Accessors for incidence_fit objects

get_fit(x)

# S3 method for incidence_fit
get_fit(x)

# S3 method for incidence_fit_list
get_fit(x)

get_info(x, what = "r", ...)

# S3 method for incidence_fit
get_info(x, what = "r", ...)

# S3 method for incidence_fit_list
get_info(x, what = "r", groups = NULL,
  na.rm = TRUE, ...)

Arguments

x

an incidence_fit or incidence_fit_list object.

what

the name of the item in the "info" element of the incidence_fit object.

...

currently unused.

groups

if what = "pred" and x is an incidence_fit_list object, then this indicates what part of the nesting hierarchy becomes the column named "groups". Defaults to NULL, indicating that no groups column will be added/modified.

na.rm

when TRUE (default), missing values will be excluded from the results.

Value

a list of incidence_fit objects.

Examples

if (require(outbreaks)) { withAutoprint({ dat <- ebola_sim$linelist$date_of_onset ## EXAMPLE WITH A SINGLE MODEL ## compute weekly incidence sex <- ebola_sim$linelist$gender i.sex <- incidence(dat, interval = 7, group = sex) ## Compute the optimal split for each group separately fits <- fit_optim_split(i.sex, separate_split = TRUE) ## `fits` contains an `incidence_fit_list` object fits$fit ## Grab the list of `incidence_fit` objects get_fit(fits$fit) ## Get the predictions for all groups get_info(fits$fit, "pred", groups = 1) ## Get the predictions, but set `groups` to "before" and "after" get_info(fits$fit, "pred", groups = 2) ## Get the reproduction number get_info(fits$fit, "r") ## Get the doubling confidence interval get_info(fits$fit, "doubling.conf") ## Get the halving confidence interval get_info(fits$fit, "halving.conf") })}
#> > dat <- ebola_sim$linelist$date_of_onset #> > sex <- ebola_sim$linelist$gender #> > i.sex <- incidence(dat, interval = 7, group = sex) #> > fits <- fit_optim_split(i.sex, separate_split = TRUE) #> > fits$fit #> <list of incidence_fit objects> #> #> attr(x, 'locations'): list of vectors with the locations of each incidence_fit object #> #> 'f', 'before' #> 'm', 'before' #> 'f', 'after' #> 'm', 'after' #> #> $model: regression of log-incidence over time #> #> $info: list containing the following items: #> $r (daily growth rate): #> f_before m_before f_after m_after #> 0.02570604 0.02883607 -0.01002297 -0.01038307 #> #> $r.conf (confidence interval): #> 2.5 % 97.5 % #> f_before 0.02289333 0.028518743 #> m_before 0.02502254 0.032649606 #> f_after -0.01102735 -0.009018595 #> m_after -0.01138910 -0.009377034 #> #> $doubling (doubling time in days): #> f_before m_before #> 26.96437 24.03750 #> #> $doubling.conf (confidence interval): #> 2.5 % 97.5 % #> f_before 24.30497 30.27725 #> m_before 21.22988 27.70091 #> #> $halving (halving time in days): #> f_after m_after #> 69.15586 66.75746 #> #> $halving.conf (confidence interval): #> 2.5 % 97.5 % #> f_after 62.85711 76.85756 #> m_after 60.86059 73.91966 #> #> $pred: data.frame of incidence predictions (111 rows, 7 columns) #> > get_fit(fits$fit) #> $f_before #> <incidence_fit object> #> #> $model: regression of log-incidence over time #> #> $info: list containing the following items: #> $r (daily growth rate): #> [1] 0.02570604 #> #> $r.conf (confidence interval): #> 2.5 % 97.5 % #> [1,] 0.02289333 0.02851874 #> #> $doubling (doubling time in days): #> [1] 26.96437 #> #> $doubling.conf (confidence interval): #> 2.5 % 97.5 % #> [1,] 24.30497 30.27725 #> #> $pred: data.frame of incidence predictions (24 rows, 6 columns) #> #> $m_before #> <incidence_fit object> #> #> $model: regression of log-incidence over time #> #> $info: list containing the following items: #> $r (daily growth rate): #> [1] 0.02883607 #> #> $r.conf (confidence interval): #> 2.5 % 97.5 % #> [1,] 0.02502254 0.03264961 #> #> $doubling (doubling time in days): #> [1] 24.0375 #> #> $doubling.conf (confidence interval): #> 2.5 % 97.5 % #> [1,] 21.22988 27.70091 #> #> $pred: data.frame of incidence predictions (22 rows, 6 columns) #> #> $f_after #> <incidence_fit object> #> #> $model: regression of log-incidence over time #> #> $info: list containing the following items: #> $r (daily growth rate): #> [1] -0.01002297 #> #> $r.conf (confidence interval): #> 2.5 % 97.5 % #> [1,] -0.01102735 -0.009018595 #> #> $halving (halving time in days): #> [1] 69.15586 #> #> $halving.conf (confidence interval): #> 2.5 % 97.5 % #> [1,] 62.85711 76.85756 #> #> $pred: data.frame of incidence predictions (32 rows, 6 columns) #> #> $m_after #> <incidence_fit object> #> #> $model: regression of log-incidence over time #> #> $info: list containing the following items: #> $r (daily growth rate): #> [1] -0.01038307 #> #> $r.conf (confidence interval): #> 2.5 % 97.5 % #> [1,] -0.0113891 -0.009377034 #> #> $halving (halving time in days): #> [1] 66.75746 #> #> $halving.conf (confidence interval): #> 2.5 % 97.5 % #> [1,] 60.86059 73.91966 #> #> $pred: data.frame of incidence predictions (33 rows, 6 columns) #> #> > get_info(fits$fit, "pred", groups = 1) #> dates dates.x fit lwr upr groups location #> f_before.1 2014-04-10 3.5 2.259928 1.705798 2.994069 f f_before #> f_before.2 2014-04-24 17.5 3.238848 2.528189 4.149268 f f_before #> f_before.3 2014-05-01 24.5 3.877381 3.075764 4.887919 f f_before #> f_before.4 2014-05-08 31.5 4.641800 3.739715 5.761484 f f_before #> f_before.5 2014-05-15 38.5 5.556923 4.543664 6.796143 f f_before #> f_before.6 2014-05-22 45.5 6.652460 5.515453 8.023861 f f_before #> f_before.7 2014-05-29 52.5 7.963981 6.687588 9.483986 f f_before #> f_before.8 2014-06-05 59.5 9.534066 8.097617 11.225329 f f_before #> f_before.9 2014-06-12 66.5 11.413690 9.788378 13.308879 f f_before #> f_before.10 2014-06-19 73.5 13.663880 11.808199 15.811184 f f_before #> f_before.11 2014-06-26 80.5 16.357689 14.211245 18.828329 f f_before #> f_before.12 2014-07-03 87.5 19.582579 17.058390 22.480282 f f_before #> f_before.13 2014-07-10 94.5 23.443250 20.419032 26.915379 f f_before #> f_before.14 2014-07-17 101.5 28.065046 24.373983 32.315063 f f_before #> f_before.15 2014-07-24 108.5 33.598021 29.019070 38.899489 f f_before #> f_before.16 2014-07-31 115.5 40.221812 34.468895 46.934899 f f_before #> f_before.17 2014-08-07 122.5 48.151471 40.860444 56.743490 