Sweet Adaptation vs Desensitization MS
May code codes!
March 29, 2022
- Setting Up
- Import Data Sets
- Experiment 1: 5-min adaptation reliability
- Experiment 2: Detection threshold, JND, exposed JND reliability
- Experiment 2: Detection threshold, JND, exposed JND difference
- Experiment 3: Suprathreshold Reliability
- Experiment 3: Suprathreshold DRC - Glucose
- Experiment 3: Suprathreshold DRC - Glucose: Fixed Top
- Experiment 3: Suprathreshold DRC - Sucralose
- Experiment 3: Suprathreshold DRC - Sucralose: Fixed Top
- Experiment 3: Suprathreshold DRC - RebA
- Experiment 3: Suprathreshold DRC - RebA: Fixed Top
Setting Up
Import Data Sets
Experiment 1: 5-min adaptation reliability
## Experiment 1: 5-min adaptation differences 
## Df Sum Sq Mean Sq F value Pr(>F)
## Sweetener 2 1423 711.6 18.638 1.56e-08 ***
## Cup 9 5652 628.0 16.448 < 2e-16 ***
## Sweetener:Cup 18 41 2.3 0.059 1
## Residuals 500 19089 38.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## Cup 9 1694 188.27 2.606 0.00764 **
## Residuals 170 12280 72.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Intensity ~ Cup, data = .)
##
## $Cup
## diff lwr upr p adj
## 2-1 -7.81862507 -16.902129 1.2648792 0.1591151
## 3-1 -10.69455038 -19.778055 -1.6110461 0.0081955
## 4-1 -9.98575550 -19.069260 -0.9022512 0.0189425
## 5-1 -10.41390281 -19.497407 -1.3303985 0.0115057
## 6-1 -10.17827485 -19.261779 -1.0947705 0.0151821
## 7-1 -10.08243690 -19.165941 -0.9989326 0.0169603
## 8-1 -10.20155557 -19.285060 -1.1180513 0.0147765
## 9-1 -10.24021546 -19.323720 -1.1567111 0.0141245
## 10-1 -9.64122364 -18.724728 -0.5577193 0.0278060
## 3-2 -2.87592531 -11.959430 6.2075790 0.9910081
## 4-2 -2.16713042 -11.250635 6.9163739 0.9989535
## 5-2 -2.59527773 -11.678782 6.4882266 0.9957770
## 6-2 -2.35964977 -11.443154 6.7238545 0.9979586
## 7-2 -2.26381183 -11.347316 6.8196925 0.9985229
## 8-2 -2.38293050 -11.466435 6.7005738 0.9977973
## 9-2 -2.42159039 -11.505095 6.6619139 0.9975062
## 10-2 -1.82259857 -10.906103 7.2609057 0.9997425
## 4-3 0.70879488 -8.374709 9.7922992 0.9999999
## 5-3 0.28064757 -8.802857 9.3641519 1.0000000
## 6-3 0.51627553 -8.567229 9.5997799 1.0000000
## 7-3 0.61211347 -8.471391 9.6956178 1.0000000
## 8-3 0.49299481 -8.590510 9.5764991 1.0000000
## 9-3 0.45433492 -8.629169 9.5378392 1.0000000
## 10-3 1.05332674 -8.030178 10.1368311 0.9999976
## 5-4 -0.42814731 -9.511652 8.6553570 1.0000000
## 6-4 -0.19251935 -9.276024 8.8909850 1.0000000
## 7-4 -0.09668141 -9.180186 8.9868229 1.0000000
## 8-4 -0.21580008 -9.299304 8.8677042 1.0000000
## 9-4 -0.25445997 -9.337964 8.8290444 1.0000000
## 10-4 0.34453185 -8.738972 9.4280362 1.0000000
## 6-5 0.23562796 -8.847876 9.3191323 1.0000000
## 7-5 0.33146590 -8.752038 9.4149702 1.0000000
## 8-5 0.21234723 -8.871157 9.2958515 1.0000000
## 9-5 0.17368734 -8.909817 9.2571917 1.0000000
## 10-5 0.77267916 -8.310825 9.8561835 0.9999998
## 7-6 0.09583794 -8.987666 9.1793423 1.0000000
## 8-6 -0.02328073 -9.106785 9.0602236 1.0000000
## 9-6 -0.06194062 -9.145445 9.0215637 1.0000000
## 10-6 0.53705120 -8.546453 9.6205555 1.0000000
## 8-7 -0.11911867 -9.202623 8.9643856 1.0000000
## 9-7 -0.15777856 -9.241283 8.9257258 1.0000000
## 10-7 0.44121326 -8.642291 9.5247176 1.0000000
## 9-8 -0.03865989 -9.122164 9.0448444 1.0000000
## 10-8 0.56033193 -8.523172 9.6438362 1.0000000
## 10-9 0.59899182 -8.484512 9.6824961 1.0000000## Df Sum Sq Mean Sq F value Pr(>F)
## Cup 9 2155 239.42 11.01 1.95e-13 ***
## Residuals 170 3698 21.75
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Intensity ~ Cup, data = .)
