From 20b9c584e7c43ecbb708459e531c24a1a4751e17 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 9 Nov 2019 01:05:51 +0100 Subject: Add a lack-of-fit test - Switch an example dataset in the test setup to a dataset with replicates, adapt tests - Skip the test for lrtest with an update specification as it does not only fail when pkgdown generates static help pages, but also in testthat --- docs/reference/index.html | 6 + docs/reference/loftest-1.png | Bin 0 -> 27354 bytes docs/reference/loftest-2.png | Bin 0 -> 27721 bytes docs/reference/loftest-3.png | Bin 0 -> 65409 bytes docs/reference/loftest-4.png | Bin 0 -> 64457 bytes docs/reference/loftest-5.png | Bin 0 -> 63057 bytes docs/reference/loftest.html | 343 +++++++++++++++++++++++++++++++++++++++++++ 7 files changed, 349 insertions(+) create mode 100644 docs/reference/loftest-1.png create mode 100644 docs/reference/loftest-2.png create mode 100644 docs/reference/loftest-3.png create mode 100644 docs/reference/loftest-4.png create mode 100644 docs/reference/loftest-5.png create mode 100644 docs/reference/loftest.html (limited to 'docs/reference') diff --git a/docs/reference/index.html b/docs/reference/index.html index 1c9975e0..0947ff94 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -214,6 +214,12 @@ more datasets

Likelihood ratio test for mkinfit models

+ +

loftest()

+ +

Lack-of-fit test for models fitted to data with replicates

+ +

mkinerrmin()

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This is a generic function with a method currently only defined for mkinfit +objects. It fits an anova model to the data contained in the object and +compares the likelihoods using the likelihood ratio test +lrtest.default from the lmtest package.

+
+ +
loftest(object, ...)
+
+# S3 method for mkinfit
+loftest(object, ...)
+ +

Arguments

+ + + + + + + + + + +
object

A model object with a defined loftest method

...

Not used

+ +

Details

+ +

The anova model is interpreted as the simplest form of an mkinfit model, +assuming only a constant variance about the means, but not enforcing any +structure of the means, so we have one model parameter for every mean +of replicate samples.