f f_before #> f_before.18 2014-08-14 129.5 57.644449 48.356664 68.716125 f f_before #> f_before.19 2014-08-21 136.5 69.008952 57.150391 83.328133 f f_before #> f_before.20 2014-08-28 143.5 82.613945 67.468974 101.158556 f f_before #> f_before.21 2014-09-04 150.5 98.901140 79.579845 122.913478 f f_before #> f_before.22 2014-09-11 157.5 118.399326 93.797212 149.454341 f f_before #> f_before.23 2014-09-18 164.5 141.741546 110.490043 181.832365 f f_before #> f_before.24 2014-09-25 171.5 169.685643 130.091516 221.330479 f f_before #> m_before.1 2014-04-17 3.5 1.916403 1.349897 2.720652 m m_before #> m_before.2 2014-04-24 10.5 2.345040 1.690147 3.253688 m m_before #> m_before.3 2014-05-08 24.5 3.511374 2.644309 4.662748 m m_before #> m_before.4 2014-05-15 31.5 4.296754 3.303180 5.589188 m m_before #> m_before.5 2014-05-22 38.5 5.257798 4.121343 6.707630 m m_before #> m_before.6 2014-05-29 45.5 6.433797 5.134508 8.061871 m m_before #> m_before.7 2014-06-05 52.5 7.872828 6.384783 9.707679 m m_before #> m_before.8 2014-06-12 59.5 9.633724 7.921002 11.716780 m m_before #> m_before.9 2014-06-19 66.5 11.788475 9.798835 14.182110 m m_before #> m_before.10 2014-06-26 73.5 14.425174 12.080927 17.224312 m m_before #> m_before.11 2014-07-03 80.5 17.651617 14.837707 20.999174 m m_before #> m_before.12 2014-07-10 87.5 21.599711 18.149656 25.705585 m m_before #> m_before.13 2014-07-17 94.5 26.430865 22.111393 31.594148 m m_before #> m_before.14 2014-07-24 101.5 32.342591 26.837014 38.977629 m m_before #> m_before.15 2014-07-31 108.5 39.576578 32.465640 48.245023 m m_before #> m_before.16 2014-08-07 115.5 48.428573 39.166638 59.880726 m m_before #> m_before.17 2014-08-14 122.5 59.260472 47.144835 74.489678 m m_before #> m_before.18 2014-08-21 129.5 72.515116 56.646364 92.829294 m m_before #> m_before.19 2014-08-28 136.5 88.734392 67.965742 115.849428 m m_before #> m_before.20 2014-09-04 143.5 108.581394 81.454564 144.742276 m m_before #> m_before.21 2014-09-11 150.5 132.867525 97.532100 181.004809 m m_before #> m_before.22 2014-09-18 157.5 162.585675 116.698090 226.517004 m m_before #> f_after.1 2014-09-25 3.5 156.013412 137.427698 177.112656 f f_after #> f_after.2 2014-10-02 10.5 145.442552 128.885417 164.126682 f f_after #> f_after.3 2014-10-09 17.5 135.587932 120.859818 152.110831 f f_after #> f_after.4 2014-10-16 24.5 126.401023 113.318406 140.994028 f f_after #> f_after.5 2014-10-23 31.5 117.836583 106.230558 130.710602 f f_after #> f_after.6 2014-10-30 38.5 109.852436 99.567391 121.199898 f f_after #> f_after.7 2014-11-06 45.5 102.409263 93.301655 112.405908 f f_after #> f_after.8 2014-11-13 52.5 95.470411 87.407633 104.276929 f f_after #> f_after.9 2014-11-20 59.5 89.001709 81.861068 96.765219 f f_after #> f_after.10 2014-11-27 66.5 82.971300 76.639126 89.826660 f f_after #> f_after.11 2014-12-04 73.5 77.349489 71.720390 83.420397 f f_after #> f_after.12 2014-12-11 80.5 72.108589 67.084920 77.508458 f f_after #> f_after.13 2014-12-18 87.5 67.222793 62.714358 72.055332 f f_after #> f_after.14 2014-12-25 94.5 62.668039 58.592083 67.027538 f f_after #> f_after.15 