##
## $Cup
## diff lwr upr p adj
## 2-1 -9.407108578 -14.391457 -4.422760 0.0000004
## 3-1 -11.403937054 -16.388286 -6.419588 0.0000000
## 4-1 -11.223219379 -16.207568 -6.238871 0.0000000
## 5-1 -11.459844059 -16.444193 -6.475495 0.0000000
## 6-1 -11.657441598 -16.641790 -6.673093 0.0000000
## 7-1 -11.796529176 -16.780878 -6.812180 0.0000000
## 8-1 -11.655163205 -16.639512 -6.670814 0.0000000
## 9-1 -11.637451538 -16.621800 -6.653103 0.0000000
## 10-1 -11.649563407 -16.633912 -6.665215 0.0000000
## 3-2 -1.996828476 -6.981177 2.987520 0.9558957
## 4-2 -1.816110800 -6.800460 3.168238 0.9761319
## 5-2 -2.052735480 -7.037084 2.931613 0.9476745
## 6-2 -2.250333020 -7.234682 2.734016 0.9101321
## 7-2 -2.389420598 -7.373769 2.594928 0.8752740
## 8-2 -2.248054627 -7.232403 2.736294 0.9106441
## 9-2 -2.230342960 -7.214692 2.754006 0.9145599
## 10-2 -2.242454829 -7.226804 2.741894 0.9118945
## 4-3 0.180717676 -4.803631 5.165066 1.0000000
## 5-3 -0.055907004 -5.040256 4.928442 1.0000000
## 6-3 -0.253504544 -5.237853 4.730844 1.0000000
## 7-3 -0.392592122 -5.376941 4.591757 0.9999999
## 8-3 -0.251226151 -5.235575 4.733123 1.0000000
## 9-3 -0.233514484 -5.217863 4.750834 1.0000000
## 10-3 -0.245626353 -5.229975 4.738722 1.0000000
## 5-4 -0.236624680 -5.220973 4.747724 1.0000000
## 6-4 -0.434222220 -5.418571 4.550127 0.9999998
## 7-4 -0.573309797 -5.557659 4.411039 0.9999978
## 8-4 -0.431943826 -5.416293 4.552405 0.9999998
## 9-4 -0.414232159 -5.398581 4.570117 0.9999999
## 10-4 -0.426344028 -5.410693 4.558005 0.9999998
## 6-5 -0.197597540 -5.181946 4.786751 1.0000000
## 7-5 -0.336685117 -5.321034 4.647664 1.0000000
## 8-5 -0.195319146 -5.179668 4.789030 1.0000000
## 9-5 -0.177607480 -5.161956 4.806741 1.0000000
## 10-5 -0.189719348 -5.174068 4.794629 1.0000000
## 7-6 -0.139087578 -5.123436 4.845261 1.0000000
## 8-6 0.002278393 -4.982070 4.986627 1.0000000
## 9-6 0.019990060 -4.964359 5.004339 1.0000000
## 10-6 0.007878191 -4.976471 4.992227 1.0000000
## 8-7 0.141365971 -4.842983 5.125715 1.0000000
## 9-7 0.159077638 -4.825271 5.143426 1.0000000
## 10-7 0.146965769 -4.837383 5.131315 1.0000000
## 9-8 0.017711667 -4.966637 5.002060 1.0000000
## 10-8 0.005599798 -4.978749 4.989949 1.0000000
## 10-9 -0.012111869 -4.996461 4.972237 1.0000000## Df Sum Sq Mean Sq F value Pr(>F)
## Cup 9 1843 204.80 10.53 9.98e-13 ***
## Residuals 160 3111 19.44
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Intensity ~ Cup, data = .)
