Improve interface to experimental version of nlme

The experimental nlme version in my drat repository contains the
variance function structure varConstProp which makes it possible to use
the two-component error model in generalized nonlinear models using
nlme::gnls() and in mixed effects models using nlme::nlme().

1 files changed, 30 insertions, 3 deletions

diff --git a/R/sigma_twocomp.R b/R/sigma_twocomp.R
index 1e012d15..e8a92ced 100644
--- a/R/sigma_twocomp.R
+++ b/R/sigma_twocomp.R
@@ -23,7 +23,34 @@
 #' 
 #'   Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for
 #'   measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+#' @examples
+#' times <- c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+#' d_pred <- data.frame(time = times, parent = 100 * exp(- 0.03 * times))
+#' set.seed(123456)
+#' d_syn <- add_err(d_pred, function(y) sigma_twocomp(y, 1, 0.07),
+#'   reps = 2, n = 1)[[1]]
+#' f_nls <- nls(value ~ SSasymp(time, 0, parent_0, lrc), data = d_syn,
+#'  start = list(parent_0 = 100, lrc = -3))
+#' library(nlme)
+#' f_gnls <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
+#'   data = d_syn, na.action = na.omit,
+#'   start = list(parent_0 = 100, lrc = -3))
+#' if (length(findFunction("varConstProp")) > 0) {
+#'   f_gnls_tc <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
+#'     data = d_syn, na.action = na.omit,
+#'     start = list(parent_0 = 100, lrc = -3),
+#'     weights = varConstProp())
+#'   f_gnls_tc_sf <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
+#'     data = d_syn, na.action = na.omit,
+#'     start = list(parent_0 = 100, lrc = -3),
+#'     control = list(sigma = 1),
+#'     weights = varConstProp())
+#' }
+#' f_mkin <- mkinfit("SFO", d_syn, error_model = "const", quiet = TRUE)
+#' f_mkin_tc <- mkinfit("SFO", d_syn, error_model = "tc", quiet = TRUE)
+#' plot_res(f_mkin_tc, standardized = TRUE)
+#' AIC(f_nls, f_gnls, f_gnls_tc, f_gnls_tc_sf, f_mkin, f_mkin_tc)
 #' @export
-  sigma_twocomp <- function(y, sigma_low, rsd_high) {
-    sqrt(sigma_low^2 + y^2 * rsd_high^2)
-  }
+sigma_twocomp <- function(y, sigma_low, rsd_high) {
+  sqrt(sigma_low^2 + y^2 * rsd_high^2)
+}