2015-01-01 101.5 58.421897 54.703364 62.393202 f f_after #> f_after.16 2015-01-08 108.5 54.463457 51.035443 58.121729 f f_after #> f_after.17 2015-01-15 115.5 50.773226 47.577481 54.183627 f f_after #> f_after.18 2015-01-22 122.5 47.333030 44.320300 50.550555 f f_after #> f_after.19 2015-01-29 129.5 44.125929 41.255960 47.195547 f f_after #> f_after.20 2015-02-05 136.5 41.136128 38.377249 44.093339 f f_after #> f_after.21 2015-02-12 143.5 38.348905 35.677209 41.220671 f f_after #> f_after.22 2015-02-19 150.5 35.750532 33.148792 38.556475 f f_after #> f_after.23 2015-02-26 157.5 33.328216 30.784685 36.081902 f f_after #> f_after.24 2015-03-05 164.5 31.070026 28.577266 33.780226 f f_after #> f_after.25 2015-03-12 171.5 28.964842 26.518670 31.636658 f f_after #> f_after.26 2015-03-19 178.5 27.002298 24.600890 29.638117 f f_after #> f_after.27 2015-03-26 185.5 25.172727 22.815905 27.773003 f f_after #> f_after.28 2015-04-02 192.5 23.467122 21.155785 26.030979 f f_after #> f_after.29 2015-04-09 199.5 21.877081 19.612784 24.402791 f f_after #> f_after.30 2015-04-16 206.5 20.394775 18.179412 22.880106 f f_after #> f_after.31 2015-04-23 213.5 19.012905 16.848482 21.455379 f f_after #> f_after.32 2015-04-30 220.5 17.724665 15.613144 20.121747 f f_after #> m_after.1 2014-09-18 3.5 171.439964 150.372278 195.459307 m m_after #> m_after.2 2014-09-25 10.5 159.421506 140.672208 180.669779 m m_after #> m_after.3 2014-10-02 17.5 148.245579 131.582849 167.018360 m m_after #> m_after.4 2014-10-09 24.5 137.853117 123.064563 154.418798 m m_after #> m_after.5 2014-10-16 31.5 128.189198 115.080126 142.791559 m m_after #> m_after.6 2014-10-23 38.5 119.202750 107.594566 132.063320 m m_after #> m_after.7 2014-10-30 45.5 110.846278 100.575014 122.166499 m m_after #> m_after.8 2014-11-06 52.5 103.075620 93.990577 113.038815 m m_after #> m_after.9 2014-11-13 59.5 95.849709 87.812230 104.622861 m m_after #> m_after.10 2014-11-20 66.5 89.130356 82.012732 96.865694 m m_after #> m_after.11 2014-11-27 73.5 82.882049 76.566596 89.718421 m m_after #> m_after.12 2014-12-04 80.5 77.071768 71.450080 83.135770 m m_after #> m_after.13 2014-12-11 87.5 71.668805 66.641253 77.075645 m m_after #> m_after.14 2014-12-18 94.5 66.644606 62.120084 71.498672 m m_after #> m_after.15 2014-12-25 101.5 61.972618 57.868551 66.367749 m m_after #> m_after.16 2015-01-01 108.5 57.628151 53.870716 61.647664 m m_after #> m_after.17 2015-01-08 115.5 53.588244 50.112694 57.304841 m m_after #> m_after.18 2015-01-15 122.5 49.831547 46.582461 53.307254 m m_after #> m_after.19 2015-01-22 129.5 46.338206 43.269509 49.624535 m m_after #> m_after.20 2015-01-29 136.5 43.089758 40.164381 46.228204 m m_after #> m_after.21 2015-02-05 143.5 40.069036 37.258201 43.091926 m m_after #> m_after.22 2015-02-12 150.5 37.260076 34.542291 40.191696 m m_after #> m_after.23 2015-02-19 157.5 34.648032 32.007919 37.505911 m m_after #> m_after.24 2015-02-26 164.5 32.219101 29.646202 35.015294 m m_after #> m_after.25 2015-03-05 171.5 29.960445 27.448111 32.702733 m m_after #> m_after.26 2015-03-12 178.5 27.860128 25.404547 30.553062 m