This function performs the nonlinear least squares (NLS) regression method for the cosine model. It fits the NLS method as required, and then computes different quantities for the birth seasonality estimates corresponding to different individuals.
makeFits( paths, amplitude = NULL, intercept = NULL, method = c("OLS", "initial") )
| paths | A list of data frames, where each frame contains the data for one individual. Every data frame should have two columns with names 'distance' and 'oxygen'. |
|---|---|
| amplitude | Initial value for the amplitude parameter for the |
| intercept | Initial value for the intercept parameter for the |
| method | A character string giving the initialization for the nonlinear least squares regression. This must be either |
A data frame containing the following components:
estimated amplitude
estimated intercept
delay of the data
period of the data
birth seasonality estimate
predicted minimum for the oxygen isotope variable
predicted maximum for the oxygen isotope variable
observed minimum for the oxygen isotope variable
observed minimum for the oxygen isotope variable
mean squared error corresponding to the model fit for every individual
Pearson's R^2 corresponding to the model fit for every individual
armenia_split = split(armenia,f = armenia$ID) amp = seq(1,10,by=0.5) int = seq(-25,0,by=0.5) makeFits(armenia_split,amp[1],int[1],method = "initial")#> Warning: step factor 0.000488281 reduced below 'minFactor' of 0.000976562#> Warning: step factor 0.000488281 reduced below 'minFactor' of 0.000976562#> Warning: step factor 0.000488281 reduced below 'minFactor' of 0.000976562#> Warning: step factor 0.000488281 reduced below 'minFactor' of 0.000976562#> amplitude intercept x0 X birth predictedMin #> 1 1.827734e+00 -7.647449 87.7098935 145.64104 0.60223335 -9.475183 #> 2 3.991127e+03 -3976.413482 3.9153567 14.77674 0.26496761 -7967.540201 #> 3 3.324663e+00 -6.738144 9.0470441 22.62968 0.39978671 -10.062807 #> 4 1.465846e+00 -8.480712 182.0684115 252.28524 0.72167682 -9.946558 #> 5 6.991007e-01 -4.026564 9.9913090 32.96119 0.30312347 -4.725665 #> 6 4.179477e+00 -5.118462 19.5194126 33.51153 0.58246853 -9.297939 #> 7 1.586247e+00 -9.522778 3.4181413 11.58800 0.29497240 -11.109025 #> 8 3.336780e+00 -6.145026 10.4120192 33.13019 0.31427588 -9.481806 #> 9 3.430782e+00 -6.294009 9.1120997 29.38081 0.31013778 -9.724791 #> 10 3.462499e+00 -6.380319 17.7009496 33.13924 0.53413874 -9.842818 #> 11 3.799398e+00 -6.302004 12.6856187 33.51430 0.37851356 -10.101402 #> 12 4.937785e+00 -7.423759 12.3397881 35.36799 0.34889703 -12.361544 #> 13 3.373132e+00 -5.604018 16.5363565 22.30952 0.74122408 -8.977150 #> 14 4.680810e+00 -6.338310 8.8111855 25.27965 0.34854862 -11.019120 #> 15 4.322567e+00 -8.183361 0.8511138 30.41352 0.02798472 -12.505928 #> 16 3.119539e+00 -6.700140 3.8342339 35.44646 0.10816972 -9.819679 #> 17 3.366478e+00 -6.120014 10.1234903 33.96789 0.29803119 -9.486492 #> 18 3.648866e+00 -5.760416 11.1946164 30.09531 0.37197209 -9.409282 #> 19 4.747568e+00 -7.322763 11.9597359 34.02145 0.35153515 -12.070331 #> 20 4.027442e+00 -7.389552 11.1089044 32.89129 0.33774607 -11.416994 #> 21 3.222196e+00 -6.548946 9.7099414 29.49495 0.32920688 -9.771141 #> 22 1.995941e-02 -4.397856 31.0258285 33.80784 0.91771111 -4.417815 #> 23 4.009349e+00 -5.498994 12.1927893 30.00243 0.40639335 -9.508344 #> 24 4.152821e+00 -5.817953 31.2650834 40.13792 0.77894133 -9.970773 #> predictedMax observedMin observedMax MSE Pearson #> 1 -5.8197151 -9.28 -2.48 1.529117e+01 0.9686447 #> 2 14.7132377 -11.34 -0.09 2.676534e+07 -0.2328175 #> 3 -3.4134814 -9.52 -2.42 2.470694e-01 0.9752471 #> 4 -7.0148657 -10.18 -0.69 2.017519e+01 0.9046926 #> 5 -3.3274637 -4.65 -2.90 2.070901e-01 0.7365597 #> 6 -0.9389847 -10.07 -0.47 6.031176e-01 0.9716171 #> 7 -7.9365314 -12.30 -6.22 2.114833e+00 0.6120182 #> 8 -2.8082454 -9.37 -1.90 