##
## $Cup
## diff lwr upr p adj
## 2-1 -9.16971446 -14.022767 -4.316662 0.0000004
## 3-1 -10.47864884 -15.331701 -5.625596 0.0000000
## 4-1 -11.09025054 -15.943303 -6.237198 0.0000000
## 5-1 -10.97590042 -15.828953 -6.122848 0.0000000
## 6-1 -10.91294452 -15.765997 -6.059892 0.0000000
## 7-1 -11.16684816 -16.019901 -6.313796 0.0000000
## 8-1 -10.92330197 -15.776354 -6.070250 0.0000000
## 9-1 -11.05776856 -15.910821 -6.204716 0.0000000
## 10-1 -11.41088434 -16.263937 -6.557832 0.0000000
## 3-2 -1.30893438 -6.161987 3.544118 0.9972455
## 4-2 -1.92053609 -6.773588 2.932516 0.9588676
## 5-2 -1.80618596 -6.659238 3.046866 0.9723084
## 6-2 -1.74323006 -6.596282 3.109822 0.9781325
## 7-2 -1.99713371 -6.850186 2.855919 0.9475560
## 8-2 -1.75358751 -6.606640 3.099465 0.9772448
## 9-2 -1.88805410 -6.741106 2.964998 0.9630872
## 10-2 -2.24116988 -7.094222 2.611882 0.8974636
## 4-3 -0.61160171 -5.464654 4.241451 0.9999950
## 5-3 -0.49725158 -5.350304 4.355801 0.9999992
## 6-3 -0.43429568 -5.287348 4.418757 0.9999998
## 7-3 -0.68819933 -5.541252 4.164853 0.9999861
## 8-3 -0.44465313 -5.297705 4.408399 0.9999997
## 9-3 -0.57911972 -5.432172 4.273933 0.9999969
## 10-3 -0.93223550 -5.785288 3.920817 0.9998184
## 5-4 0.11435012 -4.738702 4.967402 1.0000000
## 6-4 0.17730603 -4.675746 5.030358 1.0000000
## 7-4 -0.07659762 -4.929650 4.776455 1.0000000
## 8-4 0.16694858 -4.686104 5.020001 1.0000000
## 9-4 0.03248198 -4.820570 4.885534 1.0000000
## 10-4 -0.32063379 -5.173686 4.532419 1.0000000
## 6-5 0.06295590 -4.790096 4.916008 1.0000000
## 7-5 -0.19094774 -5.044000 4.662105 1.0000000
## 8-5 0.05259845 -4.800454 4.905651 1.0000000
## 9-5 -0.08186814 -4.934920 4.771184 1.0000000
## 10-5 -0.43498392 -5.288036 4.418068 0.9999997
## 7-6 -0.25390365 -5.106956 4.599149 1.0000000
## 8-6 -0.01035745 -4.863410 4.842695 1.0000000
## 9-6 -0.14482404 -4.997876 4.708228 1.0000000
## 10-6 -0.49793982 -5.350992 4.355113 0.9999992
## 8-7 0.24354620 -4.609506 5.096599 1.0000000
## 9-7 0.10907960 -4.743973 4.962132 1.0000000
## 10-7 -0.24403618 -5.097089 4.609016 1.0000000
## 9-8 -0.13446659 -4.987519 4.718586 1.0000000
## 10-8 -0.48758237 -5.340635 4.365470 0.9999993
## 10-9 -0.35311578 -5.206168 4.499937 1.0000000## Df Sum Sq Mean Sq F value Pr(>F)
## Sweetener 2 67 33.61 0.265 0.769
## Residuals 50 6351 127.02## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Intensity ~ Sweetener, data = .)
##
## $Sweetener
## diff lwr upr p adj
## RebA-Glucose -2.2311912 -11.437876 6.975494 0.8285617
## Sucralose-Glucose -2.4958121 -11.570020 6.578396 0.7851134
## Sucralose-RebA -0.2646209 -9.471306 8.942064 0.9973464## Df Sum Sq Mean Sq F value Pr(>F)
## Sweetener 2 177.5 88.76 1.919 0.157
## Residuals 50 2313.1 46.26## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Intensity ~ Sweetener, data = .)
##
## $Sweetener
## diff lwr upr p adj
## RebA-Glucose -3.582281 -9.138523 1.973962 0.2735381
## Sucralose-Glucose -4.084296 -9.560588 1.391997 0.1795310
## Sucralose-RebA -0.502015 -6.058258 5.054228 0.9740995## Df Sum Sq Mean Sq F value Pr(>F)
## Sweetener 2 94.4 47.21 2.345 0.106
## Residuals 50 1006.5 20.13## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Intensity ~ Sweetener, data = .)
##
## $Sweetener
## diff lwr upr p adj
## RebA-Glucose -2.015290 -5.680424 1.6498451 0.3864891
## Sucralose-Glucose -3.205199 -6.817595 0.4071975 0.0914277
## Sucralose-RebA -1.189909 -4.855044 2.4752257 0.7143989## Df Sum Sq Mean Sq F value Pr(>F)
## Sweetener 2 150.4 75.18 2.964 0.0607 .
## Residuals 50 1268.2 25.36
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Intensity ~ Sweetener, data = .)
