version 0.0.2-5

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: qlifetable
 Title: Managing and Building of Quarterly Life Tables
-Version: 0.0.2-4
+Version: 0.0.2-5
 Authors@R: 
     c(person(given = "Jose M.",
            family = "Pavía",
@@ -23,7 +23,7 @@ Description: Manages, builds and computes statistics and datasets for the
    Pavía and Lledó (2022) <doi:10.1111/rssa.12769>.
    Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>.
    Acknowledgements:
-   The authors wish to thank Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257), Ministerio de Economía e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research.
+   The authors wish to thank Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research.
 License: EPL
 Encoding: UTF-8
 Imports: stats, methods
@@ -32,8 +32,8 @@ LazyData: true
 RoxygenNote: 7.2.3
 Depends: R (>= 3.5.0)
 NeedsCompilation: no
-Packaged: 2024-01-24 09:52:30 UTC; mariaamparo
+Packaged: 2024-06-25 13:28:38 UTC; mariaamparo
 Author: Jose M. Pavía [aut, cre] (<https://orcid.org/0000-0002-0129-726X>),
   Josep Lledó [aut] (<https://orcid.org/0000-0002-7475-8549>)
 Repository: CRAN
-Date/Publication: 2024-01-24 14:40:09 UTC
+Date/Publication: 2024-06-28 07:30:07 UTC

MD5

@@ -1,21 +1,21 @@
-5f2f5de43b7b5676a3b23d8f61dddefd *DESCRIPTION
+fb94c6c932017ecc6d063c913f0b9e47 *DESCRIPTION
 45b44298d41cc2081f8f4f3a0ef60b2b *LICENSE.note
 cbed70e8c98c523dd6d2365bce95ab9c *NAMESPACE
-790138ffb94994b7bf2afea31511be33 *NEWS.md
-dc2d34634bda1d6efc5aacb72b6d36ac *R/SAI_shortcut_1.R
-0fb168371356ded9b128bb31ac822ccd *R/annual2quarterly.R
+f855e32614b43c1927511b7f9941df8e *NEWS.md
+85260a9adb1a3d456efc11e634f3ab72 *R/SAI_shortcut_1.R
+891e5879efa3a6dab1a476e05acb507e *R/annual2quarterly.R
 5d5f6d99960a207cf7dc7d092d3b5de9 *R/auxiliar_functions.R
-cfe7a685d4ba06e35c50ee8209a074a6 *R/compute_SAI.R
+1036a01a179e5587a5fa3e8e7a6f3845 *R/compute_SAI.R
 9c88d2c45b392e2e3c4155185ca5c2b7 *R/coord_age.R
 e36db8c885172c33b7800881960c517c *R/coord_time.R
 ce8ecf14aa7c7bcfa0a79e2092077508 *R/count_events_quarter.R
 6b56c7468fcf429e72af5bb06d7221af *R/crude_mx.R
-ceb321d0ce179fbefc595c42f08ffcac *R/crude_mx_sh2.R
-7016ee33ba7f51c9589f9805baabc4ea *R/crude_mx_sh3.R
+3063689b6110662cbc8292c96c1522d3 *R/crude_mx_sh2.R
+ab197341184910a0b889e863e2ddba5c *R/crude_mx_sh3.R
 425270b41d94a98b421096f7586d8e37 *R/distribute_excess.R
 1a7c6f72bb9138590cd5fba84ead92d9 *R/exact_age.R
 f5f8cb2fc99fb1651f46591482784c6a *R/plot.R
-2d6de2b5a694e4478d1d5ede745a9d7e *R/plot.SAI.R
+0a2e8d6c93aea78bd0bf06634c357e61 *R/plot.SAI.R
 561d3d867600fa7fd94929b19f29511a *R/quarterly_variables.R
 6a91f1dacc16f41296feb79560493249 *R/time_exposed_ins.R
 2d2f89bacbc11edf14264aa2039331e8 *R/time_exposed_newborns.R
@@ -27,16 +27,16 @@ e9d08b8550667da6f5b586ed4eea0a9d *data/death_2006.rda
 74ebb2ba88eba08b5a1ee0fefeaf0ba2 *data/emi_2006.rda
 ddd59fded156340ca89982b92a0d2986 *data/immi_2006.rda
 9ba6266555bd1dd3664fca0fea859140 *data/pop_2006.rda
-cf7adcb15ce50586b1ead0b97621971a *man/SAI_shortcut_1.Rd
-61e86a238f600350ac75bbb78e48587e *man/annual2quarterly.Rd
+51828722cafb9cee29a358622e6d6156 *man/SAI_shortcut_1.Rd
+611656db869341e58d416c60988eb245 *man/annual2quarterly.Rd
 5f95f1293c6db8d7bf7602d062be1f82 *man/birth_2006.Rd
-42ff0eeca0112d054b6709ee78e66dbe *man/compute_SAI.Rd
+737147966761929882e47610586c42aa *man/compute_SAI.Rd
 75ca7192b81aadf56a1dc007d7ac459b *man/coord_age.Rd
 c55920c54bb42693eb68bc5353fcc74a *man/coord_time.Rd
 d23ca1080ea40f9c61672cba5c3d4061 *man/count_events_quarter.Rd
 8abab62e05d5da8e8b7604f112e3f8f7 *man/crude_mx.Rd
-41262e11f349a4fce99ff9a49a0dca50 *man/crude_mx_sh2.Rd
-a837d6b19f4307942665cac5a0f4a465 *man/crude_mx_sh3.Rd
+8e2af9d66750f70bd3227d9a5aa89e57 *man/crude_mx_sh2.Rd
+187fa041476b56eff4118d212e2934c7 *man/crude_mx_sh3.Rd
 40246ccff585f1133f61a8514b3bd041 *man/death_2006.Rd
 115621d50536b1790e189392ed172726 *man/distribute_excess.Rd
 bc6d4338a796bd9bc1beab23bd7fc895 *man/emi_2006.Rd

