diff options
Diffstat (limited to 'man')
| -rw-r--r-- | man/sigma_twocomp.Rd | 4 | ||||
| -rw-r--r-- | man/synthetic_data_for_UBA_2014.Rd | 2 |
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)
|