Updating vignettes following suggestions.

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mortAAR
 Type: Package
 Title: Analysis of Archaeological Mortality Data
-Version: 1.0.2
+Version: 1.1.0
 Authors@R: c(
   person("Nils", "Mueller-Scheessel", email = "[email protected]", role = c("aut", "cre", "cph")), 
   person("Martin", "Hinz", email = "[email protected]", role = c("aut")),
@@ -19,7 +19,7 @@ Description: A collection of functions for the analysis of archaeological mortal
     <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). 
     It takes demographic data in different formats and displays the result in a standard life table
     as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population).
-Date: 2019-07-31
+Date: 2020-12-01
 License: GPL-3 | file LICENSE
 Encoding: UTF-8
 LazyData: true

NEWS.md

@@ -1,4 +1,4 @@
-# mortAAR master
+# mortAAR 1.1
 - adding an option for plotting in color instead of linetype
 - adding more functions for checking representativity of age data, calculating reproduction indices, masculinity index, maternal mortality rate as well as corrected life tables
 - major re-factoring

inst/REFERENCES.bib

@@ -484,11 +484,15 @@ @Article{van_gerven_armelagos_rumors_1983
 
 @article{weiss_demography_1973,
   title = {{Demographic Models for Anthropology}},
-  volume = {38},
-  journal = {American Antiquity},
-  author = {Weiss, Kenneth M.},
-  year = {1973},
-  pages = {1--186}
+ author = {Weiss, Kenneth M. and Wobst, H. Martin},
+ year = {1973},
+ journal = {Memoirs of the Society for American Archaeology},
+ pages = {i-186},
+ publisher = {{Society for American Archaeology}},
+ issn = {0081-1300},
+ eprint = {25146719},
+ eprinttype = {jstor},
+ volume = {27}
 }
 
 @Book{wiedmer-stern_graeberfeld_1908,

vignettes/mortAAR_vignette_extended.Rmd

@@ -242,7 +242,7 @@ As a last step for our analysis, we will compare the curves of the life tables o
 
 #### Constructing the life table
 
-The output of the function `prep.life.table` can be addressed separately by their grouping names. We use this option to collect the results of the above examples Münsingen-Rain and Magdalenenberg. For 'Magdalenenberg' with no grouping variable the name is "Deceased" by default. Because we want to have comparable data from Münsingen-Rain we choose the output for all individuals ("All" by default).  
+The output of the function `prep.life.table` can be addressed separately by their grouping names. We use this option to collect the results of the above examples Münsingen-Rain and Magdalenenberg. For 'Magdalenenberg' with no grouping variable the name of the corresponding `data.frame` within the list of results is "Deceased" by default. Because we want to have comparable data from Münsingen-Rain we choose the output for all individuals (`data.frame` named "All" by default).  
 
 ```{r}
 comp <- list(mag_prep$Deceased, muen_prep$All)  

vignettes/mortAAR_vignette_lt_correction.Rmd

@@ -28,7 +28,7 @@ library(magrittr)
 
 Due to the nature of most anthropological ageing methods, life tables from archaeological series often contain artificial jumps in the data. To counteract this effect, mortAAR provides the option to interpolate values for adults by a monotonic cubic spline. Usual options will by '10', '15' or '20' which will interpolate the values for individuals of an age of 20 or older by 10-, 15- or 20-year cumulated values. This is to be used carefully, as diagnostic features of the life table might be smoothed and essentially removed. This option is only available when the methods `Standard` or `Equal5` in `prep.life.table` have been chosen.
 
-As an example, we work with the Early Neolithic cemetery of Nitra. First, we prepare the data, calculate the life table and plot d<sub>x</sub>- and q<sub>x</sub>-diagrams. The age-classes 25--44 show a bimodal distribution which might be artifical. Furthermore, the curve sharply drops after 40 which also looks very unnatural.
+As an example, we work with the Early Neolithic cemetery of Nitra. First, we prepare the data, calculate the life table and plot d<sub>x</sub>- and q<sub>x</sub>-diagrams. The age-classes 25-44 show a bimodal distribution which might be artifical. Furthermore, the curve sharply drops after 40 which also looks very unnatural.
 
 ```{r}
 nitra_prep <- prep.life.table(nitra, method="Equal5", agebeg = "age_start", ageend = "age_end")
@@ -51,9 +51,9 @@ As expected, applying the option leads to much smoother curves. However, for the
 
 Anthropological data from archaeological contexts is necessarily fragmentary. The question remains if this fragmentation leads to completely unreliable inferences when statistical methods are applied to it. K. M. Weiss [-@weiss_demography_1973, 46f.] and C. Masset and J.-P. Bocquet-Appel [-@masset_bocquet_1977; see also @herrmann_prahistorische_1990, 306f.] have therefore devised indices which check if the non-adult age groups are represented in proportions as can be expected from modern comparable data. Whether this is really applicable to archaeological data-sets is a matter of debate.
 