NEWS.md

@@ -1,6 +1,12 @@
+### Package changes from previous qlifetable version 0.0.2-4
+
+The way grobs are defined in the method **plot** for objects of class SAI has been modified to accomodate some changes made in new versions of ggplot2 (>=3.5.0).
+
+A bug in the function **annual2quaterly** has been fixed when its input annual life table is defined using death rates (mx).
+
 ### Package changes from previous qlifetable version 0.0.1-15
 
-The previous version (0.0.1-15= of qlifetable just computes from microdata the summary statistics to build quarterly life tables. This new version includes two new sets of functions. On the one hand, `qlifetable` now incorporates a bunch of functions to construct from summary statistics Seasonal-ageing indexes (SAIs) and quarterly life tables and, on the other hand, the new version also has new functions to estimate SAIs approximations as detailed in Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>.
+The previous version (0.0.1-15) of qlifetable just computes from microdata the summary statistics to build quarterly life tables. This new version includes two new sets of functions. On the one hand, `qlifetable` now incorporates a bunch of functions to construct from summary statistics Seasonal-ageing indexes (SAIs) and quarterly life tables and, on the other hand, the new version also has new functions to estimate SAIs approximations as detailed in Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>.
 
 The list of new functions includes:
 

R/SAI_shortcut_1.R

@@ -52,6 +52,8 @@
 #'  e.death <- count_events_quarter(temp)
 #'  SAI.example <- SAI_shortcut_1(e.death, e.death)
 #' }
+#'
+#' # Fast example
 #' dates.b <- c("2017-05-13", "2018-04-12", "2018-01-01")
 #' dates.d <- c("2020-09-23", "2021-10-11", "2021-11-23")
 #' x <- quarterly_variables(dates.b, dates.d)

R/annual2quarterly.R

@@ -8,7 +8,7 @@
 #' @references Pavia, JM and Lledo, J (2022). Estimation of the Combined Effects of Ageing and Seasonality on Mortality Risk. An application to Spain. *Journal of the Royal Statistical Society, Series A (Statistics in Society)*, 185(2), 471-497. \doi{10.1111/rssa.12769}
 #'
 #' @param table.a A data.frame corresponding to the reference annual life table. The life table can be defined via
-#'                death rates (`mx`, default) or via death probabilities (`qx`). The first column of `table.a` refers to `age`,
+#'                via death probabilities (`qx`, default) or death rates (`mx`). The first column of `table.a` refers to `age`,
 #'                and the second column to either `mx` rates or `qx` probabilities. In case of using death probabilities (`qx`),
 #'                `table.a` can have an optional third column, which refers to the the average number of years lived
 #'                for those dying with age x, `ax`. If this last column is missing ax is assumed to be constant and equal to 0.5.
@@ -47,24 +47,20 @@
 #'  e.death <- e.death[e.death$age <= 100, ]
 #'  t.birth <- time_exposed_newborns(birth_2006$date.birth)
 #'  out <- crude_mx(t.stock, e.death, time.birth = t.birth)
-#'  SAI.example <- compute_SAI(out, out)}
+#'  SAI.example <- compute_SAI(out, out)
+#'  dx <- tapply(e.death$number.events, e.death$age, sum)
+#'  Lax <- tapply(t.stock$time.exposed, t.stock$age, sum)
+#'  table.ex <- data.frame(age = 0:100, mx.a = dx/Lax)
+#'  example <- annual2quarterly(table.a = table.ex, SAIs = SAI.example, mx = TRUE)
+#' }
 #'
-#' dates.b <- c("2017-05-13", "2018-04-12", "2018-12-01")
-#' t.stock <- time_exposed_stock(dates.b, year = 2020, type = "backward")
-#' dates.bd <- c("2018-04-12")
-#' dates.d <- c("2020-05-23")
-#' x <- quarterly_variables(dates.bd, dates.d)
-#' e.death <- count_events_quarter(x)
-#' t.death <- time_exposed_outs(x)
-#' out <- crude_mx(t.stock, e.death, t.death)
-#' SAI.example <- compute_SAI(out, out)
 
 annual2quarterly <- function(table.a, SAIs, mx = FALSE, min.age = 0, max.age = 100){
 
   argg <- as.list(environment())
   SAIs <- check_a2q(argg)
 
-  if (!mx){
+  if (mx){
     colnames(table.a)[1L:2L] <- c("age", "mx")
   } else {
     table.a <- q2m(table.a)
@@ -115,7 +111,7 @@ q2m <- function(tabla){
     colnames(tabla) <- c("age", "qx", "ax")
     ax <- tabla[, 3L]
   }
-  qx <- tabla[, 1L]
+  qx <- tabla[, 2L]
   tabla$mx <- qx/(1 - (1- ax)*qx)
   return(tabla)
 }

R/compute_SAI.R

@@ -50,6 +50,8 @@
 #'  out <- crude_mx(t.stock, e.death, time.birth = t.birth)
 #'  SAI.example <- compute_SAI(out, out)
 #' }
+#'
+#' # Fast example
 #' dates.b <- c("2017-05-13", "2018-04-12", "2018-12-01")
 #' t.stock <- time_exposed_stock(dates.b, year = 2020, type = "backward")
 #' dates.bd <- c("2018-04-12")

R/crude_mx_sh2.R

@@ -61,6 +61,8 @@
 #'  e.death <- count_events_quarter(temp)
 #'  out <- crude_mx_sh2(pop, pop, e.death)
 #' }
+#'
+#' # Fast example
 #' pop.1 <- data.frame(age = c(40, 41), people = c(4134, 4353))
 #' pop.2 <- data.frame(age = c(40, 41), people = c(4250, 4213))
 #' dates.b <- c("1980-04-12")