+

See also

+ +

lrtest

+ +

Examples

+
# \dontrun{ +test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == "parent") +sfo_fit <- mkinfit("SFO", test_data, quiet = TRUE) +plot_res(sfo_fit) # We see a clear pattern in the residuals
loftest(sfo_fit) # We have a clear lack of fit
#> Likelihood ratio test +#> +#> Model 1: ANOVA with error model const +#> Model 2: SFO with error model const +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 10 -40.710 +#> 2 3 -63.954 -7 46.487 7.027e-08 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# +# We try a different model (the one that was used to generate the data) +dfop_fit <- mkinfit("DFOP", test_data, quiet = TRUE) +plot_res(dfop_fit) # We don't see systematic deviations, but heteroscedastic residuals
# therefore we should consider adapting the error model, although we have +loftest(dfop_fit) # no lack of fit
#> Likelihood ratio test +#> +#> Model 1: ANOVA with error model const +#> Model 2: DFOP with error model const +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 10 -40.710 +#> 2 5 -42.453 -5 3.485 0.6257
# +# This is the anova model used internally for the comparison +test_data_anova <- test_data +test_data_anova$time <- as.factor(test_data_anova$time) +anova_fit <- lm(value ~ time, data = test_data_anova) +summary(anova_fit)
#> +#> Call: +#> lm(formula = value ~ time, data = test_data_anova) +#> +#> Residuals: +#> Min 1Q Median 3Q Max +#> -6.1000 -0.5625 0.0000 0.5625 6.1000 +#> +#> Coefficients: +#> Estimate Std. Error t value Pr(>|t|) +#> (Intercept) 103.150 2.323 44.409 7.44e-12 *** +#> time1 -19.950 3.285 -6.073 0.000185 *** +#> time3 -50.800 3.285 -15.465 8.65e-08 *** +#> time7 -68.500 3.285 -20.854 6.28e-09 *** +#> time14 -79.750 3.285 -24.278 1.63e-09 *** +#> time28 -86.000 3.285 -26.181 8.35e-10 *** +#> time60 -94.900 3.285 -28.891 3.48e-10 *** +#> time90 -98.500 3.285 -29.986 2.49e-10 *** +#> time120 -100.450 3.285 -30.580 2.09e-10 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 +#> +#> Residual standard error: 3.285 on 9 degrees of freedom +#> Multiple R-squared: 0.9953, Adjusted R-squared: 0.9912 +#> F-statistic: 240.5 on 8 and 9 DF, p-value: 1.417e-09 +#>
logLik(anova_fit) # We get the same likelihood and degrees of freedom
#> 'log Lik.' -40.71015 (df=10)
# +test_data_2 <- synthetic_data_for_UBA_2014[[12]]$data +m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"), + M1 = list(type = "SFO", to = "M2"), + M2 = list(type = "SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
sfo_lin_fit <- mkinfit(m_synth_SFO_lin, test_data_2, quiet = TRUE) +plot_res(sfo_lin_fit) # not a good model, we try parallel formation
loftest(sfo_lin_fit)
#> Likelihood ratio test +#> +#> Model 1: ANOVA with error model const +#> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 28 -93.606 +#> 2 7 -171.927 -21 156.64 < 2.2e-16 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# +m_synth_SFO_par <- mkinmod(parent = list(type = "SFO", to = c("M1", "M2")), + M1 = list(type = "SFO"), + M2 = list(type = "SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
sfo_par_fit <- mkinfit(m_synth_SFO_par, test_data_2, quiet = TRUE) +plot_res(sfo_par_fit) # much better for metabolites
loftest(sfo_par_fit)
#> Likelihood ratio test +#> +#> Model 1: ANOVA with error model const +#> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 28 -93.606 +#> 2 7 -156.331 -21 125.45 < 2.2e-16 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# +m_synth_DFOP_par <- mkinmod(parent = list(type = "DFOP", to = c("M1", "M2")), + M1 = list(type = "SFO"), + M2 = list(type = "SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
dfop_par_fit <- mkinfit(m_synth_DFOP_par, test_data_2, quiet = TRUE) +plot_res(dfop_par_fit) # No visual lack of fit
loftest(dfop_par_fit) # no lack of fit found by the test
#> Likelihood ratio test +#> +#> Model 1: ANOVA with error model const +#> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 28 -93.606 +#> 2 9 -102.763 -19 18.313 0.5016
# +# The anova model used for comparison in the case of transformation products +test_data_anova_2 <- dfop_par_fit$data +test_data_anova_2$variable <- as.factor(test_data_anova_2$variable) +test_data_anova_2$time <- as.factor(test_data_anova_2$time) +anova_fit_2 <- lm(observed ~ time:variable - 1, data = test_data_anova_2) +summary(anova_fit_2)
#> +#> Call: +#> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2) +#> +#> Residuals: +#> Min 1Q Median 3Q Max +#> -6.1000 -0.5875 0.0000 0.5875 6.1000 +#> +#> Coefficients: (2 not defined because of singularities) +#> Estimate Std. Error t value Pr(>|t|) +#> time0:variableparent 103.150 1.573 65.562 < 2e-16 *** +#> time1:variableparent 83.200 1.573 52.882 < 2e-16 *** +#> time3:variableparent 52.350 1.573 33.274 < 2e-16 *** +#> time7:variableparent 34.650 1.573 22.024 < 2e-16 *** +#> time14:variableparent 23.400 1.573 14.873 6.35e-14 *** +#> time28:variableparent 17.150 1.573 10.901 5.47e-11 *** +#> time60:variableparent 8.250 1.573 5.244 1.99e-05 *** +#> time90:variableparent 4.650 1.573 2.956 0.006717 ** +#> time120:variableparent 2.700 1.573 1.716 0.098507 . +#> time0:variableM1 NA NA NA NA +#> time1:variableM1 11.850 1.573 7.532 6.93e-08 *** +#> time3:variableM1 22.700 1.573 14.428 1.26e-13 *** +#> time7:variableM1 33.050 1.573 21.007 < 2e-16 *** +#> time14:variableM1 31.250 1.573 19.863 < 2e-16 *** +#> time28:variableM1 18.900 1.573 12.013 7.02e-12 *** +#> time60:variableM1 7.550 1.573 4.799 6.28e-05 *** +#> time90:variableM1 3.850 1.573 2.447 0.021772 * +#> time120:variableM1 2.050 1.573 1.303 0.204454 +#> time0:variableM2 NA NA NA NA +#> time1:variableM2 6.700 1.573 4.259 0.000254 *** +#> time3:variableM2 16.750 1.573 10.646 8.93e-11 *** +#> time7:variableM2 25.800 1.573 16.399 6.89e-15 *** +#> time14:variableM2 28.600 1.573 18.178 6.35e-16 *** +#> time28:variableM2 25.400 1.573 16.144 9.85e-15 *** +#> time60:variableM2 21.600 1.573 13.729 3.81e-13 *** +#> time90:variableM2 17.800 1.573 11.314 2.51e-11 *** +#> time120:variableM2 14.100 1.573 8.962 2.79e-09 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 +#> +#> Residual standard error: 2.225 on 25 degrees of freedom +#> Multiple R-squared: 0.9979, Adjusted R-squared: 0.9957 +#> F-statistic: 469.2 on 25 and 25 DF, p-value: < 2.2e-16 +#>
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