##
## $Sweetener
## diff lwr upr p adj
## RebA-Glucose -3.3356863 -7.449725 0.7783524 0.1332555
## Sucralose-Glucose -3.7332760 -7.788117 0.3215648 0.0768960
## Sucralose-RebA -0.3975897 -4.511628 3.7164490 0.9704257## Df Sum Sq Mean Sq F value Pr(>F)
## Sweetener 2 125 62.48 2.821 0.0691 .
## Residuals 50 1108 22.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Intensity ~ Sweetener, data = .)
##
## $Sweetener
## diff lwr upr p adj
## RebA-Glucose -2.7931888 -6.637756 1.0513786 0.1953420
## Sucralose-Glucose -3.5417533 -7.331000 0.2474937 0.0714152
## Sucralose-RebA -0.7485645 -4.593132 3.0960029 0.8855124Experiment 2: Detection threshold, JND, exposed JND reliability
Experiment 2: Detection threshold, JND, exposed JND difference
Experiment 3: Suprathreshold Reliability
Experiment 3: Suprathreshold DRC - Glucose
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.2207 -0.5119 -0.2196 0.2325 3.3959
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 43.198 20.4454 65.951
## Growth rate 5.533 -0.8604 11.926
## Midpoint at 2.768 2.5285 3.008
## Residual std err. 15.197 12.5113 17.883
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -271.71
## AIC: 549.41
## BIC: 555.98
##
## Optimization algorithm converged in 20 iterations
##
## Model fitted: Log-logistic (ED50 as parameter) (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## slope:(Intercept) -25.064210 10.691093 -2.3444 0.02222 *
## Max:(Intercept) 48.062262 11.025312 4.3593 4.917e-05 ***
## EC50:(Intercept) 2.916286 0.073259 39.8081 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 14.29746 (63 degrees of freedom)##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.5895 -0.5732 -0.2202 0.4063 3.1623
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.00 NA NA
## Maximum 46.78 28.454 65.114
## Growth rate 9.00 1.803 16.196
## Midpoint at 2.91 2.786 3.035
## Residual std err. 14.29 11.762 16.812
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -267.63
## AIC: 541.26
## BIC: 547.83
##
## Optimization algorithm converged in 19 iterations
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -2.0105 -0.6855 -0.1691 0.6067 3.4706
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000e+00 NA NA
## Maximum 4.433e+05 -2.274e+07 2.363e+07
## Growth rate 3.886e+00 2.385e+00 5.386e+00
## Midpoint at 5.608e+00 -7.900e+00 1.911e+01
## Residual std err. 1.194e+01 9.830e+00 1.405e+01
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -255.79
## AIC: 517.58
## BIC: 524.15
##
## Optimization algorithm converged in 785 iterations
Experiment 3: Suprathreshold DRC - Glucose: Fixed Top
##
## Model fitted: Log-logistic (ED50 as parameter) (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## slope:(Intercept) -14.753830 3.746298 -3.9382 0.0002053 ***
## EC50:(Intercept) 2.779221 0.040317 68.9347 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 15.07882 (64 degrees of freedom)##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.2207 -0.5119 -0.2196 0.2325 3.3959
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 43.198 20.4454 65.951
## Growth rate 5.533 -0.8604 11.926
## Midpoint at 2.768 2.5285 3.008
## Residual std err. 15.197 12.5113 17.883
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -271.71
## AIC: 549.41
## BIC: 555.98
##
## Optimization algorithm converged in 91 iterations
##
## Model fitted: Log-logistic (ED50 as parameter) (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## slope:(Intercept) -28.369183 6.979494 -4.0646 0.000134 ***
## EC50:(Intercept) 2.895697 0.025175 115.0215 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 14.19945 (64 degrees of freedom)##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.5307 -0.5668 -0.1917 0.4330 3.1586
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 44.670 30.555 58.785
