diff options
Diffstat (limited to 'man')
| -rw-r--r-- | man/add_err.Rd | 2 | ||||
| -rw-r--r-- | man/nlme.mmkin.Rd | 44 | ||||
| -rw-r--r-- | man/sigma_twocomp.Rd | 28 |
3 files changed, 66 insertions, 8 deletions
diff --git a/man/add_err.Rd b/man/add_err.Rd index 9527d508..fe7002ee 100644 --- a/man/add_err.Rd +++ b/man/add_err.Rd @@ -8,7 +8,7 @@ add_err( prediction, sdfunc, secondary = c("M1", "M2"), - n = 1000, + n = 10, LOD = 0.1, reps = 2, digits = 1, diff --git a/man/nlme.mmkin.Rd b/man/nlme.mmkin.Rd index 10c3ec78..0af670a0 100644 --- a/man/nlme.mmkin.Rd +++ b/man/nlme.mmkin.Rd @@ -77,16 +77,16 @@ have been obtained by fitting the same model to a list of datasets. \examples{ ds <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")], name == "parent")) -f <- mmkin("SFO", ds, quiet = TRUE, cores = 1) +f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1) library(nlme) -endpoints(f[[1]]) -f_nlme <- nlme(f) -print(f_nlme) -endpoints(f_nlme) +f_nlme_sfo <- nlme(f["SFO", ]) +f_nlme_dfop <- nlme(f["DFOP", ]) +AIC(f_nlme_sfo, f_nlme_dfop) +print(f_nlme_dfop) +endpoints(f_nlme_dfop) \dontrun{ - f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1)) + f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1)) update(f_nlme_2, random = parent_0 ~ 1) - # Test on some real data ds_2 <- lapply(experimental_data_for_UBA_2019[6:10], function(x) x$data[c("name", "time", "value")]) m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), @@ -130,6 +130,36 @@ endpoints(f_nlme) endpoints(f_nlme_sfo_sfo) endpoints(f_nlme_dfop_sfo) + + if (findFunction("varConstProp")) { # tc error model for nlme available + # Attempts to fit metabolite kinetics with the tc error model + #f_2_tc <- mmkin(list("SFO-SFO" = m_sfo_sfo, + # "SFO-SFO-ff" = m_sfo_sfo_ff, + # "FOMC-SFO" = m_fomc_sfo, + # "DFOP-SFO" = m_dfop_sfo), + # ds_2, quiet = TRUE, + # error_model = "tc") + #f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ], control = list(maxIter = 100)) + #f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ]) + #f_nlme_dfop_sfo_tc <- update(f_nlme_dfop_sfo, weights = varConstProp(), + # control = list(sigma = 1, msMaxIter = 100, pnlsMaxIter = 15)) + # Fitting metabolite kinetics with nlme.mmkin and the two-component + # error model currently does not work, at least not with these data. + + f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc") + f_nlme_sfo_tc <- nlme(f_tc["SFO", ]) + f_nlme_dfop_tc <- nlme(f_tc["DFOP", ]) + AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc) + print(f_nlme_dfop_tc) + } + f_2_obs <- mmkin(list("SFO-SFO" = m_sfo_sfo, + "DFOP-SFO" = m_dfop_sfo), + ds_2, quiet = TRUE, error_model = "obs") + f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ]) + # The same with DFOP-SFO does not converge, apparently the variances of + # parent and A1 are too similar in this case, so that the model is + # overparameterised + #f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100)) } } \seealso{ diff --git a/man/sigma_twocomp.Rd b/man/sigma_twocomp.Rd index 4e1f7c38..ed79d493 100644 --- a/man/sigma_twocomp.Rd +++ b/man/sigma_twocomp.Rd @@ -31,6 +31,34 @@ proposed by Rocke and Lorenzato (1995) can be written in this form as well, but assumes approximate lognormal distribution of errors for high values of y. } +\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) +} \references{ Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978) Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry |