From e959fde98f95f3595e01490b67892678bbcd1b27 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 7 May 2014 14:47:28 +0200 Subject: Fork the gmkin GUI from mkin. See ChangeLog for details --- vignettes/FOCUS_L.Rmd | 243 -------------------------------------------------- 1 file changed, 243 deletions(-) delete mode 100644 vignettes/FOCUS_L.Rmd (limited to 'vignettes/FOCUS_L.Rmd') diff --git a/vignettes/FOCUS_L.Rmd b/vignettes/FOCUS_L.Rmd deleted file mode 100644 index 957b34a..0000000 --- a/vignettes/FOCUS_L.Rmd +++ /dev/null @@ -1,243 +0,0 @@ - - -# Example evaluation of FOCUS Laboratory Data L1 to L3 - -## Laboratory Data L1 - -The following code defines example dataset L1 from the FOCUS kinetics -report, p. 284 - -```{r} -library("mkin") -FOCUS_2006_L1 = data.frame( - t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), - parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, - 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, - 27.7, 27.3, 10.0, 10.4, 2.9, 4.0)) -FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1) -``` - -The next step is to set up the models used for the kinetic analysis. Note that -the model definitions contain the names of the observed variables in the data. -In this case, there is only one variable called `parent`. - -```{r} -SFO <- mkinmod(parent = list(type = "SFO")) -FOMC <- mkinmod(parent = list(type = "FOMC")) -DFOP <- mkinmod(parent = list(type = "DFOP")) -``` - -The three models cover the first assumption of simple first order (SFO), -the case of declining rate constant over time (FOMC) and the case of two -different phases of the kinetics (DFOP). For a more detailed discussion -of the models, please see the FOCUS kinetics report. - -The following two lines fit the model and produce the summary report -of the model fit. This covers the numerical analysis given in the -FOCUS report. - -```{r} -m.L1.SFO <- mkinfit(SFO, FOCUS_2006_L1_mkin, quiet=TRUE) -summary(m.L1.SFO) -``` - -A plot of the fit is obtained with the plot function for mkinfit objects. - -```{r fig.width=7, fig.height = 5} -plot(m.L1.SFO) -``` -The residual plot can be easily obtained by - -```{r fig.width=7, fig.height = 5} -mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time") -``` - -For comparison, the FOMC model is fitted as well, and the chi^2 error level -is checked. - -```{r} -m.L1.FOMC <- mkinfit(FOMC, FOCUS_2006_L1_mkin, quiet=TRUE) -summary(m.L1.FOMC, data = FALSE) -``` - -Due to the higher number of parameters, and the lower number of degrees of -freedom of the fit, the chi^2 error level is actually higher for the FOMC -model (3.6%) than for the SFO model (3.4%). Additionally, the covariance -matrix can not be obtained, indicating overparameterisation of the model. -As a consequence, no standard errors for transformed parameters nor -confidence intervals for backtransformed parameters are available. - -The chi^2 error levels reported in Appendix 3 and Appendix 7 to the FOCUS -kinetics report are rounded to integer percentages and partly deviate by one -percentage point from the results calculated by mkin. The reason for -this is not known. However, mkin gives the same chi^2 error levels -as the kinfit package. - -Furthermore, the calculation routines of the kinfit package have been extensively -compared to the results obtained by the KinGUI software, as documented in the -kinfit package vignette. KinGUI is a widely used standard package in this field. -Therefore, the reason for the difference was not investigated further. - -## Laboratory Data L2 - -The following code defines example dataset L2 from the FOCUS kinetics -report, p. 287 - -```{r} -FOCUS_2006_L2 = data.frame( - t = rep(c(0, 1, 3, 7, 14, 28), each = 2), - parent = c(96.1, 91.8, 41.4, 38.7, - 19.3, 22.3, 4.6, 4.6, - 2.6, 1.2, 0.3, 0.6)) -FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) -``` - -Again, the SFO model is fitted and a summary is obtained. - -```{r} -m.L2.SFO <- mkinfit(SFO, FOCUS_2006_L2_mkin, quiet=TRUE) -summary(m.L2.SFO) -``` - -The chi^2 error level of 14% suggests that the model does not fit very well. -This is also obvious from the plots of the fit and the residuals. - -```{r fig.height = 8} -par(mfrow = c(2, 