C3
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239 e <- C3stdv * (rnorm(1)) # normally distributed random error, 0 mean
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240 # generate R(ight plateau edge = Release) of prevocalic consonant
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241 CCR3 <- rnorm(words_per_word_type/word_types, mean=500, sd=sqrt(20)) # generate N Gaussian distributed numbers with mean 500, variance 10
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242 # generate L(eft plateau edge = Target) of prevocalic consonant
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243 CCL3 <- abs(CCR3 - C3p + e) # generate L3 corresponding to R3 by assuming a plateau duration of C3p
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244 # calculate midpoint of prevocalic consonant plateau
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245 CCM3 <- abs((CCR3 + CCL3) / 2)
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246 #generate timestamps for C2
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247 # generate general error-term for C2
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248 e1 <- C23stdv * (rnorm(1))
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249 e2 <- C2stdv * (rnorm(1))
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250 # Generate right edge of C2
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251 CCR2 <- abs(CCL3 - C23ipi + e) # generate right edge of C2 from left edge of C3 assuming an ipi of C23ipi
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252 # Generate left edge of C2
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253 CCL2 <- abs(CCR2 - C2p + e) # generate left edge from right edge by assuming a plateau duration
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254 # Calculate midpoint of C2
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255 CCM2 <- abs((CCR2 + CCL2) / 2)
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256 # generate timestamps for CCglobal
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257 # for CCC clusters
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258 CCglobal <- apply(cbind(CCCM1, CCCM2, CCCM3), 1, mean) #mean of consonant plateaux midpoints
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259 # for CC clusters
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260 CCglobal <- append(CCglobal, apply(cbind(CCM2, CCM3), 1, mean)) # mean of consonant plateaux midpoints
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261 # populate a single array with the midpoint of the pre-vocalic
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262 # consonant of every word type; this array will be used to generate anchors
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263 # for CCC clusters
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264 Global_CM3 <- CCCM3 # mean of consonant plateaux midpoints
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265 # for CC clusters
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266 Global_CM3 <- append(Global_CM3, CCM3, after=length(CCCM3)) # mean of consonant plateaux midpoints
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267 # populate a single array with the Left_edge of the consonant cluster for every token
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268 # this array will be used to calculate SD and RSD for EDGE to Anchor intervals
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269 # for CCC clusters
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270 Global_CL1 <- CCCL1 # Assigns the left edge of tri-consonantal tokens to the first third of Global_Cl1
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271 # for CC clusters
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272 Global_CL1 <- append(Global_CL1, CCL2, after=length(CCCL1)) # Assigns the left edge of bi-consonantal tokens to the second third of Global_Cl1
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273 # populate a single array with the Right_edge of the consonant cluster for every token
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274 # this array is used to calculate SD and RSD for EDGE to Anchor intervals
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275 # for CCC clusters
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276 Global_CR3 <- CCCR3 # mean of consonant plateaux midpoints
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277 # for CC clusters
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278 Global_CR3 <- append(Global_CR3, CCR3, after=length(CCCR3)) # mean of consonant plateaux midpoints
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279 # generate series of anchor points increasing
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280 # in variability and/or distance from the prevocalic consonant
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281 stdv <- 0
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282 Ae <- NULL
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283 for (cycle in 1:variability_range){ #creates multiple anchor points for each token
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284 for (m in 1:words_per_word_type){ #creates anchor point for each token from the right edge of the token
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285 Ae<-stdv*(rnorm(n=1)) #normally distributed random error, assuming mean of 0
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286 A_simp[cycle, m]<-Global_CM3[m] + vowel_duration + Ae #generate anchor A according to the simplex onset hypothesis
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287 }
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288 stdv<-stdv+variability_resolution
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289 }
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290 # loop produces anchor for each token based on Complex Hypothesis
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291 stdv <- 0
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292 Ae <- NULL
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293 for (cycle in 1:variability_range){ #creates multiple anchor points for each token
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294 for (m in 1:words_per_word_type){ #creates anchor point for each token from the right edge of the token
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295 Ae<-stdv*(rnorm(1)) #normally distributed random error, assuming mean of 0
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296 A_comp[cycle, m]<-CCglobal[m]+vowel_duration+Ae #generate anchor A according to the complex onset hypothesis
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297 }
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298 stdv<-stdv+variability_resolution #creates new anchor point
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299 }
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300 #creating matrices to hold the SD values
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301 x <- function(x) {sd(x-Global_CL1)}
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302 y <- function(y) {sd(y-Global_CR3)}
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303 z <- function(z) {sd(z-CCglobal)}
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304 # computing the SD values
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305 LE_SD_simp[count,] <- abs(apply(A_simp, 1, x))
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306 LE_SD_comp[count,] <- abs(apply(A_comp, 1, x))
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307 RE_SD_simp[count,] <- abs(apply(A_simp, 1, y))
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308 RE_SD_comp[count,] <- abs(apply(A_comp, 1, y))
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309 CC_SD_simp[count,] <- abs(apply(A_simp, 1, z))
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310 CC_SD_comp[count,] <- abs(apply(A_comp, 1, z))
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311 # computing the RSD values
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312 LE_RSD_simp[count,] <- abs((apply(A_simp, 1, x))/((apply(A_simp, 1, mean))-mean(Global_CL1)))
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313 LE_RSD_comp[count,] <- abs((apply(A_comp, 1, x))/((apply(A_comp, 1, mean))-mean(Global_CL1)))
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314 RE_RSD_simp[count,] <- abs((apply(A_simp, 1, y))/((apply(A_simp, 1, mean))-mean(Global_CR3)))
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315 RE_RSD_comp[count,] <- abs((apply(A_comp, 1, y))/((apply(A_comp, 1, mean))-mean(Global_CR3)))
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316 CC_RSD_simp[count,] <- abs((apply(A_simp, 1, z))/(apply(A_simp, 1, mean)-mean(CCglobal)))
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317 CC_RSD_comp[count,] <- abs((apply(A_comp, 1, z))/(apply(A_comp, 1, mean)-mean(CCglobal)))
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318 }
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319 }
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320 # close(pb)
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321 # pb <- txtProgressBar(min = 1, max = variability_range, style = 3)
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322 # assorted variables for diagnostics / plotting
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323 aip_1<-rep(c(1:variability_range), 3)
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324 edgep_1<-rep(c("LE_RSD", "RE_RSD", "CC_RSD"), each=variability_range)
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325 LE_RSD_simp_median<-apply(apply(LE_RSD_simp, 2, sort), 2, median)
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326 RE_RSD_simp_median<-apply(apply(RE_RSD_simp, 2, sort), 2, median)
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327 CC_RSD_simp_median<-apply(apply(CC_RSD_simp, 2, sort), 2, median)
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328 LE_RSD_comp_median<-apply(apply(LE_RSD_comp, 2, sort), 2, median)
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329 RE_RSD_comp_median<-apply(apply(RE_RSD_comp, 2, sort), 2, median)
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330 CC_RSD_comp_median<-apply(apply(CC_RSD_comp, 2, sort), 2, median)
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331 simp<-c(LE_RSD_simp_median, RE_RSD_simp_median, CC_RSD_simp_median)
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332 comp<-c(LE_RSD_comp_median, RE_RSD_comp_median, CC_RSD_comp_median)
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333 RE_RSD_median<-c(RE_RSD_simp_median, RE_RSD_comp_median)
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334 CC_RSD_median<-c(CC_RSD_simp_median, CC_RSD_comp_median)
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335 # median RDSs across simulations as a function of anchorindex
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336 plot.1<-data.frame(anchorindex=aip_1, edge=edgep_1, parse_s=simp, parse_c=comp)
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337 # aggregating data for goodness of fit evaluation
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338 RE_RSD_simp<-t(RE_RSD_simp)
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339 LE_RSD_simp<-t(LE_RSD_simp)
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340 CC_RSD_simp<-t(CC_RSD_simp)
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341 RE_RSD_comp<-t(RE_RSD_comp)
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342 LE_RSD_comp<-t(LE_RSD_comp)
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343 CC_RSD_comp<-t(CC_RSD_comp)
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344 # looping through the data to get the gof results
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345 data_simp<-matrix(ncol=4)
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346 data_comp<-matrix(ncol=4)
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347 sigfit<-function(x) {
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348 if(x > 98.503) {
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349 SigFit<-1
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350 } else {
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351 SigFit<-0
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352 }
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353 }
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354 # analyzing data simplex
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355 # print(c("Analysing... simple parse"), quote=F)
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356 for (i in 1 : variability_range) {
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357 # setTxtProgressBar(pb, i)
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358 sim_RSD<-cbind(RE_RSD_simp[i,], LE_RSD_simp[i,],CC_RSD_simp[i,])
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359 temp<-apply(sim_RSD, 1, function(x) (lm(data_RSD ~ x)))
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360 # organizing data for final analyses
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361 # creating anchor-index
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362 anchor_idx<-rep(i, times=simN)
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363 # extracting F-Statistics
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364 fstat<-unlist(lapply(temp, function(x) summary(x)$fstatistic[1]))
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365 # extracting R-Squared values
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366 rsquared<-unlist(lapply(temp, function(x) summary(x)$r.squared))
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367 # check for SigFit
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368 sgf<-sapply(fstat, sigfit)
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369 # aggregating data
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370 agg_mat<-matrix(data=c(anchor_idx, fstat, rsquared, sgf), nrow=length(anchor_idx), ncol=4, dimnames=list(c(NULL), c("Anchorindex", "Fratio", "Rsquared", "SigFit")))
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371 # adding sgf to existing data
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372 data_simp<-rbind(data_simp, agg_mat)
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373 }
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374 outp_sp<-temp
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375 data_simp<-data_simp[complete.cases(data_simp),]
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376 data_simp<-as.data.frame(data_simp)
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377 data_simp$Anchorindex<-as.factor(data_simp$Anchorindex)
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378 output_simp<-tapply(data_simp$SigFit, data_simp$Anchorindex, sum)
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379 # analyzing data complex
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380 # print(c("Analysing... complex parse"), quote=F)
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381 for (i in 1 : variability_range) {
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382 # setTxtProgressBar(pb, i)
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383 sim_RSD<-cbind(RE_RSD_comp[i,], LE_RSD_comp[i,],CC_RSD_comp[i,])
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384 temp<-apply(sim_RSD, 1, function(x) (lm(data_RSD ~ x)))
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385 # organizing data for final analyses
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386 anchor_idx<-rep(i, times=simN)
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387 # extracting F-Statistics
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388 fstat<-unlist(lapply(temp, function(x) summary(x)$fstatistic[1]))
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389 # extracting R-Squared values
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390 rsquared<-unlist(lapply(temp, function(x) summary(x)$r.squared))
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391 # check for SigFit
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392 sgf<-sapply(fstat, sigfit)
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393 # aggregating data
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394 agg_mat<-matrix(data=c(anchor_idx, fstat, rsquared, sgf), nrow=length(anchor_idx), ncol=4, dimnames=list(c(NULL), c("Anchorindex", "Fratio", "Rsquared", "SigFit")))
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395 # adding sgf to existing data
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396 data_comp<-rbind(data_comp, agg_mat)
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397 }
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398 outp_cp<-temp
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399 data_comp<-data_comp[complete.cases(data_comp),]
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400 data_comp<-as.data.frame(data_comp)
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401 data_comp$Anchorindex<-as.factor(data_comp$Anchorindex)
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402 output_comp<-tapply(data_comp$SigFit, data_comp$Anchorindex, sum)
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403 # diagnostic plot 2
