From 42171ba55222383a0d47e5aacd46a972819e7812 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 15 Apr 2020 18:13:04 +0200 Subject: Include random effects in starting parameters - mean_degparms() now optionally returns starting values for fixed and random effects, which makes it possible to obtain acceptable fits also in more difficult cases (with more parameters) - Fix the anova method, as it is currently not enough to inherit from lme: https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17761 - Show fit information, and per default also errmin information in plot.nlme.mmkin() - Examples for nlme.mmkin: Decrease tolerance and increase the number of iterations in the PNLS step in order to be able to fit FOMC-SFO and DFOP-SFO --- docs/reference/index.html | 2 +- docs/reference/nlme.html | 80 ++++------------ docs/reference/nlme.mmkin-1.png | Bin 0 -> 69863 bytes docs/reference/nlme.mmkin-2.png | Bin 0 -> 70426 bytes docs/reference/nlme.mmkin-3.png | Bin 0 -> 70519 bytes docs/reference/nlme.mmkin-4.png | Bin 0 -> 72803 bytes docs/reference/nlme.mmkin-5.png | Bin 0 -> 72587 bytes docs/reference/nlme.mmkin-6.png | Bin 0 -> 71892 bytes docs/reference/nlme.mmkin-7.png | Bin 0 -> 72316 bytes docs/reference/nlme.mmkin.html | 172 ++++++++++++++++++++++++++++++++--- docs/reference/plot.mmkin-3.png | Bin 25550 -> 32259 bytes docs/reference/plot.mmkin-4.png | Bin 38129 -> 25550 bytes docs/reference/plot.mmkin-5.png | Bin 0 -> 38129 bytes docs/reference/plot.mmkin.html | 8 +- docs/reference/plot.nlme.mmkin-1.png | Bin 32297 -> 35382 bytes docs/reference/plot.nlme.mmkin-2.png | Bin 0 -> 35313 bytes docs/reference/plot.nlme.mmkin.html | 22 ++++- 17 files changed, 200 insertions(+), 84 deletions(-) create mode 100644 docs/reference/nlme.mmkin-1.png create mode 100644 docs/reference/nlme.mmkin-2.png create mode 100644 docs/reference/nlme.mmkin-3.png create mode 100644 docs/reference/nlme.mmkin-4.png create mode 100644 docs/reference/nlme.mmkin-5.png create mode 100644 docs/reference/nlme.mmkin-6.png create mode 100644 docs/reference/nlme.mmkin-7.png create mode 100644 docs/reference/plot.mmkin-5.png create mode 100644 docs/reference/plot.nlme.mmkin-2.png (limited to 'docs/reference') diff --git a/docs/reference/index.html b/docs/reference/index.html index 3c5f1b38..f4de5bd8 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -281,7 +281,7 @@ of an mmkin object

-

nlme(<mmkin>)

+

nlme(<mmkin>) print(<nlme.mmkin>) update(<nlme.mmkin>)

Create an nlme model for an mmkin row object

diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html index 981845fe..70c6b63c 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -144,7 +144,7 @@ datasets.

nlme_function(object)
 
-mean_degparms(object)
+mean_degparms(object, random = FALSE)
 
 nlme_data(object)
@@ -155,13 +155,23 @@ datasets.

object

An mmkin row object containing several fits of the same model to different datasets

+ + random +

Should a list with fixed and random effects be returned?

+

Value

A function that can be used with nlme

-

A named vector containing mean values of the fitted degradation model parameters

+

If random is FALSE (default), a named vector containing mean values + of the fitted degradation model parameters. If random is TRUE, a list with + fixed and random effects, in the format required by the start argument of + nlme for the case of a single grouping variable ds?

A groupedData object

+

See also

+ +

nlme.mmkin

Examples

sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
@@ -226,68 +236,9 @@ datasets.

 #> -2.6169360 -0.2185329  0.0574070  0.5720937  3.0459868 
 #> 
 #> Number of Observations: 49
-#> Number of Groups: 3 
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))

-# \dontrun{
-  # 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"),
-    A1 = mkinsub("SFO"), use_of_ff = "min")
#> Successfully compiled differential equation model from auto-generated C code.
  m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
-    A1 = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
  m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
-    A1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
  m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-    A1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
  m_sforb_sfo <- mkinmod(parent = mkinsub("SFORB", "A1"),
-    A1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.

