g. To prepare your data for cross-validation, use the R commands> a <- as.data.frame(s256i$data)> s <- SpatialPointsDataFrame(s256i$coords, a, proj4string=CRS(projargs=as.character(NA)), match.ID=TRUE)> v.fit <- as.vgm.variomodel(ols.n)where ols.n is the variogram fitted by the ordinary least squares method.Cross-validate your model by using leave-one-out cross-validation and a bubble plot of the result.(0.5 mark)h. Explain the obtained plot.(1 mark)i. Cross-validate your model by using 10-fold cross-validation and a bubble plot of the result. Explain the obtained plot. and differences with leave-one-out cross-validation from f and g.(1 mark)j. Optimize the monitoring network using the criterion of minimum mean kriging variances. Which data point has the maximum mean kriging variance?(0.5 mark)
Question
g. To prepare your data for cross-validation, use the R commands> a <- as.data.frame(s256icoords, a, proj4string=CRS(projargs=as.character(NA)), match.ID=TRUE)> v.fit <- as.vgm.variomodel(ols.n)where ols.n is the variogram fitted by the ordinary least squares method.Cross-validate your model by using leave-one-out cross-validation and a bubble plot of the result.(0.5 mark)h. Explain the obtained plot.(1 mark)i. Cross-validate your model by using 10-fold cross-validation and a bubble plot of the result. Explain the obtained plot. and differences with leave-one-out cross-validation from f and g.(1 mark)j. Optimize the monitoring network using the criterion of minimum mean kriging variances. Which data point has the maximum mean kriging variance?(0.5 mark)
Solution
I'm sorry, but as an AI, I'm not currently able to execute R commands or generate plots. However, I can guide you through the steps you'd need to take to accomplish this.
g. To prepare your data for cross-validation, you're already on the right track with the R commands you've provided. After you've prepared your data, you can use the "leave-one-out" cross-validation method. This involves using a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. You can use the loo() function from the cvms package to do this. To create a bubble plot of the result, you can use the bubble() function from the sp package.
h. The obtained plot will show the distribution of your data, with the size of the bubbles representing the magnitude of the error for each point. The larger the bubble, the larger the error.
i. For 10-fold cross-validation, you would divide your data into 10 subsets, and the holdout method is repeated 10 times. Each time, one of the 10 subsets is used as the test set and the other 9 subsets form the training set. Then you can average the results to produce a single estimation. The differences in the results from the leave-one-out and 10-fold cross-validation methods could be due to the fact that the latter provides a less biased estimate of the model skill, but it also has a higher variance since the evaluation of the skill of the model is equally sensitive to the division of the data.
j. To optimize the monitoring network using the criterion of minimum mean kriging variances, you would use the optim() function in R. The data point with the maximum mean kriging variance would be the one with the highest uncertainty or error in prediction. This can be identified by looking at the kriging variance for each data point.
Similar Questions
What can be used to check if the regression model fits the data well?Select an option Clear ResponseLasso regressionCross validationResidual analysisAdjusted R square, MSE
Cross-validation is used to: Test a model on new data Train a model on multiple datasets Evaluate model performance on a held-out test set Simulate the training process
Use data s256i from the package geoR. This is the simulated data set with the coordinates of data locations and numeric data at them.a. Produce a sample variogram on the interval [0,1] using 20 bins.(0.5 mark)b. Fit the spherical variogram to the sample variogram by using ordinary least squares. Use the initial values (1, 0.5) and nugget = 0.5.(0.5 mark)c. Consider the location (1, 0.5). Plot locations of the data in black and this location in red in the same image.(0.5 mark)d. Use the kriging method to compute the predicted value and the variance at the point (1, 0.5). Round the answers with 4 decimal places. (0.5 mark)e. Perform a prediction(kriging) on a grid covering the area [0,2]x[0,2]. Plot the result.(0.5 mark)f. Explain the obtained plot.(1 mark)g. To prepare your data for cross-validation, use the R commands> a <- as.data.frame(s256i$data)> s <- SpatialPointsDataFrame(s256i$coords, a, proj4string=CRS(projargs=as.character(NA)), match.ID=TRUE)> v.fit <- as.vgm.variomodel(ols.n)where ols.n is the variogram fitted by the ordinary least squares method.Cross-validate your model by using leave-one-out cross-validation and a bubble plot of the result.
Use data s256i from the package geoR. This is the simulated data set with the coordinates of data locations and numeric data at them.a. Produce a sample variogram on the interval [0,1] using 20 bins.(0.5 mark)b. Fit the spherical variogram to the sample variogram by using ordinary least squares. Use the initial values (1, 0.5) and nugget = 0.5.(0.5 mark)c. Consider the location (1, 0.5). Plot locations of the data in black and this location in red in the same image.(0.5 mark)d. Use the kriging method to compute the predicted value and the variance at the point (1, 0.5). Round the answers with 4 decimal places. (0.5 mark)e. Perform a prediction(kriging) on a grid covering the area [0,2]x[0,2]. Plot the result.(0.5 mark)f. Explain the obtained plot.(1 mark)g. To prepare your data for cross-validation, use the R commands> a <- as.data.frame(s256i$data)> s <- SpatialPointsDataFrame(s256i$coords, a, proj4string=CRS(projargs=as.character(NA)), match.ID=TRUE)> v.fit <- as.vgm.variomodel(ols.n)where ols.n is the variogram fitted by the ordinary least squares method.Cross-validate your model by using leave-one-out cross-validation and a bubble plot of the result.(0.5 mark)h. Explain the obtained plot.(1 mark)i. Cross-validate your model by using 10-fold cross-validation and a bubble plot of the result. Explain the obtained plot. and differences with leave-one-out cross-validation from f and g.(1 mark)j. Optimize the monitoring network using the criterion of minimum mean kriging variances. Which data point has
Time Series Cross-Validation performs validation using historical data for training and the most recent data for validation.Review LaterTrueFalse
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.