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Graphical Tools"
In GofCens: Goodness-of-Fit Methods for Complete and Right-Censored Data
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library("GofCens")
Graphical tools for goodness of fit
The GofCens package provides six different plots for evaluating goodness of fit, applicable to both complete and right-censored data. In most cases, a roughly straight line formed by the points suggests good agreement with the hypothesized theoretical distribution $F_0(t)$.
Following, $\hat{F}_n$ represents the empirical distribution function in case the data are complete data and 1 minus the Kaplan-Meier estimator of the survival function if the data are subject to random right-censorship.
We will illustrate how the functions of the package works using the following simulated survival times. We generate $300$ survival times from a log-normal distribution with location parameter $\mu = 2$ and scale parameter $\beta=1$, i.e. $T\sim LN(2,1)$, and $300$ censoring times from an exponential distribution with scale parameter $\beta=20$, i.e. $C\sim Exp(20)$:
set.seed(123)
survt <- round(rlnorm(300, 2, 1), 2)
censt <- round(rexp(300, 1 / 20), 2)
The observed right-censored survival times, $Y = min(T, C)$, and the corresponding event indicators, $\delta = \boldsymbol{1}{T \leq C}$, are created as follows:
times <- pmin(survt, censt)
delta <- as.numeric(survt <= censt)
In total, $106$ ($35.3\%$) of the survival times of the generated sample are right-censored:
table(delta)
Probability and quantile-quantile plots
The probability-probability plot (P-P plot) maps $\hat{F}_0(t)$ against $\hat{F}_n(t)$, where $\hat{F}_0(t)$ corresponds to $F_0(t)$ with the unknown parameters replaced by their maximum likelihood estimates. In order to enhance the interpretability of the P-P plot and because some of the plotted points have a larger variability than others, Stabilised Probability plot (SP plot) are used. In the same way as the arcsin transformation can be used to stabilise the variance of a uniform order statistic, this function can be applied to stabilise the variance of $\hat{F}_0(t)$. The variances of the resulting SP plotted points are all approximately equal.
The quantile-quantile plot (Q-Q plot) maps the theoretical quantiles against the estimated quantiles, that is, $\hat{F}_0^{-1}(\hat{F}_n(t))$ against $t$. An empirical rescaling of the axes can help to overcome the problem that the plotted points might not be evenly spread, in particular in the presence of right-censored data. The Empirically Rescaled plot (ER plot) plots $\hat{F}_u(\hat{F}_0^{-1}(\hat{F}_n(t)))$ against $\hat{F}_u(t)$, where the latter is the empirical cumulative distribution function of the points corresponding to the uncensored observations.
The four plots exposed above (P-P, Q-Q, SP and ER plot) can be done by means of the probPlot() function. By default, the function draws all four plots, but provides the user with the option to choose only a subset of these, and by default (prnt == TRUE), it returns the maximum likelihood estimates of the parameters of the distribution under study, with the corresponding standard errors, AIC and BIC. Notice that these estimates are used to draw the plots if no specific distribution parameters are provided. One graphical feature of the function is the option for the user to choose between the base package graphics, which is the default, and the ggplot2package for drawing the plots. In the former case, probPlot() internally calls the par() function, enabling the user to set graphical parameters within the function call.
For the illustration of the probPlot() function, we assess how well the log-normal distribution and the Weibull distribution fit to the sample data. The first four plots show fairly straight lines, thus confirming that the underlying distribution is the log-normal distribution, whereas the other plots clearly show that the Weibull distribution would not be an adequate distribution to model the data.
probPlot(times, delta, distr = "lognormal", prnt = FALSE, cex.lab = 1.3)
probPlot(times, delta, distr = "weibull", ggp = TRUE, prnt = FALSE)
Note that since prnt is set to FALSE, the maximum likelihood estimates under of both distributions parameters are not returned.
