I have been using lppm (point pattern on a linear network) on spatstat with bunch of covariates and fitting a log-linear model but I couldn't see how to check over-fitting. Is there a quick way to do it?
It depends on what you want.
What tool would you use to check overfitting in (say) a linear model?
To identify whether individual observations may have been over-fitted, you could use influence.lppm (from the spatstat.linnet package).
To identify collinearity in the covariates, currently we do not provide a dedicated function in spatstat, but you could use the following trick. If fit is your fitted model of class lppm, first extract the corresponding GLM using
g <- getglmfit(as.ppm(fit))
Next install the package faraway and use the vif function to calculate the variance inflation factors
library(faraway)
vif(g)
Related
I have a huge dataset and I'm trying to build a good predictive linear model using the relaimpo package.
Using the calc.relimp function with type="lmg, i get an output of variables which are of relative importance. Although the proportion of variance explained by the model is only at 52%, I want to go and build a linear model using these variables.
Is there a way to build a lm model using these variables and somehow take into account the relative importance values into the model?
I'm not too familiar with this and was thinking maybe something along the lines of weighting each variable based on its relative importance value...?
I'm not a statistician, so I won't give you any Greek symbols, but I think you are confusing a few things.
As you correctly say, the relative importances based on the LMG method are more or less some sort of variance decomposition in case of correlated predictor variables, i.e. it tells you how much of your variance in the model is explained by which predictor.
However, this doesn't have to do anything with the lm function and its estimation itself. In fact, the R² of your lm model is exactly the same as you'll get by summing up the relative importances from calc.relimp.
There is no way to tell the lm function to pay more attention to a certain predictor during prediction/estimation.
What you probably want to do is an elastic net (which is a combination of LASSO and RIDGE regression), which basically does what you want, i.e. it shrinks the impact of "unimportant"/small predictors and emphasizes the impact of important/large predictors: https://en.wikipedia.org/wiki/Elastic_net_regularization (Lasso and Ridge regression are linked in the Wikipedia article).
I think this one here is the original package from Jerome Friedman, Trevor Hastie, Rob Tibshirani, et al.: https://cran.r-project.org/web/packages/glmnet/index.html
I want to fit a spatiotemporal model where my dependent variable is in the range [>0,<1].
A beta regression seems suitable for this case.
I tried the betareg package, that works like a charm, but to my knowledge I cannot include complex interaction terms that occur e.g. in spatiotemporal datasets to account for autocorrelation.
I know that GAMs e.g. package mgcv support beta regression via the betar() family. To my knowledge the precision/variance parameter is held constant though and only the mean (mu) changes as a function of the predictors.
my model looks like this (it is conceptual so no example data needed):
mgcv::gam(Y~ te(latitude,longitude,day)+s(X1)+s(X2)+s(X3),family=betar())
The problem is that only mu is modelled but not phi / precision
In the betareg I can let vary phi with my predictors:
betareg::betareg(Y ~ X1+X2+X3+latitude+longitude | X1+X2+X3+latitude+longitude)
but this doesn´t let me model the spatiotemporal term as needed, because simple additive effects are not suitable for that and I need something like what is supported with the te() functionality from mgcv or any other kind of interaction term.
Is there any work around or a way to model phi but account for my spatiotemporal term either via mgcv or betareg or any other R package?
Thanks a lot!
I am using R along with the neuralnet package see docs (https://cran.r-project.org/web/packages/neuralnet/neuralnet.pdf). I have used the neural network function to build and train my model.
Now I have built my model I want to test it on real data. Could someone explain if I should use the compute or prediction function? I have read the documentation and it isnt clear, both functions seem to do similar?
Thanks
The short answer is to use compute to do predictions.
You can see an example of using compute on the test set here. We can also see that compute is the right one from the documentation:
compute, a method for objects of class nn, typically produced by neuralnet. Computes the outputs
of all neurons for specific arbitrary covariate vectors given a trained neural network.
The above says that you can use covariate vectors in order to compute the output of the neural network i.e. make a prediction.
On the other hand prediction does what is mentioned in the title in the documentation:
Summarizes the output of the neural network, the data and the fitted
values of glm objects (if available)
Moreover, it only takes two arguments: the nn object and a list of glm models so there isn't a way to pass in the test set in order to make a prediction.
Background
The reference manual for the gbm package states the interact.gbm function computes Friedman's H-statistic to assess the strength of variable interactions. the H-statistic is on the scale of [0-1].
The reference manual for the dismo package does not reference any literature for how the gbm.interactions function detects and models interactions. Instead it gives a list of general procedures used to detect and model interactions. The dismo vignette "Boosted Regression Trees for ecological modeling" states that the dismo package extends functions in the gbm package.
Question
How does dismo::gbm.interactions actually detect and model interactions?
Why
I am asking this question because gbm.interactions in the dismo package yields results >1, which the gbm package reference manual says is not possible.
I checked the tar.gz for each of the packages to see if the source code was similar. It is different enough that I cannot determine if these two packages are using the same method to detect and model interactions.
To summarize, the difference between the two approaches boils down to how the "partial dependence function" of the two predictors is estimated.
The dismo package is based on code originally given in Elith et al., 2008 and you can find the original source in the supplementary material. The paper very briefly describes the procedure. Basically the model predictions are obtained over a grid of two predictors, setting all other predictors at their means. The model predictions are then regressed onto the grid. The mean squared errors of this model are then multiplied by 1000. This statistic indicates departures of the model predictions from a linear combination of the predictors, indicating a possible interaction.
From the dismo package, we can also obtain the relevant source code for gbm.interactions. The interaction test boils down to the following commands (copied directly from source):
interaction.test.model <- lm(prediction ~ as.factor(pred.frame[,1]) + as.factor(pred.frame[,2]))
interaction.flag <- round(mean(resid(interaction.test.model)^2) * 1000,2)
pred.frame contains a grid of the two predictors in question, and prediction is the prediction from the original gbm fitted model where all but two predictors under consideration are set at their means.
This is different than Friedman's H statistic (Friedman & Popescue, 2005), which is estimated via formula (44) for any pair of predictors. This is essentially the departure from additivity for any two predictors averaging over the values of the other variables, NOT setting the other variables at their means. It is expressed as a percent of the total variance of the partial dependence function of the two variables (or model implied predictions) so will always be between 0-1.
I'm experimenting with Bayesian networks in R and have built some networks using the bnlearn package. I can use them to make predictions for new observations with predict(), however I would also like to have the posterior distribution over the possible classes. Is there a way of retrieving this information?
It seems like there is a prob-parameter that does this for the naive bayes implementation in the bnlearn package, but not for networks fitted with bn.fit.
Thankful for any help with this.
See the documentation of bnlearn.
predict function implements prob only for naive.bayes and TAN.
In short, because all other methods do not necessarily compute posterior probabilities.
[bnlearn] :: predict returns the predicted values for node given the data specified by data. Depending on the
value of method, the predicted values are computed as follows:
a)parents b)bayes-lw
When using bayes-lw , likelihood weighting simulations are performed for making predictions.
Hope this helps. :)