4.773146e-01 0.9559358 #> 9 -2.8632274 -9.69 -3.15 9.364418e-02 0.9924722 #> 10 -2.9178197 -9.36 -2.36 2.164370e-01 0.9775559 #> 11 -2.5026067 -7.88 -2.47 7.582879e-02 0.9897773 #> 12 -2.4859740 -11.52 -2.73 8.274287e-02 0.9954652 #> 13 -2.2308865 -9.66 -2.88 5.889335e-01 0.9545514 #> 14 -1.6575007 -8.04 -1.92 1.976594e-01 0.9833570 #> 15 -3.8607932 -12.33 -3.89 1.933057e-01 0.9889660 #> 16 -3.5806005 -9.58 -3.19 1.238112e-01 0.9879239 #> 17 -2.7535360 -9.41 -2.89 1.258966e-01 0.9893759 #> 18 -2.1115501 -9.51 -2.22 1.582954e-01 0.9882583 #> 19 -2.5751949 -10.29 -2.56 2.697012e-01 0.9795975 #> 20 -3.3621106 -11.45 -3.59 1.056773e-01 0.9933704 #> 21 -3.3267502 -8.41 -3.51 3.810623e-02 0.9933530 #> 22 -4.3778962 -9.40 -1.41 9.161082e+00 -0.9222437 #> 23 -1.4896450 -8.81 -1.52 3.215473e-01 0.9790757 #> 24 -1.6651319 -10.14 -2.93 1.787485e-01 0.9841492makeFits(armenia_split, method = "OLS")#> Warning: step factor 0.000488281 reduced below 'minFactor' of 0.000976562#> amplitude intercept x0 X birth predictedMin #> 1 5.2652994 -4.248525 33.2708773 37.91834 0.87743496 -9.513825 #> 2 5.9191442 -5.714313 0.7847776 35.38069 0.02218096 -11.633457 #> 3 3.3246623 -6.738144 9.0470374 22.62969 0.39978616 -10.062806 #> 4 4.7367622 -5.509659 3.2810551 37.95713 0.08644108 -10.246421 #> 5 0.6991006 -4.026565 9.9913045 32.96120 0.30312318 -4.725665 #> 6 4.1794786 -5.118464 19.5194190 33.51158 0.58246789 -9.297942 #> 7 0.2239461 -9.845539 14.1014168 147.81603 0.09539843 -10.069485 #> 8 3.3367784 -6.145026 10.4120107 33.13023 0.31427521 -9.481804 #> 9 3.4307832 -6.294008 9.1121024 29.38078 0.31013816 -9.724792 #> 10 3.4624988 -6.380318 17.7009474 33.13923 0.53413878 -9.842817 #> 11 3.7993884 -6.301994 12.6856178 33.51424 0.37851425 -10.101382 #> 12 4.9377793 -7.423751 12.3397890 35.36795 0.34889751 -12.361531 #> 13 3.3731318 -5.604018 16.5363477 22.30952 0.74122376 -8.977150 #> 14 4.6807720 -6.338269 8.8111826 25.27949 0.34855063 -11.019041 #> 15 4.3225696 -8.183357 0.8511012 30.41354 0.02798428 -12.505927 #> 16 3.1195383 -6.700140 3.8342428 35.44646 0.10816997 -9.819679 #> 17 3.3664785 -6.120014 10.1234946 33.96788 0.29803139 -9.486493 #> 18 3.6488657 -5.760415 11.1946145 30.09531 0.37197210 -9.409281 #> 19 4.7475694 -7.322764 11.9597362 34.02146 0.35153508 -12.070334 #> 20 4.0274413 -7.389551 11.1089040 32.89129 0.33774612 -11.416993 #> 21 3.2221892 -6.548938 9.7099434 29.49489 0.32920766 -9.771127 #> 22 3.9674723 -5.180679 15.7330178 26.45066 0.59480626 -9.148151 #> 23 4.0093494 -5.498993 12.1927934 30.00242 0.40639365 -9.508343 #> 24 4.1528320 -5.817939 31.2651341 40.13802 0.77894059 -9.970771 #> predictedMax observedMin observedMax MSE Pearson #> 1 1.0167739 -9.28 -2.48 0.13712714 0.9897939 #> 2 0.2048314 -11.34 -0.09 0.10486315 0.9969164 #> 3 -3.4134819 -9.52 -2.42 0.24706940 0.9752471 #> 4 -0.7728966 -10.18 -0.69 0.17725811 0.9925065 #> 5 -3.3274640 -4.65 -2.90 0.20709011 0.7365597 #> 6 -0.9389852 -10.07 -0.47 0.60311759 0.9716171 #> 7 -9.6215924 -12.30 -6.22 3.35497392 0.7266128 #> 8 -2.8082472 -9.37 -1.90 0.47731459 0.9559358 #> 9 -2.8632250 -9.69 -3.15 0.09364418 0.9924722 #> 10 -2.9178194 -9.36 -2.36 0.21643700 0.9775559 #> 11 -2.5026056 -7.88 -2.47 0.07582879 0.9897773 #> 12 -2.4859720 -11.52 -2.73 0.08274287 0.9954652 #> 13 -2.2308865 -9.66 -2.88 0.58893347 0.9545514 #> 14 -1.6574971 -8.04 -1.92 0.19765941 0.9833570 #> 15 -3.8607874 -12.33 -3.89 0.19330565 0.9889660 #> 16 -3.5806022 -9.58 -3.19 0.12381115 0.9879239 #> 17 -2.7535356 -9.41 -2.89 0.12589665 0.9893759 #> 18 -2.1115496 -9.51 -2.22 0.15829539 0.9882583 #> 19 -2.5751950 -10.29 -2.56 0.26970116 0.9795975 #> 20 -3.3621102 -11.45 -3.59 0.10567727 0.9933704 #> 21 -3.3267489 -8.41 -3.51 0.03810623 0.9933530 #> 22 -1.2132066 -9.40 -1.41 0.10895676 0.9938152 #> 23 -1.4896439 -8.81 -1.52 0.32154728 0.9790757 #> 24 -1.6651069 -10.14 -2.93 0.17874849 0.9841492