1)) -plot(m.L2.SFO) -mkinresplot(m.L2.SFO) -``` - -In the FOCUS kinetics report, it is stated that there is no apparent systematic -error observed from the residual plot up to the measured DT90 (approximately at -day 5), and there is an underestimation beyond that point. - -We may add that it is difficult to judge the random nature of the residuals just -from the three samplings at days 0, 1 and 3. Also, it is not clear _a -priori_ why a consistent underestimation after the approximate DT90 should be -irrelevant. However, this can be rationalised by the fact that the FOCUS fate -models generally only implement SFO kinetics. - -For comparison, the FOMC model is fitted as well, and the chi^2 error level -is checked. - -```{r fig.height = 8} -m.L2.FOMC <- mkinfit(FOMC, FOCUS_2006_L2_mkin, quiet = TRUE) -par(mfrow = c(2, 1)) -plot(m.L2.FOMC) -mkinresplot(m.L2.FOMC) -summary(m.L2.FOMC, data = FALSE) -``` - -The error level at which the chi^2 test passes is much lower in this case. -Therefore, the FOMC model provides a better description of the data, as less -experimental error has to be assumed in order to explain the data. - -Fitting the four parameter DFOP model further reduces the chi^2 error level. - -```{r fig.height = 5} -m.L2.DFOP <- mkinfit(DFOP, FOCUS_2006_L2_mkin, quiet = TRUE) -plot(m.L2.DFOP) -``` - -Here, the default starting parameters for the DFOP model obviously do not lead -to a reasonable solution. Therefore the fit is repeated with different starting -parameters. - -```{r fig.height = 5} -m.L2.DFOP <- mkinfit(DFOP, FOCUS_2006_L2_mkin, - parms.ini = c(k1 = 1, k2 = 0.01, g = 0.8), - quiet=TRUE) -plot(m.L2.DFOP) -summary(m.L2.DFOP, data = FALSE) -``` - -Here, the DFOP model is clearly the best-fit model for dataset L2 based on the -chi^2 error level criterion. However, the failure to calculate the covariance -matrix indicates that the parameter estimates correlate excessively. Therefore, -the FOMC model may be preferred for this dataset. - -## Laboratory Data L3 - -The following code defines example dataset L3 from the FOCUS kinetics report, -p. 290. - -```{r} -FOCUS_2006_L3 = data.frame( - t = c(0, 3, 7, 14, 30, 60, 91, 120), - parent = c(97.8, 60, 51, 43, 35, 22, 15, 12)) -FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3) -``` - -SFO model, summary and plot: - -```{r fig.height = 5} -m.L3.SFO <- mkinfit(SFO, FOCUS_2006_L3_mkin, quiet = TRUE) -plot(m.L3.SFO) -summary(m.L3.SFO) -``` - -The chi^2 error level of 21% as well as the plot suggest that the model -does not fit very well. - -The FOMC model performs better: - -```{r fig.height = 5} -m.L3.FOMC <- mkinfit(FOMC, FOCUS_2006_L3_mkin, quiet = TRUE) -plot(m.L3.FOMC) -summary(m.L3.FOMC, data = FALSE) -``` - -The error level at which the chi^2 test passes is 7% in this case. - -Fitting the four parameter DFOP model further reduces the chi^2 error level -considerably: - -```{r fig.height = 5} -m.L3.DFOP <- mkinfit(DFOP, FOCUS_2006_L3_mkin, quiet = TRUE) -plot(m.L3.DFOP) -summary(m.L3.DFOP, data = FALSE) -``` - -Here, a look to the model plot, the confidence intervals of the parameters -and the correlation matrix suggest that the parameter estimates are reliable, and -the DFOP model can be used as the best-fit model based on the chi^2 error -level criterion for laboratory data L3. - -## Laboratory Data L4 - -The following code defines example dataset L4 from the FOCUS kinetics -report, p. 293 - -```{r} -FOCUS_2006_L4 = data.frame( - t = c(0, 3, 7, 14, 30, 60, 91, 120), - parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0)) -FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4) -``` - -SFO model, summary and plot: - -```{r fig.height = 5} -m.L4.SFO <- mkinfit(SFO, FOCUS_2006_L4_mkin, quiet = TRUE) -plot(m.L4.SFO) -summary(m.L4.SFO, data = FALSE) -``` - -The chi^2 error level of 3.3% as well as the plot suggest that the model -fits very well. - -The FOMC model for comparison - -```{r fig.height = 5} -m.L4.FOMC <- mkinfit(FOMC, FOCUS_2006_L4_mkin, quiet = TRUE) -plot(m.L4.FOMC) -summary(m.L4.FOMC, data = FALSE) -``` - -The error level at which the chi^2 test passes is slightly lower for the FOMC -model. However, the difference appears negligible. - -- cgit v1.2.1