-  f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
-   "SFO-SFO-ff" = m_sfo_sfo_ff,
-   "FOMC-SFO" = m_fomc_sfo,
-   "DFOP-SFO" = m_dfop_sfo,
-   "SFORB-SFO" = m_sforb_sfo),
-    ds_2)
-
-  grouped_data_2 <- nlme_data(f_2["SFO-SFO", ])
-
-  mean_dp_sfo_sfo <- mean_degparms(f_2["SFO-SFO", ])
-  mean_dp_sfo_sfo_ff <- mean_degparms(f_2["SFO-SFO-ff", ])
-  mean_dp_fomc_sfo <- mean_degparms(f_2["FOMC-SFO", ])
-  mean_dp_dfop_sfo <- mean_degparms(f_2["DFOP-SFO", ])
-  mean_dp_sforb_sfo <- mean_degparms(f_2["SFORB-SFO", ])
-
-  nlme_f_sfo_sfo <- nlme_function(f_2["SFO-SFO", ])
-  nlme_f_sfo_sfo_ff <- nlme_function(f_2["SFO-SFO-ff", ])
-  nlme_f_fomc_sfo <- nlme_function(f_2["FOMC-SFO", ])
-  assign("nlme_f_sfo_sfo", nlme_f_sfo_sfo, globalenv())
-  assign("nlme_f_sfo_sfo_ff", nlme_f_sfo_sfo_ff, globalenv())
-  assign("nlme_f_fomc_sfo", nlme_f_fomc_sfo, globalenv())
-
-  # Allowing for correlations between random effects (not shown)
-  # leads to non-convergence
-  f_nlme_sfo_sfo <- nlme(value ~ nlme_f_sfo_sfo(name, time,
-       parent_0, log_k_parent_sink, log_k_parent_A1, log_k_A1_sink),
-     data = grouped_data_2,
-     fixed = parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1,
-     random = pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1),
-     start = mean_dp_sfo_sfo)
-   # augPred does not see to work on this object, so no plot is shown
-
-  # The same model fitted with transformed formation fractions does not converge
-  f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time,
-       parent_0, log_k_parent, log_k_A1, f_parent_ilr_1),
-     data = grouped_data_2,
-     fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1,
-     random = pdDiag(parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1),
-     start = mean_dp_sfo_sfo_ff)
#> Error in nlme.formula(value ~ nlme_f_sfo_sfo_ff(name, time, parent_0,     log_k_parent, log_k_A1, f_parent_ilr_1), data = grouped_data_2,     fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~         1, random = pdDiag(parent_0 + log_k_parent + log_k_A1 +         f_parent_ilr_1 ~ 1), start = mean_dp_sfo_sfo_ff): step halving factor reduced below minimum in PNLS step

-  f_nlme_fomc_sfo <- nlme(value ~ nlme_f_fomc_sfo(name, time,
-       parent_0, log_alpha, log_beta, log_k_A1, f_parent_ilr_1),
-     data = grouped_data_2,
-     fixed = parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1,
-     random = pdDiag(parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1),
-     start = mean_dp_fomc_sfo)
-
-  # DFOP-SFO and SFORB-SFO did not converge with full random effects
-
-  anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo)
#>                 Model df       AIC       BIC    logLik   Test  L.Ratio p-value
-#> f_nlme_fomc_sfo     1 11  932.5817  967.0755 -455.2909                        
-#> f_nlme_sfo_sfo      2  9 1089.2492 1117.4714 -535.6246 1 vs 2 160.6675  <.0001
# }
+#> Number of Groups: 3 
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
# augPred does not seem to work on fits with more than one state
+# variable
+
#> Nonlinear mixed-effects model fit by maximum likelihood +print(f_nlme)
#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ deg_func(name, time, parent_0, log_k_parent_sink) -#> Data: structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3)), row.names = c(NA, -90L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE) -#> Log-likelihood: -394.4901 +#> Data: "Not shown" +#> Log-likelihood: -307.5269 #> Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> parent_0 log_k_parent_sink -#> 73.985522 -3.869079 +#> 85.541149 -3.229596 #> #> Random effects: -#> Formula: parent_0 ~ 1 | ds -#> parent_0 Residual -#> StdDev: 18.6134 18.22029 +#> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) +#> Level: ds +#> Structure: Diagonal +#> parent_0 log_k_parent_sink Residual +#> StdDev: 1.30857 1.288591 6.304906 #> #> Number of Observations: 90 -#> Number of Groups: 5
f_nlme <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1)) -update(f_nlme, random = parent_0 ~ 1)
#> Nonlinear mixed-effects model fit by maximum likelihood +#> Number of Groups: 5
f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1)) +update(f_nlme_2, random = parent_0 ~ 1)
#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ deg_func(name, time, parent_0, log_k_parent_sink) -#> Data: structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3)), row.names = c(NA, -90L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE) +#> Data: "Not shown" #> Log-likelihood: -404.3729 #> Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> parent_0 log_k_parent_sink @@ -265,7 +285,133 @@ parameters taken from the mmkin object are used

