diff options
Diffstat (limited to 'R/mkinerrmin.R')
| -rw-r--r-- | R/mkinerrmin.R | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/R/mkinerrmin.R b/R/mkinerrmin.R index 6103dd1d..b361c466 100644 --- a/R/mkinerrmin.R +++ b/R/mkinerrmin.R @@ -24,7 +24,7 @@ mkinerrmin <- function(fit, alpha = 0.05) kinerrmin <- function(errdata, n.parms) { means.mean <- mean(errdata$value_mean, na.rm = TRUE) df = length(errdata$value_mean) - n.parms - + err.min <- sqrt((1 / qchisq(1 - alpha, df)) * sum((errdata$value_mean - errdata$value_pred)^2)/(means.mean^2)) @@ -32,12 +32,12 @@ mkinerrmin <- function(fit, alpha = 0.05) } means <- aggregate(value ~ time + name, data = fit$observed, mean, na.rm=TRUE) - errdata <- merge(means, fit$predicted, by = c("time", "name"), + errdata <- merge(means, fit$predicted, by = c("time", "name"), suffixes = c("_mean", "_pred")) errdata <- errdata[order(errdata$time, errdata$name), ] # Remove values at time zero for variables whose value for state.ini is fixed, - # as these will not have any effect in the optimization and should therefore not + # as these will not have any effect in the optimization and should therefore not # be counted as degrees of freedom. fixed_initials = gsub("_0$", "", rownames(subset(fit$fixed, type = "state"))) errdata <- subset(errdata, !(time == 0 & name %in% fixed_initials)) @@ -59,10 +59,10 @@ mkinerrmin <- function(fit, alpha = 0.05) # Rate constants and IORE exponents are attributed to the source variable n.k.optim <- length(grep(paste("^k", obs_var, sep="_"), names(parms.optim))) - n.k.optim <- n.k.optim + length(grep(paste("^log_k", obs_var, sep="_"), + n.k.optim <- n.k.optim + length(grep(paste("^log_k", obs_var, sep="_"), names(parms.optim))) n.k__iore.optim <- length(grep(paste("^k__iore", obs_var, sep="_"), names(parms.optim))) - n.k__iore.optim <- n.k__iore.optim + length(grep(paste("^log_k__iore", obs_var, + n.k__iore.optim <- n.k__iore.optim + length(grep(paste("^log_k__iore", obs_var, sep = "_"), names(parms.optim))) @@ -87,8 +87,8 @@ mkinerrmin <- function(fit, alpha = 0.05) # FOMC, DFOP and HS parameters are only counted if we are looking at the # first variable in the model which is always the source variable if (obs_var == fit$obs_vars[[1]]) { - special_parms = c("alpha", "log_alpha", "beta", "log_beta", - "k1", "log_k1", "k2", "log_k2", + special_parms = c("alpha", "log_alpha", "beta", "log_beta", + "k1", "log_k1", "k2", "log_k2", "g", "g_ilr", "tb", "log_tb") n.optim <- n.optim + length(intersect(special_parms, names(parms.optim))) } |