m_after #> m_after.27 2015-03-19 185.5 25.907049 23.506444 28.552817 m m_after #> m_after.28 2015-03-26 192.5 24.090887 21.744872 26.690009 m m_after #> m_after.29 2015-04-02 199.5 22.402043 20.111132 24.953917 m m_after #> m_after.30 2015-04-09 206.5 20.831592 18.596829 23.334905 m m_after #> m_after.31 2015-04-16 213.5 19.371235 17.193918 21.824272 m m_after #> m_after.32 2015-04-23 220.5 18.013253 15.894744 20.414124 m m_after #> m_after.33 2015-04-30 227.5 16.750470 14.692060 19.097270 m m_after #> > get_info(fits$fit, "pred", groups = 2) #> dates dates.x fit lwr upr groups location #> f_before.1 2014-04-10 3.5 2.259928 1.705798 2.994069 before f_before #> f_before.2 2014-04-24 17.5 3.238848 2.528189 4.149268 before f_before #> f_before.3 2014-05-01 24.5 3.877381 3.075764 4.887919 before f_before #> f_before.4 2014-05-08 31.5 4.641800 3.739715 5.761484 before f_before #> f_before.5 2014-05-15 38.5 5.556923 4.543664 6.796143 before f_before #> f_before.6 2014-05-22 45.5 6.652460 5.515453 8.023861 before f_before #> f_before.7 2014-05-29 52.5 7.963981 6.687588 9.483986 before f_before #> f_before.8 2014-06-05 59.5 9.534066 8.097617 11.225329 before f_before #> f_before.9 2014-06-12 66.5 11.413690 9.788378 13.308879 before f_before #> f_before.10 2014-06-19 73.5 13.663880 11.808199 15.811184 before f_before #> f_before.11 2014-06-26 80.5 16.357689 14.211245 18.828329 before f_before #> f_before.12 2014-07-03 87.5 19.582579 17.058390 22.480282 before f_before #> f_before.13 2014-07-10 94.5 23.443250 20.419032 26.915379 before f_before #> f_before.14 2014-07-17 101.5 28.065046 24.373983 32.315063 before f_before #> f_before.15 2014-07-24 108.5 33.598021 29.019070 38.899489 before f_before #> f_before.16 2014-07-31 115.5 40.221812 34.468895 46.934899 before f_before #> f_before.17 2014-08-07 122.5 48.151471 40.860444 56.743490 before f_before #> f_before.18 2014-08-14 129.5 57.644449 48.356664 68.716125 before f_before #> f_before.19 2014-08-21 136.5 69.008952 57.150391 83.328133 before f_before #> f_before.20 2014-08-28 143.5 82.613945 67.468974 101.158556 before f_before #> f_before.21 2014-09-04 150.5 98.901140 79.579845 122.913478 before f_before #> f_before.22 2014-09-11 157.5 118.399326 93.797212 149.454341 before f_before #> f_before.23 2014-09-18 164.5 141.741546 110.490043 181.832365 before f_before #> f_before.24 2014-09-25 171.5 169.685643 130.091516 221.330479 before f_before #> m_before.1 2014-04-17 3.5 1.916403 1.349897 2.720652 before m_before #> m_before.2 2014-04-24 10.5 2.345040 1.690147 3.253688 before m_before #> m_before.3 2014-05-08 24.5 3.511374 2.644309 4.662748 before m_before #> m_before.4 2014-05-15 31.5 4.296754 3.303180 5.589188 before m_before #> m_before.5 2014-05-22 38.5 5.257798 4.121343 6.707630 before m_before #> m_before.6 2014-05-29 45.5 6.433797 5.134508 8.061871 before m_before #> m_before.7 2014-06-05 52.5 7.872828 6.384783 9.707679 before m_before #> m_before.8 2014-06-12 59.5 9.633724 7.921002 11.716780 before m_before #> m_before.9 2014-06-19 66.5 11.788475 9.798835 14.182110 before m_before #> m_before.10 2014-06-26 