#> StdDev: 0.002416792 21.63027 #> #> Number of Observations: 90 -#> Number of Groups: 5
+#> Number of Groups: 5
# \dontrun{ + # 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"), + A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE) + m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"), + A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE) + m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), + A1 = mkinsub("SFO"), quiet = TRUE) + m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), + A1 = mkinsub("SFO"), quiet = TRUE) + + f_2 <- 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) + plot(f_2["SFO-SFO", 3:4]) # Separate fits for datasets 3 and 4
+ f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ]) + # plot(f_nlme_sfo_sfo) # not feasible with pkgdown figures + plot(f_nlme_sfo_sfo, 3:4) # Global mixed model: Fits for datasets 3 and 4
+ # With formation fractions + f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ]) + plot(f_nlme_sfo_sfo_ff, 3:4) # chi2 different due to different df attribution
+ # For more parameters, we need to increase pnlsMaxIter and the tolerance + # to get convergence + f_nlme_fomc_sfo <- nlme(f_2["FOMC-SFO", ], + control = list(pnlsMaxIter = 100, tolerance = 1e-4), verbose = TRUE)
#> +#> **Iteration 1 +#> LME step: Loglik: -394.1603, nlminb iterations: 2 +#> reStruct parameters: +#> ds1 ds2 ds3 ds4 ds5 +#> -0.2079984 0.8563873 1.7454146 1.0917723 1.2756924 +#> Beginning PNLS step: .. completed fit_nlme() step. +#> PNLS step: RSS = 643.8786 +#> fixed effects: 94.17379 -5.473199 -0.6970239 -0.2025094 2.103883 +#> iterations: 100 +#> Convergence crit. (must all become <= tolerance = 0.0001): +#> fixed reStruct +#> 0.7865373 0.1448077 +#> +#> **Iteration 2 +#> LME step: Loglik: -396.3824, nlminb iterations: 7 +#> reStruct parameters: +#> ds1 ds2 ds3 ds4 ds5 +#> -1.712408e-01 -2.680989e-05 1.842119e+00 1.073975e+00 1.322924e+00 +#> Beginning PNLS step: .. completed fit_nlme() step. +#> PNLS step: RSS = 643.8022 +#> fixed effects: 94.17385 -5.473491 -0.6970405 -0.202514 2.103871 +#> iterations: 100 +#> Convergence crit. (must all become <= tolerance = 0.0001): +#> fixed reStruct +#> 5.341904e-05 1.227073e-03 +#> +#> **Iteration 3 +#> LME step: Loglik: -396.3825, nlminb iterations: 7 +#> reStruct parameters: +#> ds1 ds2 ds3 ds4 ds5 +#> -0.1712484347 -0.0001513555 1.8420964843 1.0739800649 1.3229176990 +#> Beginning PNLS step: .. completed fit_nlme() step. +#> PNLS step: RSS = 643.7947 +#> fixed effects: 94.17386 -5.473522 -0.6970423 -0.2025142 2.10387 +#> iterations: 100 +#> Convergence crit. (must all become <= tolerance = 0.0001): +#> fixed reStruct +#> 5.568186e-06 1.276609e-04 +#> +#> **Iteration 4 +#> LME step: Loglik: -396.3825, nlminb iterations: 7 +#> reStruct parameters: +#> ds1 ds2 ds3 ds4 ds5 +#> -0.171251200 -0.000164506 1.842095097 1.073980200 1.322916184 +#> Beginning PNLS step: .. completed fit_nlme() step. +#> PNLS step: RSS = 643.7934 +#> fixed effects: 94.17386 -5.473526 -0.6970426 -0.2025146 2.103869 +#> iterations: 100 +#> Convergence crit. (must all become <= tolerance = 0.0001): +#> fixed reStruct +#> 2.332100e-06 1.979007e-05
f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ], + control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
#> +#> **Iteration 1 +#> LME step: Loglik: -404.9591, nlminb iterations: 1 +#> reStruct parameters: +#> ds1 ds2 ds3 ds4 ds5 ds6 +#> -0.4114594 0.9798456 1.6990016 0.7293119 0.3353829 1.7112922 +#> Beginning PNLS step: .. completed fit_nlme() step. +#> PNLS step: RSS = 630.391 +#> fixed effects: 93.82265 -5.455841 -0.6788837 -1.862191 -4.199654 0.05531046 +#> iterations: 120 +#> Convergence crit. (must all become <= tolerance = 0.0005): +#> fixed reStruct +#> 0.7872619 0.5811683 +#> +#> **Iteration 2 +#> LME step: Loglik: -407.7755, nlminb iterations: 11 +#> reStruct parameters: +#> ds1 ds2 ds3 ds4 ds5 ds6 +#> -0.371222832 0.003084754 1.789952290 0.724634064 