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404 output_plot.2<-cbind(output_simp, output_comp)
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405 names(output_plot.2)<-NULL
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406 colnames(output_plot.2)<-c("parse_s", "parse_c")
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407 aip_2<-(1:variability_range)
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408 plot.2<-data.frame(anchorindex=aip_2, output_plot.2, hitr_s=(output_simp/simN), hitr_c=(output_comp/simN))
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409 # assessing overall model quality
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410 # sum of hits per number of simulations
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411 modq_s<-(sum(plot.2[,2]))/simN
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412 modq_c<-(sum(plot.2[,3]))/simN
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413 # assorted data for third diagnostic plot
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414 # sorting by Rsquared (asc), tie-breaker by Fratio (asc)
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415 data_simp_o<-data_simp[order(data_simp[,3], data_simp[,2]),]
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416 data_comp_o<-data_comp[order(data_comp[,3], data_comp[,2]),]
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417 aip_3<-rep(c(1:variability_range), 2)
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418 parse.f<-rep(c("simp","comp"), each=variability_range)
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419 # median
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420 simp_rs_median<-tapply(data_simp_o$Rsquared, data_simp_o$Anchorindex, median)
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421 comp_rs_median<-tapply(data_comp_o$Rsquared, data_comp_o$Anchorindex, median)
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422 simp_fr_median<-tapply(data_simp_o$Fratio, data_simp_o$Anchorindex, median)
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423 comp_fr_median<-tapply(data_comp_o$Fratio, data_comp_o$Anchorindex, median)
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424 rs_median<-c(simp_rs_median, comp_rs_median)
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425 fr_median<-c(simp_fr_median, comp_fr_median)
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stevecassidy
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426 plot.3_median<-data.frame(anchorindex=aip_3, parse=parse.f, rs_median=rs_median, fr_median=fr_median)
6c1e450b02c3 planemo upload commit 72cee9103c0ae4acb5794afaed179bea2c729f2c-dirty
stevecassidy
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427 # mean
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428 simp_rs_mean<-tapply(data_simp_o$Rsquared, data_simp_o$Anchorindex, mean)
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stevecassidy
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429 comp_rs_mean<-tapply(data_comp_o$Rsquared, data_comp_o$Anchorindex, mean)
6c1e450b02c3 planemo upload commit 72cee9103c0ae4acb5794afaed179bea2c729f2c-dirty
stevecassidy
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430 simp_fr_mean<-tapply(data_simp_o$Fratio, data_simp_o$Anchorindex, mean)
6c1e450b02c3 planemo upload commit 72cee9103c0ae4acb5794afaed179bea2c729f2c-dirty
stevecassidy
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431 comp_fr_mean<-tapply(data_comp_o$Fratio, data_comp_o$Anchorindex, mean)
6c1e450b02c3 planemo upload commit 72cee9103c0ae4acb5794afaed179bea2c729f2c-dirty
stevecassidy
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432 rs_mean<-c(simp_rs_mean, comp_rs_mean)
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stevecassidy
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433 fr_mean<-c(simp_fr_mean, comp_fr_mean)
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stevecassidy
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434 plot.3_mean<-data.frame(anchorindex=aip_3, parse=parse.f, rs_mean=rs_mean, fr_mean=fr_mean)
6c1e450b02c3 planemo upload commit 72cee9103c0ae4acb5794afaed179bea2c729f2c-dirty
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435 # prepare for output
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436 # close(pb)
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437 output<-list("perf"=list("Performance Simple"=modq_s, "Performance Complex"=modq_c), "mode"=tepa, "Plot_1"=plot.1, "Plot_2"=plot.2, "Plot_3"=list("mean"=plot.3_mean, "median"=plot.3_median), "reg"=list("simplex_parse"<-outp_sp, "complex_parse"<-outp_cp), "sim_RSD"=list("simp"=data_simp, "comp"=data_comp))
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stevecassidy
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438 cat("\n", "\n","Overall Quality of Modell-Performance", "\t", "(", tepa, ")", "\n",
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stevecassidy
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439 "(Ratio of:","\t", "Total Number of Hits / Number of Simulations)","\n",
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440 "------------------------","\n",
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441 "Simple Modelling:", "\t", modq_s, "\t","\t","\t","\t", sum(plot.2[,2])," / ", simN, "\n", "\n",
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442 "Complex Modelling:", "\t", modq_c, "\t","\t","\t","\t", sum(plot.2[,3])," / ", simN, "\n", "\n", sep="")
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stevecassidy
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443 return(invisible(output))
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stevecassidy
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444 }