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-rw-r--r--man/sigma_twocomp.Rd4
-rw-r--r--man/synthetic_data_for_UBA_2014.Rd2
2 files changed, 3 insertions, 3 deletions
diff --git a/man/sigma_twocomp.Rd b/man/sigma_twocomp.Rd
index 6f941093..9e91fe78 100644
--- a/man/sigma_twocomp.Rd
+++ b/man/sigma_twocomp.Rd
@@ -5,8 +5,8 @@
Function describing the standard deviation of the measurement error
in dependence of the measured value \eqn{y}:
- \deqn{\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}}{%
- sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2)}
+ \deqn{\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}}
+ {sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2)}
This is the error model used for example by Werner et al. (1978). The model
proposed by Rocke and Lorenzato (1995) can be written in this form as well,
diff --git a/man/synthetic_data_for_UBA_2014.Rd b/man/synthetic_data_for_UBA_2014.Rd
index af67fb82..9b2b9d60 100644
--- a/man/synthetic_data_for_UBA_2014.Rd
+++ b/man/synthetic_data_for_UBA_2014.Rd
@@ -110,7 +110,7 @@ d_synth_names = paste0("d_synth_", c("SFO_lin", "SFO_par",
# d_rep = data.frame(lapply(d_long, rep, each = 2))
# d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))
#
-# d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0
+# d_rep[d_rep$time == 0 & d_rep$name \%in\% c("M1", "M2"), "value"] <- 0
# d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))
# d_NA$value <- round(d_NA$value, 1)
# return(d_NA)

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