0.301559136 1.754244638 +#> Beginning PNLS step: .. completed fit_nlme() step. +#> PNLS step: RSS = 630.359 +#> fixed effects: 93.82269 -5.456014 -0.6788967 -1.862202 -4.199678 0.05534118 +#> iterations: 120 +#> Convergence crit. (must all become <= tolerance = 0.0005): +#> fixed reStruct +#> 0.0005550885 0.0007749418 +#> +#> **Iteration 3 +#> LME step: Loglik: -407.7756, nlminb iterations: 11 +#> reStruct parameters: +#> ds1 ds2 ds3 ds4 ds5 ds6 +#> -0.371217033 0.003064156 1.789935045 0.724683005 0.301622307 1.754234135 +#> Beginning PNLS step: .. completed fit_nlme() step. +#> PNLS step: RSS = 630.358 +#> fixed effects: 93.82269 -5.456017 -0.6788969 -1.862197 -4.199677 0.05532978 +#> iterations: 120 +#> Convergence crit. (must all become <= tolerance = 0.0005): +#> fixed reStruct +#> 2.059533e-04 4.860085e-05
plot(f_2["FOMC-SFO", 3:4])
plot(f_nlme_fomc_sfo, 3:4)
+ plot(f_2["DFOP-SFO", 3:4])
plot(f_nlme_dfop_sfo, 3:4)
+ anova(f_nlme_dfop_sfo, f_nlme_fomc_sfo, f_nlme_sfo_sfo)
#> Model df AIC BIC logLik Test L.Ratio p-value +#> f_nlme_dfop_sfo 1 13 843.8541 884.6194 -408.9270 +#> f_nlme_fomc_sfo 2 11 818.5149 853.0087 -398.2575 1 vs 2 21.33913 <.0001 +#> f_nlme_sfo_sfo 3 9 1085.1821 1113.4042 -533.5910 2 vs 3 270.66712 <.0001
anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) # if we ignore FOMC
#> Model df AIC BIC logLik Test L.Ratio p-value +#> f_nlme_dfop_sfo 1 13 843.8541 884.6194 -408.927 +#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4042 -533.591 1 vs 2 249.328 <.0001
# } +
#> Warning: Optimisation did not converge: -#> iteration limit reached without convergence (10)
plot(fits[, "FOCUS C"])
plot(fits["FOMC", ])
+#> iteration limit reached without convergence (10)
plot(fits[, "FOCUS C"])
plot(fits["FOMC", ])
plot(fits["FOMC", ], show_errmin = FALSE)
# We can also plot a single fit, if we like the way plot.mmkin works, but then the plot # height should be smaller than the plot width (this is not possible for the html pages # generated by pkgdown, as far as I know). - plot(fits["FOMC", "FOCUS C"]) # same as plot(fits[1, 2])
+ plot(fits["FOMC", "FOCUS C"]) # same as plot(fits[1, 2])
# Show the error models - plot(fits["FOMC", ], resplot = "errmod")
# } + plot(fits["FOMC", ], resplot = "errmod")
# }
diff --git a/docs/reference/plot.nlme.mmkin-1.png b/docs/reference/plot.nlme.mmkin-1.png index 0717f30d..fe2ef7d3 100644 Binary files a/docs/reference/plot.nlme.mmkin-1.png and b/docs/reference/plot.nlme.mmkin-1.png differ diff --git a/docs/reference/plot.nlme.mmkin-2.png b/docs/reference/plot.nlme.mmkin-2.png new file mode 100644 index 00000000..27c09796 Binary files /dev/null and b/docs/reference/plot.nlme.mmkin-2.png differ diff --git a/docs/reference/plot.nlme.mmkin.html b/docs/reference/plot.nlme.mmkin.html index d5b7c00c..256739eb 100644 --- a/docs/reference/plot.nlme.mmkin.html +++ b/docs/reference/plot.nlme.mmkin.html @@ -144,6 +144,9 @@ legends = 1, resplot = c("time", "errmod"), standardized = FALSE, + show_errmin = TRUE, + errmin_var = "All data", + errmin_digits = 3, cex = 0.7, rel.height.middle = 0.9, ymax = "auto", @@ -181,6 +184,21 @@ values, with the error model, using mkinerrplot

Should the residuals be standardized? This option is passed to mkinresplot, it only takes effect if `resplot = "time"`.

+ + + show_errmin +

Should the chi2 error level be shown on top of the plots +to the left?

+ + + errmin_var +

The variable for which the FOCUS chi2 error value should +be shown.

+ + + errmin_digits +

The number of significant digits for rounding the FOCUS +chi2 error percentage.

cex @@ -211,11 +229,11 @@ than two rows of plots are shown.

function(x) subset(x$data[c("name", "time", "value")], name == "parent")) f <- mmkin("SFO", ds, quiet = TRUE, cores = 1) #plot(f) # too many panels for pkgdown -library(nlme) +plot(f[, 3:4])
library(nlme) f_nlme <- nlme(f) #plot(f_nlme) # too many panels for pkgdown -plot(f_nlme, 1:2)
+plot(f_nlme, 3:4)