To illustrate additional functionalities of this function, we apply it again on the data set to assess the suitability of fitting a log-normal distribution with parameters $\mu=2$ and $\beta=1$. For this purpose, we employ the params0 argument, whose default value is NULL, to specify the parameter values. Additionally, the main argument is a Surv object and we utilize the m argument to display the four probability plots in a single row.
probPlot(Surv(times, delta) ~ 1, distr = "lognormal", m = matrix(1:4, nrow = 1),
params0 = list(location = 2, scale = 1.5), ggp = TRUE)
From these plots, it becomes evident that this log-normal distribution does not fit well to the data. Moreover, in this illustration, the function provides both the parameter values used in the probability plots and the maximum likelihood estimates of the parameters with the corresponding standard errors, AIC and BIC.
Cumulative hazard plot
Another set of plots consists of cumulative hazard plots, derived from transforming the cumulative hazard function $\Lambda$ in such a way that it becomes linear in $t$ or in $\log(t)$. The Nelson-Aalen estimator $\hat{\Lambda}$ of $\Lambda$ is computed from the data, and the distribution-specific transformation $A(\hat{\Lambda}(t))$ is then plotted against either $t$ or its natural logarithm.
The cumhazPlot() function can be used to draw cumulative hazard plots for any of the distributions available in the GofCens package. Given a set of survival times, this function is a helpful tool to assess which parametric model fits best to the given data.
By default, the function draws the cumulative hazard plots for the Weibull, Gumbel, log-logistic, logistic, log-normal, and normal distributions, but provides the user with the options to also choose the exponential and beta distribution or a subset of all these. Like the probPlot() function, the user can choose between the base package graphics, which is the default value, and the ggplot2 package to draw the plots, and by default, the maximum likelihood estimates of the distribution under study are returned with the corresponding standard errors, the AIC and the BIC.
An illustration with the previously generated right-censored sample from the log-normal distribution is shown. As expected, the points of the cumulative hazard plot for the log-normal distribution show a fairly straight line, whereas most of the other distributions under study can clearly be discarded. The corresponding code is the following. Different from the previous example, the function returns the parameter estimated for each of the six default distributions.
cumhazPlot(times, delta, font.lab = 4, cex.lab = 1.3)
If the user wants to assess the goodness of fit of the exponential and the beta distribution and compare these with the fit of the log-normal distribution, she/he has to specifically choose these distributions as it is done in the following example. Notice therein, that the user has to provide the limits of the beta distribution, because the default domain, the interval $(0, 1)$, does not cover the range of the sample’s survival times. Moreover, in this example, the plots are drawn with the ggplot2 package (ggp = TRUE) and the parameter estimates will not be returned on the screen (prnt = FALSE). We can see that neither the exponential nor the beta distribution fit well to the data. The corresponding code is the following.
cumhazPlot(times, delta, distr = c("exponential", "beta", "lognormal"),
betaLimits = c(0, 100), ggp = TRUE, prnt = FALSE)
Kaplan-Meier comparison
The GofCens package includes a function to graphically compare the Kaplan-Meier estimate of the survival function $(1-\hat{F}_n(t))$ with the parametric estimations of the survival function from the parametric models under study $(1-\hat{F}_0(t))$. This function is the kmplot().
For this purpose, each parametric estimation is individually added to the Kaplan-Meier estimator of $S(t)$. Likewise the cumhazPlot() function, this is done by default for the Weibull, Gumbel, log-logistic, logistic, log-normal, and normal distributions. Additionally, users have the option to choose the exponential and beta distributions. By default, the plots are generated using the graphics package, but setting the argument ggp = TRUE allows for the use of the ggplot2 package. With both options, the pointwise $95\%$ confidence intervals are plotted.
kmPlot(times, delta, ggp = TRUE, prnt = FALSE)
As expected, the log-normal distribution exhibits the best fit to the data. Nevertheless, based on the plots in this figure one might also consider the log-logistic distribution as a parametric model that fits well to the sample data.