## Growth rate 9.752 2.569 16.936
## Midpoint at 2.897 2.799 2.996
## Residual std err. 14.294 11.766 16.822
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -267.66
## AIC: 541.33
## BIC: 547.9
##
## Optimization algorithm converged in 33 iterations
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.7996 -0.6114 -0.1273 0.6296 3.6071
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 44.670 15.339 74.001
## Growth rate 6.597 2.373 10.822
## Midpoint at 3.044 2.826 3.262
## Residual std err. 12.105 9.948 14.261
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -256.69
## AIC: 519.38
## BIC: 525.95
##
## Optimization algorithm converged in 33 iterations
Experiment 3: Suprathreshold DRC - Sucralose
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -2.02039 -0.70372 -0.07711 0.55412 2.66829
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 41.415 28.9397 53.889
## Growth rate 3.644 0.9571 6.331
## Midpoint at 2.576 2.3489 2.803
## Residual std err. 10.944 9.0099 12.878
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -250.04
## AIC: 506.08
## BIC: 512.65
##
## Optimization algorithm converged in 23 iterations
##
## Model fitted: Log-logistic (ED50 as parameter) (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## slope:(Intercept) -9.50851 6.61556 -1.4373 0.1556
## Max:(Intercept) 44.82131 32.69372 1.3709 0.1753
## EC50:(Intercept) 2.96259 0.52408 5.6529 4.094e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 15.95378 (63 degrees of freedom)##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.6677 -0.4624 -0.0969 0.3067 3.3203
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 39.742 2.2274 77.256
## Growth rate 3.784 -0.8052 8.374
## Midpoint at 2.891 2.2621 3.520
## Residual std err. 15.951 13.1319 18.770
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -274.9
## AIC: 555.8
## BIC: 562.37
##
## Optimization algorithm converged in 24 iterations
##
## Model fitted: Log-logistic (ED50 as parameter) (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## slope:(Intercept) -3.2529 1.2156 -2.6759 0.009486 **
## Max:(Intercept) 354.2130 NA NA NA
## EC50:(Intercept) 8.1901 NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 12.22454 (63 degrees of freedom)##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.4139 -0.6542 -0.3330 0.2268 2.6805
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 60903.755 -3.150e+07 3.162e+07
## Growth rate 1.118 2.049e-01 2.031e+00
## Midpoint at 10.576 -4.539e+02 4.750e+02
## Residual std err. 12.221 1.006e+01 1.438e+01
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -257.33
## AIC: 520.65
## BIC: 527.22
##
## Optimization algorithm DID NOT converge in 10783 iterations
Experiment 3: Suprathreshold DRC - Sucralose: Fixed Top
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -2.02039 -0.70372 -0.07711 0.55412 2.66829
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 41.415 28.9397 53.889
## Growth rate 3.644 0.9571 6.331
## Midpoint at 2.576 2.3489 2.803
## Residual std err. 10.944 9.0099 12.878
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -250.04
## AIC: 506.08
## BIC: 512.65
##
## Optimization algorithm DID NOT converge in 10021 iterations
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.6677 -0.4624 -0.0969 0.3067 3.3203
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 39.742 2.2274 77.256
## Growth rate 3.784 -0.8052 8.374
## Midpoint at 2.891 2.2621 3.520
## Residual std err. 15.951 13.1319 18.770
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -274.9
## AIC: 555.8
## BIC: 562.37
##
## Optimization algorithm converged in 1724 iterations
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.3894 -0.6667 -0.3466 0.2218 2.6696