73.5 14.425174 12.080927 17.224312 before m_before #> m_before.11 2014-07-03 80.5 17.651617 14.837707 20.999174 before m_before #> m_before.12 2014-07-10 87.5 21.599711 18.149656 25.705585 before m_before #> m_before.13 2014-07-17 94.5 26.430865 22.111393 31.594148 before m_before #> m_before.14 2014-07-24 101.5 32.342591 26.837014 38.977629 before m_before #> m_before.15 2014-07-31 108.5 39.576578 32.465640 48.245023 before m_before #> m_before.16 2014-08-07 115.5 48.428573 39.166638 59.880726 before m_before #> m_before.17 2014-08-14 122.5 59.260472 47.144835 74.489678 before m_before #> m_before.18 2014-08-21 129.5 72.515116 56.646364 92.829294 before m_before #> m_before.19 2014-08-28 136.5 88.734392 67.965742 115.849428 before m_before #> m_before.20 2014-09-04 143.5 108.581394 81.454564 144.742276 before m_before #> m_before.21 2014-09-11 150.5 132.867525 97.532100 181.004809 before m_before #> m_before.22 2014-09-18 157.5 162.585675 116.698090 226.517004 before m_before #> f_after.1 2014-09-25 3.5 156.013412 137.427698 177.112656 after f_after #> f_after.2 2014-10-02 10.5 145.442552 128.885417 164.126682 after f_after #> f_after.3 2014-10-09 17.5 135.587932 120.859818 152.110831 after f_after #> f_after.4 2014-10-16 24.5 126.401023 113.318406 140.994028 after f_after #> f_after.5 2014-10-23 31.5 117.836583 106.230558 130.710602 after f_after #> f_after.6 2014-10-30 38.5 109.852436 99.567391 121.199898 after f_after #> f_after.7 2014-11-06 45.5 102.409263 93.301655 112.405908 after f_after #> f_after.8 2014-11-13 52.5 95.470411 87.407633 104.276929 after f_after #> f_after.9 2014-11-20 59.5 89.001709 81.861068 96.765219 after f_after #> f_after.10 2014-11-27 66.5 82.971300 76.639126 89.826660 after f_after #> f_after.11 2014-12-04 73.5 77.349489 71.720390 83.420397 after f_after #> f_after.12 2014-12-11 80.5 72.108589 67.084920 77.508458 after f_after #> f_after.13 2014-12-18 87.5 67.222793 62.714358 72.055332 after f_after #> f_after.14 2014-12-25 94.5 62.668039 58.592083 67.027538 after f_after #> f_after.15 2015-01-01 101.5 58.421897 54.703364 62.393202 after f_after #> f_after.16 2015-01-08 108.5 54.463457 51.035443 58.121729 after f_after #> f_after.17 2015-01-15 115.5 50.773226 47.577481 54.183627 after f_after #> f_after.18 2015-01-22 122.5 47.333030 44.320300 50.550555 after f_after #> f_after.19 2015-01-29 129.5 44.125929 41.255960 47.195547 after f_after #> f_after.20 2015-02-05 136.5 41.136128 38.377249 44.093339 after f_after #> f_after.21 2015-02-12 143.5 38.348905 35.677209 41.220671 after f_after #> f_after.22 2015-02-19 150.5 35.750532 33.148792 38.556475 after f_after #> f_after.23 2015-02-26 157.5 33.328216 30.784685 36.081902 after f_after #> f_after.24 2015-03-05 164.5 31.070026 28.577266 33.780226 after f_after #> f_after.25 2015-03-12 171.5 28.964842 26.518670 31.636658 after f_after #> f_after.26 2015-03-19 178.5 27.002298 24.600890 29.638117 after f_after #> f_after.27 2015-03-26 185.5 25.172727 22.815905 27.773003 after f_after #> f_after.28 2015-04-02 192.5 23.467122 21.155785 26.030979 after f_after #> f_after.29 