R/crude_mx_sh3.R

@@ -82,6 +82,8 @@
 #'  e.immi <- count_events_quarter(temp)
 #'  out <- crude_mx_sh3(pop, pop, e.death, e.emi, e.immi)
 #' }
+#'
+#' # Fast example
 #' pop.1 <- data.frame(age = c(rep(40, 4), rep(41, 4)), quarter = rep(1:4, 2), people = c(4134, 4353))
 #' pop.2 <- data.frame(age = c(rep(40, 4), rep(41, 4)), quarter = rep(1:4, 2), people = c(4250, 4213))
 #' dates.b <- c("1980-04-12")

R/plot.SAI.R

@@ -173,7 +173,7 @@ plot_SAI_margins <- function(x, min.age = 1, max.age = 100, decimal.digits = 1,
                                   ggplot2::aes(x = age.j, y = SAI.norm.j)) +
     ggplot2::geom_line(col = color.main) +
     ggplot2::geom_smooth(formula = y ~ x, se = F, method = "lm",
-                         color = color.lm, size = 0.25) +
+                         color = color.lm, linewidth = 0.25) +
     ggplot2::geom_hline(yintercept = 1, col = color.hline, lty = 2) +
     ggplot2::scale_x_continuous(breaks = axis.age.ticks) +
     ggplot2::facet_wrap("Quarter") +
@@ -291,26 +291,30 @@ plot_SAI_margins <- function(x, min.age = 1, max.age = 100, decimal.digits = 1,
   } else {
     posiciones <- unique(seasonal.figure1$grobs[[2L]]$children[[1L]]$children[[5L]]$x)
   }
+  posiciones <- c(0.145, 0.4575, 0.76)
   ticks0 <- grid::segmentsGrob(x0 = posiciones, x1 = posiciones,
                                y0 = rep(1 - 0.007, length(posiciones)),
                                y1 = rep(1 - .018, length(posiciones)),
                                gp = grid::gpar(lwd = 1.35))
   ticks0 <- grid::grobTree(blank, children = grid::gList(ticks0))
 
-  labels.ages0 <- grid::textGrob(axis.age.ticks, x = posiciones, y = rep(0.35, length(posiciones)),
-                                gp = grid::gpar(cex = 8.7/12, col = "grey30"))
+  labels.ages0 <- grid::textGrob(axis.age.ticks, x = posiciones,
+                                 y = rep(0.35, length(posiciones)),
+                                 gp = grid::gpar(cex = 8.7/12, col = "grey30"))
   labels.ages0 <- grid::grobTree(blank, children = grid::gList(labels.ages0))
 
-  ticks <- gridExtra::arrangeGrob( blank, ticks0, blank, blank,
-                                   ncol = 4L,
-                                   widths = ageing.figure1$widths[c(1L, 5L, 6L, 9L)])
+  #1 ticks <- gridExtra::arrangeGrob( blank, ticks0, blank, blank,
+  #1                                 ncol = 4L,
+  #1                                 widths = ageing.figure1$widths[c(1L, 5L, 6L, 9L)])
+  ticks <- gridExtra::arrangeGrob(ticks0)
 
-  labels.ages <- gridExtra::arrangeGrob( blank, labels.ages0, blank, blank,
-                                         ncol = 4L,
-                                         widths = ageing.figure1$widths[c(1L, 5L, 6L, 9L)])
+  #1 labels.ages <- gridExtra::arrangeGrob( blank, labels.ages0, blank, blank,
+  #1                                        ncol = 4L,
+  #1                                        widths = ageing.figure1$widths[c(1L, 5L, 6L, 9L)])
 
+  labels.ages <- gridExtra::arrangeGrob(labels.ages0)
   ageing.figure <- grid::grobTree(ticks, children = grid::gList(ageing.figure1))
-  # ageing.figure <- grid::grobTree(labels.ages, children = grid::gList(ageing.figure))
+  ageing.figure <- grid::grobTree(labels.ages, children = grid::gList(ageing.figure))
 
   ageing.label <- grid::textGrob(title.ageing, x = .48, y = 1.02, gp = grid::gpar(cex = .85))
   ageing.label <- grid::grobTree(blank, children = grid::gList(ageing.label))