Real data example: Survival times of retired NBA players
In this section, we apply the above functions of the GofCens package to determine which parametric model fits best to the survival times of former NBA players.
The data frame nba comes with the GofCens package and contains the survival times (variable survtime) of all $3962$ former players of the of the National Basketball Association (NBA) until July 2019. Survival times are measured as the elapsed time (in years) from the end of the NBA career until either death (cens == 1) or July 31, 2019 (cens == 1). By this date, $864$ ($21.8\%$) of the former players had died with uncensored post-NBA survival times ranging from a few days until nearly 70 years.
In order to estimate the median and model the survival times parametrically, we need to know which distribution is the most appropriate distribution. For this purpose, we take advantage of the cumhazPlot() function, which provides the following six cumulative hazard plots:
data("nba")
cumhazPlot(Surv(survtime, cens) ~ 1, nba, font.lab = 4, cex.lab = 1.3,
prnt = FALSE, lwd = 3, colour = "blue")
According to the cumulative hazard plots above, the logistic distribution fits reasonably well to the data, even though the corresponding plot does not show a completely straight line of the points. In addition, we could also consider the normal distribution for parametric analyses of the survival times. To choose one of either distributions, we run the following code to draw probability and quantile-quantile plots with the probPlot() function. In addition, we estimate the parameter of the logistic distribution fitted to the data.
probPlot(Surv(survtime, cens) ~ 1, nba, distr = "logistic", ggp = TRUE,
degs = 2)
probPlot(Surv(survtime, cens) ~ 1, nba, distr = "normal", ggp = TRUE,
prnt = FALSE)
According to the plots exposed above, the logistic distribution appears to be a slightly better choice than the normal distribution, as the points in all four plots for the normal distribution show some curvature.
Note that in the previous function calls, we used the formula() versions of both functions, which allow for the inclusion of the data frame nba in the argument list, which simplifies their use.
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GofCens documentation built on Oct. 30, 2024, 9:13 a.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library("GofCens")
Graphical tools for goodness of fit
The GofCens package provides six different plots for evaluating goodness of fit, applicable to both complete and right-censored data. In most cases, a roughly straight line formed by the points suggests good agreement with the hypothesized theoretical distribution $F_0(t)$.
Following, $\hat{F}_n$ represents the empirical distribution function in case the data are complete data and 1 minus the Kaplan-Meier estimator of the survival function if the data are subject to random right-censorship.
We will illustrate how the functions of the package works using the following simulated survival times. We generate $300$ survival times from a log-normal distribution with location parameter $\mu = 2$ and scale parameter $\beta=1$, i.e. $T\sim LN(2,1)$, and $300$ censoring times from an exponential distribution with scale parameter $\beta=20$, i.e. $C\sim Exp(20)$:
set.seed(123) survt <- round(rlnorm(300, 2, 1), 2) censt <- round(rexp(300, 1 / 20), 2)
The observed right-censored survival times, $Y = min(T, C)$, and the corresponding event indicators, $\delta = \boldsymbol{1}{T \leq C}$, are created as follows:
times <- pmin(survt, censt) delta <- as.numeric(survt <= censt)
In total, $106$ ($35.3\%$) of the survival times of the generated sample are right-censored:
table(delta)
Probability and quantile-quantile plots
The probability-probability plot (P-P plot) maps $\hat{F}_0(t)$ against $\hat{F}_n(t)$, where $\hat{F}_0(t)$ corresponds to $F_0(t)$ with the unknown parameters replaced by their maximum likelihood estimates. In order to enhance the interpretability of the P-P plot and because some of the plotted points have a larger variability than others, Stabilised Probability plot (SP plot) are used. In the same way as the arcsin transformation can be used to stabilise the variance of a uniform order statistic, this function can be applied to stabilise the variance of $\hat{F}_0(t)$. The variances of the resulting SP plotted points are all approximately equal.