-Weiss chose the mortality (q<sub>x</sub>) as deciding factor and claimed that (1) the probability of death of the age group 10--15 (<sub>5</sub>q<sub>10</sub>) should be lower than that of the group 15--20 (<sub>5</sub>q<sub>15</sub>) and that (2) the latter in turn should be lower than that of age group 0--5 (<sub>5</sub>q<sub>0</sub>).
+Weiss chose the mortality (q<sub>x</sub>) as deciding factor and claimed that (1) the probability of death of the age group 10-15 (<sub>5</sub>q<sub>10</sub>) should be lower than that of the group 15-20 (<sub>5</sub>q<sub>15</sub>) and that (2) the latter in turn should be lower than that of age group 0-5 (<sub>5</sub>q<sub>0</sub>).
 
-In contrast, Bocquet-Appel and Masset took the raw number of dead (D<sub>x</sub>) and asserted that (1) the ratio of those having died between 5 and 10 (<sub>5</sub>D<sub>5</sub>) and those died between 10 and 15 years (<sub>5</sub>D<sub>10</sub>) should be equal or larger than 2 and that (2) the ratio of those having died between 5 and 15 (<sub>10</sub>D<sub>5</sub>) and all adults (>= 20; D<sub>20+</sub>) should be 0.1 or larger.
+In contrast, Bocquet-Appel and Masset took the raw number of dead (D<sub>x</sub>) and asserted that (1) the ratio of those having died between 5 and 10 (D<sub>5-10</sub>) and those died between 10 and 15 years (D<sub>5-10</sub>) should be equal or larger than 2 and that (2) the ratio of those having died between 5 and 15 (D<sub>5-15</sub>) and all adults (>= 20; D<sub>20+</sub>) should be 0.1 or larger.
 
 If either of these prerequisites is not met, the results from such data should be treated with extreme caution as the mortality structure is different from that of known populations. Due to the specific nature of the indices, they only give meaningful results if 5-year-age categories have been chosen for the non-adults.
 
@@ -96,6 +96,6 @@ Apart from the corrected life table, it also lists -- as separate data.frame --
 life.table(schleswig_ma[c("a", "Dx")])
 ```
 
-With the uncorrected data, the youngest age group (years 0--4) 'only' comprises around 20% of the population. However, according to the formulas by Masset and Bocquet-Appel, this number should be more than doubled (46%). This would mean that in reality nearly 50% of the individuals died before they reached their 5^th^ birthday. Applying this value to the life table, the number of individuals increases from 247 to 368.1 and at the same time the life expectancy at birth decreases from 30.7 to 21.1 years. Please note that this value differs somehow from that which is computed by the formula by Masset and Bocquet-Appel (22.5 years). This is easily explainable as the life expectancy of the life table includes _all_ individuals. However, the value of 21.1 years is still in the range of 21.0 to 24.1 years (see indices) given by Masset and Bocquet-Appel.
+With the uncorrected data, the youngest age group (years 0--4) 'only' comprises around 20% of the population. However, according to the formulas by Masset and Bocquet-Appel, this number should be more than doubled (46%). This would mean that in reality nearly 50% of the individuals died before they reached their 5^th^ birthday. Applying this value to the life table, the number of individuals increases from 247 to 368 and at the same time the life expectancy at birth decreases from 30.7 to 21.1 years. Please note that this value differs somehow from that which is computed by the formula by Masset and Bocquet-Appel (22.5 years). This is easily explainable as the life expectancy of the life table includes _all_ individuals. However, the value of 21.1 years is still in the range of 21.0 to 24.1 years (see indices) given by Masset and Bocquet-Appel.
 
 ## References

vignettes/mortAAR_vignette_reproduction.Rmd

@@ -77,7 +77,7 @@ As stated above, the index assumes, somehow unrealistically, that the youngest a
 lt.reproduction(schleswig, fertility_rate = "BA_log", growth_rate = "fertility")[c(-1,-2),]
 ```
 
-On contrast, the log-regression derived from the $P_{5-19}$-index by Bocquet-Appel arrives at a Total fertility rate of around 7, also in line with the expectation (see above). This higher value, not surprisingly, leads to much higher numbers in all the other indices, including a much higher Rate of natural increase and a much shorter doubling time of only 26.4 years.
+On contrast, the log-regression derived from the $P_{5-19}$-index by Bocquet-Appel arrives at a Total fertility rate of around 7, also in line with the expectation (see above). This higher value, not surprisingly, leads to much higher numbers in all the other indices, including a much higher Rate of natural increase and a much shorter doubling time of only 26 years.
 
 ```{r}
 lt.reproduction(lt.correction(schleswig)$life_table_corr, fertility_rate = "BA_log", growth_rate = "fertility")[c(-1,-2),]