<p>Representative examples of the forecast performances of the models for the lung data set (mouse number 2). Five data points were used to estimate the animal parameters and predict future growth. Prediction success of the models are reported for the next day data point (OK<sub>1</sub>) or global future curve (OK<sub>glob</sub>), based on the criterion of a normalized error smaller than 3 (meaning that the model prediction is within 3 standard deviations of the measurement error) for OK<sub>1</sub> and the median of this metric over the future curve for OK<sub>glob</sub> (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003800#s2" target="_blank">Materials and Methods</a> for details).</p
<p>Models were ranked in ascending order of the <i>RMSE</i>, defined by expression (13). For each me...
<div><p>Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be...
We discuss prediction of random effects and of expected responses in multilevel generalized linear m...
<p>Models are presented in descending order of overall mean success (defined in (18)). , defined in ...
<p>Predictions were considered when randomly dividing the animals between two equal groups, one used...
<p>Predictive power of some representative models depending on the number of data points used for es...
<p>Models are presented in descending order of overall mean success (defined in (18)). , defined in ...
<p>A. Representative examples of all growth models fitting the same growth curve (animal 10 for lung...
The problem of predicting a future measurement on an individual given the past measurements is discu...
(A) Log-likelihood ratio of model against the null random baseline model based on predicting histori...
The performance of prediction models can be assessed using a variety of methods and metrics. Traditi...
textabstractThe performance of prediction models can be assessed using a variety of methods and metr...
In the previous chapter, you have learned how to prepare your data before you start the process of g...
<p>Prediction models play an increasingly important role in clinical and shared decision making. In ...
We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tr...
<p>Models were ranked in ascending order of the <i>RMSE</i>, defined by expression (13). For each me...
<div><p>Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be...
We discuss prediction of random effects and of expected responses in multilevel generalized linear m...
<p>Models are presented in descending order of overall mean success (defined in (18)). , defined in ...
<p>Predictions were considered when randomly dividing the animals between two equal groups, one used...
<p>Predictive power of some representative models depending on the number of data points used for es...
<p>Models are presented in descending order of overall mean success (defined in (18)). , defined in ...
<p>A. Representative examples of all growth models fitting the same growth curve (animal 10 for lung...
The problem of predicting a future measurement on an individual given the past measurements is discu...
(A) Log-likelihood ratio of model against the null random baseline model based on predicting histori...
The performance of prediction models can be assessed using a variety of methods and metrics. Traditi...
textabstractThe performance of prediction models can be assessed using a variety of methods and metr...
In the previous chapter, you have learned how to prepare your data before you start the process of g...
<p>Prediction models play an increasingly important role in clinical and shared decision making. In ...
We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tr...
<p>Models were ranked in ascending order of the <i>RMSE</i>, defined by expression (13). For each me...
<div><p>Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be...
We discuss prediction of random effects and of expected responses in multilevel generalized linear m...