The quantile-quantile plot (Q-Q plot) maps the theoretical quantiles against the estimated quantiles, that is, $\hat{F}_0^{-1}(\hat{F}_n(t))$ against $t$. An empirical rescaling of the axes can help to overcome the problem that the plotted points might not be evenly spread, in particular in the presence of right-censored data. The Empirically Rescaled plot (ER plot) plots $\hat{F}_u(\hat{F}_0^{-1}(\hat{F}_n(t)))$ against $\hat{F}_u(t)$, where the latter is the empirical cumulative distribution function of the points corresponding to the uncensored observations.
The four plots exposed above (P-P, Q-Q, SP and ER plot) can be done by means of the probPlot() function. By default, the function draws all four plots, but provides the user with the option to choose only a subset of these, and by default (prnt == TRUE), it returns the maximum likelihood estimates of the parameters of the distribution under study, with the corresponding standard errors, AIC and BIC. Notice that these estimates are used to draw the plots if no specific distribution parameters are provided. One graphical feature of the function is the option for the user to choose between the base package graphics, which is the default, and the ggplot2package for drawing the plots. In the former case, probPlot() internally calls the par() function, enabling the user to set graphical parameters within the function call.
For the illustration of the probPlot() function, we assess how well the log-normal distribution and the Weibull distribution fit to the sample data. The first four plots show fairly straight lines, thus confirming that the underlying distribution is the log-normal distribution, whereas the other plots clearly show that the Weibull distribution would not be an adequate distribution to model the data.
probPlot(times, delta, distr = "lognormal", prnt = FALSE, cex.lab = 1.3) probPlot(times, delta, distr = "weibull", ggp = TRUE, prnt = FALSE)
Note that since prnt is set to FALSE, the maximum likelihood estimates under of both distributions parameters are not returned.
To illustrate additional functionalities of this function, we apply it again on the data set to assess the suitability of fitting a log-normal distribution with parameters $\mu=2$ and $\beta=1$. For this purpose, we employ the params0 argument, whose default value is NULL, to specify the parameter values. Additionally, the main argument is a Surv object and we utilize the m argument to display the four probability plots in a single row.
probPlot(Surv(times, delta) ~ 1, distr = "lognormal", m = matrix(1:4, nrow = 1), params0 = list(location = 2, scale = 1.5), ggp = TRUE)
From these plots, it becomes evident that this log-normal distribution does not fit well to the data. Moreover, in this illustration, the function provides both the parameter values used in the probability plots and the maximum likelihood estimates of the parameters with the corresponding standard errors, AIC and BIC.
Cumulative hazard plot
Another set of plots consists of cumulative hazard plots, derived from transforming the cumulative hazard function $\Lambda$ in such a way that it becomes linear in $t$ or in $\log(t)$. The Nelson-Aalen estimator $\hat{\Lambda}$ of $\Lambda$ is computed from the data, and the distribution-specific transformation $A(\hat{\Lambda}(t))$ is then plotted against either $t$ or its natural logarithm.
The cumhazPlot() function can be used to draw cumulative hazard plots for any of the distributions available in the GofCens package. Given a set of survival times, this function is a helpful tool to assess which parametric model fits best to the given data.
By default, the function draws the cumulative hazard plots for the Weibull, Gumbel, log-logistic, logistic, log-normal, and normal distributions, but provides the user with the options to also choose the exponential and beta distribution or a subset of all these. Like the probPlot() function, the user can choose between the base package graphics, which is the default value, and the ggplot2 package to draw the plots, and by default, the maximum likelihood estimates of the distribution under study are returned with the corresponding standard errors, the AIC and the BIC.