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 44.640 -200.315 289.594
## Growth rate 1.491 -1.485 4.468
## Midpoint at 3.596 -2.815 10.007
## Residual std err. 12.225 10.064 14.386
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -257.35
## AIC: 520.69
## BIC: 527.26
##
## Optimization algorithm DID NOT converge in 10019 iterations
Experiment 3: Suprathreshold DRC - RebA
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.88018 -0.75961 -0.05485 0.61124 3.07789
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 30.368 19.2984 41.437
## Growth rate 3.842 -0.8728 8.558
## Midpoint at 2.297 2.0617 2.531
## Residual std err. 10.978 9.0375 12.917
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -250.24
## AIC: 506.48
## BIC: 513.05
##
## Optimization algorithm converged in 14 iterations
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.5718 -0.6037 -0.1305 0.5580 3.0087
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 31.967 -72.1182 136.053
## Growth rate 2.219 -4.3531 8.791
## Midpoint at 2.746 -0.4059 5.897
## Residual std err. 13.392 11.0252 15.759
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -263.36
## AIC: 532.72
## BIC: 539.29
##
## Optimization algorithm converged in 27 iterations
##
## Model fitted: Log-logistic (ED50 as parameter) (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## slope:(Intercept) -1.9365 2.9672 -0.6526 0.5164
## Max:(Intercept) 84.9038 849.5231 0.0999 0.9207
## EC50:(Intercept) 7.5755 55.5154 0.1365 0.8919
##
## Residual standard error:
##
## 9.58849 (63 degrees of freedom)##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.3079 -0.8383 -0.1847 0.6246 1.9773
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000e+00 NA NA
## Maximum 3.211e+04 -1.576e+07 1.583e+07
## Growth rate 6.548e-01 -2.105e-01 1.520e+00
## Midpoint at 1.503e+01 -7.386e+02 7.687e+02
## Residual std err. 9.585e+00 7.891e+00 1.128e+01
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -241.29
## AIC: 488.58
## BIC: 495.15
##
## Optimization algorithm DID NOT converge in 10765 iterations
Experiment 3: Suprathreshold DRC - RebA: Fixed Top
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.88018 -0.75961 -0.05485 0.61124 3.07789
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 30.368 19.2984 41.437
## Growth rate 3.842 -0.8728 8.558
## Midpoint at 2.297 2.0617 2.531
## Residual std err. 10.978 9.0375 12.917
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -250.24
## AIC: 506.48
## BIC: 513.05
##
## Optimization algorithm converged in 1718 iterations
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.5690 -0.6014 -0.1292 0.5579 3.0078
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.000 NA NA
## Maximum 31.177 -59.67584 122.030
## Growth rate 2.270 -4.05244 8.592
## Midpoint at 2.722 -0.06001 5.503
## Residual std err. 13.392 11.02522 15.759
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -263.36
## AIC: 532.72
## BIC: 539.29
##
## Optimization algorithm DID NOT converge in 10033 iterations
##
## Call: drda(formula = geo.Int ~ log10(Conc.mM), data = ., mean_function = "logistic4",
## is_log = TRUE, lower_bound = lb, upper_bound = ub)
##
## Pearson Residuals:
## Min 1Q Median 3Q Max
## -1.2954 -0.8392 -0.1912 0.6317 1.9892
##
## Parameters:
## Estimate Lower .95 Upper .95
## Minimum 0.0000 NA NA
## Maximum 31.1443 -203.893 266.182
## Growth rate 0.9476 -2.475 4.371
## Midpoint at 3.4747 -11.077 18.026
## Residual std err. 9.5872 7.893 11.282
##
## Residual standard error on 63 degrees of freedom
##
## Log-likelihood: -241.3
## AIC: 488.61
## BIC: 495.17
##
## Optimization algorithm DID NOT converge in 10043 iterations