2015-04-09 199.5 21.877081 19.612784 24.402791 after f_after #> f_after.30 2015-04-16 206.5 20.394775 18.179412 22.880106 after f_after #> f_after.31 2015-04-23 213.5 19.012905 16.848482 21.455379 after f_after #> f_after.32 2015-04-30 220.5 17.724665 15.613144 20.121747 after f_after #> m_after.1 2014-09-18 3.5 171.439964 150.372278 195.459307 after m_after #> m_after.2 2014-09-25 10.5 159.421506 140.672208 180.669779 after m_after #> m_after.3 2014-10-02 17.5 148.245579 131.582849 167.018360 after m_after #> m_after.4 2014-10-09 24.5 137.853117 123.064563 154.418798 after m_after #> m_after.5 2014-10-16 31.5 128.189198 115.080126 142.791559 after m_after #> m_after.6 2014-10-23 38.5 119.202750 107.594566 132.063320 after m_after #> m_after.7 2014-10-30 45.5 110.846278 100.575014 122.166499 after m_after #> m_after.8 2014-11-06 52.5 103.075620 93.990577 113.038815 after m_after #> m_after.9 2014-11-13 59.5 95.849709 87.812230 104.622861 after m_after #> m_after.10 2014-11-20 66.5 89.130356 82.012732 96.865694 after m_after #> m_after.11 2014-11-27 73.5 82.882049 76.566596 89.718421 after m_after #> m_after.12 2014-12-04 80.5 77.071768 71.450080 83.135770 after m_after #> m_after.13 2014-12-11 87.5 71.668805 66.641253 77.075645 after m_after #> m_after.14 2014-12-18 94.5 66.644606 62.120084 71.498672 after m_after #> m_after.15 2014-12-25 101.5 61.972618 57.868551 66.367749 after m_after #> m_after.16 2015-01-01 108.5 57.628151 53.870716 61.647664 after m_after #> m_after.17 2015-01-08 115.5 53.588244 50.112694 57.304841 after m_after #> m_after.18 2015-01-15 122.5 49.831547 46.582461 53.307254 after m_after #> m_after.19 2015-01-22 129.5 46.338206 43.269509 49.624535 after m_after #> m_after.20 2015-01-29 136.5 43.089758 40.164381 46.228204 after m_after #> m_after.21 2015-02-05 143.5 40.069036 37.258201 43.091926 after m_after #> m_after.22 2015-02-12 150.5 37.260076 34.542291 40.191696 after m_after #> m_after.23 2015-02-19 157.5 34.648032 32.007919 37.505911 after m_after #> m_after.24 2015-02-26 164.5 32.219101 29.646202 35.015294 after m_after #> m_after.25 2015-03-05 171.5 29.960445 27.448111 32.702733 after m_after #> m_after.26 2015-03-12 178.5 27.860128 25.404547 30.553062 after m_after #> m_after.27 2015-03-19 185.5 25.907049 23.506444 28.552817 after m_after #> m_after.28 2015-03-26 192.5 24.090887 21.744872 26.690009 after m_after #> m_after.29 2015-04-02 199.5 22.402043 20.111132 24.953917 after m_after #> m_after.30 2015-04-09 206.5 20.831592 18.596829 23.334905 after m_after #> m_after.31 2015-04-16 213.5 19.371235 17.193918 21.824272 after m_after #> m_after.32 2015-04-23 220.5 18.013253 15.894744 20.414124 after m_after #> m_after.33 2015-04-30 227.5 16.750470 14.692060 19.097270 after m_after #> > get_info(fits$fit, "r") #> f_before m_before f_after m_after #> 0.02570604 0.02883607 -0.01002297 -0.01038307 #> > get_info(fits$fit, "doubling.conf") #> 2.5 % 97.5 % #> f_before 24.30497 30.27725 #> m_before 21.22988 27.70091 #> > get_info(fits$fit, "halving.conf") #> 2.5 % 97.5 % #> f_after 62.85711 76.85756 #> m_after 60.86059 73.91966