An illustration with the previously generated right-censored sample from the log-normal distribution is shown. As expected, the points of the cumulative hazard plot for the log-normal distribution show a fairly straight line, whereas most of the other distributions under study can clearly be discarded. The corresponding code is the following. Different from the previous example, the function returns the parameter estimated for each of the six default distributions.
cumhazPlot(times, delta, font.lab = 4, cex.lab = 1.3)
If the user wants to assess the goodness of fit of the exponential and the beta distribution and compare these with the fit of the log-normal distribution, she/he has to specifically choose these distributions as it is done in the following example. Notice therein, that the user has to provide the limits of the beta distribution, because the default domain, the interval $(0, 1)$, does not cover the range of the sample’s survival times. Moreover, in this example, the plots are drawn with the ggplot2 package (ggp = TRUE) and the parameter estimates will not be returned on the screen (prnt = FALSE). We can see that neither the exponential nor the beta distribution fit well to the data. The corresponding code is the following.
cumhazPlot(times, delta, distr = c("exponential", "beta", "lognormal"), betaLimits = c(0, 100), ggp = TRUE, prnt = FALSE)
Kaplan-Meier comparison
The GofCens package includes a function to graphically compare the Kaplan-Meier estimate of the survival function $(1-\hat{F}_n(t))$ with the parametric estimations of the survival function from the parametric models under study $(1-\hat{F}_0(t))$. This function is the kmplot().
For this purpose, each parametric estimation is individually added to the Kaplan-Meier estimator of $S(t)$. Likewise the cumhazPlot() function, this is done by default for the Weibull, Gumbel, log-logistic, logistic, log-normal, and normal distributions. Additionally, users have the option to choose the exponential and beta distributions. By default, the plots are generated using the graphics package, but setting the argument ggp = TRUE allows for the use of the ggplot2 package. With both options, the pointwise $95\%$ confidence intervals are plotted.
kmPlot(times, delta, ggp = TRUE, prnt = FALSE)
As expected, the log-normal distribution exhibits the best fit to the data. Nevertheless, based on the plots in this figure one might also consider the log-logistic distribution as a parametric model that fits well to the sample data.
Real data example: Survival times of retired NBA players
In this section, we apply the above functions of the GofCens package to determine which parametric model fits best to the survival times of former NBA players.
The data frame nba comes with the GofCens package and contains the survival times (variable survtime) of all $3962$ former players of the of the National Basketball Association (NBA) until July 2019. Survival times are measured as the elapsed time (in years) from the end of the NBA career until either death (cens == 1) or July 31, 2019 (cens == 1). By this date, $864$ ($21.8\%$) of the former players had died with uncensored post-NBA survival times ranging from a few days until nearly 70 years.
In order to estimate the median and model the survival times parametrically, we need to know which distribution is the most appropriate distribution. For this purpose, we take advantage of the cumhazPlot() function, which provides the following six cumulative hazard plots:
data("nba") cumhazPlot(Surv(survtime, cens) ~ 1, nba, font.lab = 4, cex.lab = 1.3, prnt = FALSE, lwd = 3, colour = "blue")
According to the cumulative hazard plots above, the logistic distribution fits reasonably well to the data, even though the corresponding plot does not show a completely straight line of the points. In addition, we could also consider the normal distribution for parametric analyses of the survival times. To choose one of either distributions, we run the following code to draw probability and quantile-quantile plots with the probPlot() function. In addition, we estimate the parameter of the logistic distribution fitted to the data.
probPlot(Surv(survtime, cens) ~ 1, nba, distr = "logistic", ggp = TRUE, degs = 2) probPlot(Surv(survtime, cens) ~ 1, nba, distr = "normal", ggp = TRUE, prnt = FALSE)
According to the plots exposed above, the logistic distribution appears to be a slightly better choice than the normal distribution, as the points in all four plots for the normal distribution show some curvature.
Note that in the previous function calls, we used the formula() versions of both functions, which allow for the inclusion of the data frame nba in the argument list, which simplifies their use.
Try the GofCens package in your browser
Any scripts or data that you put into this service are public.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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