Research Proposals: A Guide to Success

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One thing this tells us is that when the standard deviations of x and y are the same e. If we want to make inferences about the regression parameter estimates, then we also need an estimate of their variability. To compute this, we first need to compute the residual variance or error variance for the model — that is, how much variability in the dependent variable is not explained by the model.

We can compute the model residuals as follows:. Once we have the mean squared error, we can compute the standard error for the model as:. Once we have the parameter estimates and their standard errors, we can compute a t statistic to tell us the likelihood of the observed parameter estimates compared to some expected value under the null hypothesis. In this case we will test against the null hypothesis of no effect i.

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In this case we see that the intercept is significantly different from zero which is not very interesting and that the effect of studyTime on grades is marginally significant. If there is only one x variable, then this is easy to compute by simply squaring the correlation coefficient:. Using this, we can then compute the coefficient of determination as:. Often we would like to understand the effects of multiple variables on some particular outcome, and how they relate to one another.

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If we plot their grades see Figure We would like to build a statistical model that takes this into account, which we can do by extending the model that we built above:. The blue line shows the slope relating grades to study time, and the black dotted line corresponds to the difference in means between the two groups. In the previous model, we assumed that the effect of study time on grade i. However, in some cases we might imagine that the effect of one variable might differ depending on the value of another variable, which we refer to as an interaction between variables.

Looking at Figure As we see from Figure Here we see there are no significant effects of either caffeine or anxiety, which might seem a bit confusing. The problem is that this model is trying to fit the same line relating speaking to caffeine for both groups. From these results we see that there are significant effects of both caffeine and anxiety which we call main effects and an interaction between caffeine and anxiety.

This results in two lines that separately model the slope for each group. Sometimes we want to compare the relative fit of two different models, in order to determine which is a better model; we refer to this as model comparison.

For the models above, we can compare the goodness of fit of the model with and without the interaction, using the anova command in R:. This tells us that there is good evidence to prefer the model with the interaction over the one without an interaction. Model comparison is relatively simple in this case because the two models are nested — one of the models is a simplified version of the other model. Model comparison with non-nested models can get much more complicated.

This has an unfortunate connotation, as it implies that our model should also be able to predict the values of new data points in the future. In reality, the fit of a model to the dataset used to obtain the parameters will nearly always be better than the fit of the model to a new dataset Copas Here we see that whereas the model fit on the original data showed a very good fit only off by a few pounds per individual , the same model does a much worse job of predicting the weight values for new children sampled from the same population off by more than 25 pounds per individual.

This happens because the model that we specified is quite complex, since it includes not just each of the individual variables, but also all possible combinations of them i. Since this is almost as many coefficients as there are data points i. Another way to see the effects of overfitting is to look at what happens if we randomly shuffle the values of the weight variable.

Randomly shuffling the value should make it impossible to predict weight from the other variables, because they should have no systematic relationship. This shows us that even when there is no true relationship to be modeled because shuffling should have obliterated the relationship , the complex model still shows a very low error in its predictions, because it fits the noise in the specific dataset.

However, when that model is applied to a new dataset, we see that the error is much larger, as it should be. One method that has been developed to help address the problem of overfitting is known as cross-validation. The idea behind cross-validation is that we fit our model repeatedly, each time leaving out a subset of the data, and then test the ability of the model to predict the values in each held-out subset.

The caret package in R provides us with the ability to easily run cross-validation across our dataset:. We can also confirm that cross-validation accurately estimates the predictive accuracy when the dependent variable is randomly shuffled:. Here again we see that cross-validation gives us an assessment of prediction accuracy that is much closer to what we expect with new data, and again even somewhat more pessimistic. In order to balance the training samples across the full range of model values, the training samples are evenly drawn from each decile of the predictor variable.

This prevents over-sampling of ocean grid cells, which are typically characterized by very uniform chemistry. Our results show very little sensitivity to the size of the training sample as long as it covers the full solution space. After training, all forest data i. Each grid cell calls the same random forest emulator separately, passing to it all local information required to evaluate the trees species concentrations, photolysis rates, environmental variables.

No attempts were made to optimize the prediction algorithm beyond the existing Message Passing Interface grid-domain splitting. Most simplistically, we could predict the concentration of a species after the integration step.

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However, many of the species in the model are log-normally distributed in which case predicting the logarithm of the concentration may provide a more accurate solution; we could also predict the change in the concentration after the integrator, the fractional change in the concentration, the logarithm of the fractional change, etc.

After some trial and error, and based on chemical considerations, we choose two types of prediction: the change in concentration after going through the integrator, and the concentration after the integrator. This fits with the differential equation perspective for chemistry given in Eq. However, if we incorporate only this approach we find that errors rapidly accrue. For these compounds, concentrations can vary by many orders of magnitude over an hour, and even small errors in the tendencies build up quickly when they are included in the full model.

For these short-lived compounds, we use a second type of prediction where the RFR predicts the concentration of the compound after the integrator.

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We imitate this process by explicitly removing the predictor species from the input features, which we find improves performance. This ratio is relatively stable and close to 1. Based on trial and error, we use a standard deviation threshold of 0. Prediction type of each species concentration, tendency, NO x family treatment is given in the prediction column.

The importance of different input variables features for making a prediction of O 3 tendency is shown in Fig. The importance metric is the fraction of decisions in the forest that are made using a particular feature, with the variability indicating the standard deviation of that value between the trees.

From a chemical perspective, these features make sense given the global sources and sinks of O 3 in the lower to middle troposphere. Shown are the 20 most important features for the entire random forest, as averaged over all 30 decision trees. The black bars indicate the standard deviation for each feature across the 30 decision trees. The arrows indicate photolytic conversion i. N O 3 photolyses to NO 2 plus O. For ozone prediction, 6 out of the 20 most important input features are related to photolysis.

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Most of the photolysis rates are highly correlated, and the individual decision trees use different photolysis rates for decision making. This results in very large standard deviations for the photolysis input features across the 30 decision trees, as indicated by the black bars in Fig. Note that the concentration of O 3 is not among the 20 most important input features for the prediction of O 3 tendency. However, when predicting the ozone tendency, the random forest algorithm is more sensitive to availability of NO x , VOCs, photolysis, etc. Similarly, for regions loosing ozone the dominant source of variability is the variability in photolysis rates multiple orders of magnitude rather than the variability in O 3 concentration less than an order of magnitude.

The predictor is not perfect, with an R 2 of 0. However, as shown in Fig. For NO and NO 2 we find that the random forest has difficulties predicting the species concentrations independently of each other. Given the central role of NO x for tropospheric chemistry, a quick deterioration of model performance occurs see Sect. Thus, the overall number of forests that needs to be calculated does not change.

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The importance of SO 2 may reflect heterogeneous N 2 O 5 chemistry, with SO 2 being a proxy for available aerosol surface area note that we do not provide any aerosol information to the RFR. As shown in Fig. While the NRMSE is relatively high, we find that the ability of the model to produce an essentially unbiased prediction is more critical for the long-term stability of the model.

As for O 3 , the NO x skill scores become almost perfect when adding the tendency perturbations to the concentration before integration Fig. The performance of the NO-to- NO x ratio predictor is very good, and the prediction is also unbiased. We now move to a systematic evaluation of the performance of the RFR models, both against the validation data and when implemented back into the GEOS-Chem model.

We use three standard statistical metrics for this comparison. For each species c , we compute the Pearson correlation coefficent R 2 ,. Ten percent of the training data was withheld to form a validation data set.


For most species the RFR predictors do a good job of prediction: R 2 values are greater than 0. Those species which do less well are typically those that are shorter lived, such as inorganic bromine species or some nitrogen species NO 3 , N 2 O 5. The performance of NO and NO 2 after implementing the NO x family and ratio methodology is consistent with other key species. Although we do not have a perfect methodology for predicting some species, we believe that it does provide a useful approach to predicting the concentration of the transported species after the chemical integrator. As such, this experiment also evaluates the ability of the RFR model to capture the sensitivity of chemistry to changes in emissions.

The first simulation is a standard simulation where we use the standard GEOS-Chem integrator; the second is a simulation where we replace the chemical integrator with the RFR predictors described earlier with the family treatment of NO x ; the third uses the RFR predictors but directly predicts the NO and NO 2 concentrations instead of NO x ; the fourth has no tropospheric chemistry and the model just transports, emits and deposits species. This buffers the impact of the RFR emulator over the long-term since all simulations use the same relaxation scheme in the stratosphere.

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For the time frame of 1 month considered here, we consider this impact to be negligible in the lowest 25 model levels. We evaluate the performance of the second, third and fourth model configuration against the first. We first focus on the statistical evaluation of the best RFR model configuration second model configuration for all species and then turn our attention to the specific performance of surface O 3 and NO 2 , two critical air pollutants. For each time slice, we calculate a number of metrics Sect. For the stability of the simulation, it is more important to have an overall unbiased estimation, as this prevents systematic buildups or drawdowns in concentrations that can eventually render the model unstable.