For this vignette, we will use the final model achieved in the vignette workflow as an example.
modelFinal <- DImulti(y = c("Y1", "Y2", "Y3"), eco_func = c("NA", "UN"), time = c("time", "CS"),
unit_IDs = 1, prop = 2:5, data = simMVRM, DImodel = "AV", method = "REML")
print(modelFinal)#> Note:
#> Method Used = REML
#> Correlation Structure Used = UN@CS
#> Average Term Model
#> Theta value(s) = 1
#>
#> Generalized least squares fit by REML
#> Model: value ~ 0 + func:time:((p1_ID + p2_ID + p3_ID + p4_ID + AV))
#> AIC BIC logLik
#> 7929.642 8103.053 -3933.821
#>
#> Multivariate Correlation Structure: General
#> Formula: ~0 | plot
#> Parameter estimate(s):
#> Correlation:
#> 1 2
#> 2 0.612
#> 3 -0.311 -0.364
#>
#> Repeated Measure Correlation Structure: Compound symmetry
#> Formula: ~0 | plot
#> Parameter estimate(s):
#> Rho
#> 0.3161182
#>
#>
#> Table: Fixed Effect Coefficients
#>
#> Beta Std. Error t-value p-value Signif
#> ------------------- -------- ----------- -------- ----------- -------
#> funcY1:time1:p1_ID -1.407 0.402 -3.505 0.0004669 ***
#> funcY2:time1:p1_ID +0.354 0.402 0.880 0.3787
#> funcY3:time1:p1_ID +0.946 0.402 2.357 0.01853 *
#> funcY1:time2:p1_ID +0.142 0.402 0.353 0.7238
#> funcY2:time2:p1_ID +2.630 0.402 6.549 7.354e-11 ***
#> funcY3:time2:p1_ID -0.552 0.402 -1.375 0.1693
#> funcY1:time1:p2_ID +4.770 0.372 12.824 3.149e-36 ***
#> funcY2:time1:p2_ID +4.273 0.372 11.488 1.273e-29 ***
#> funcY3:time1:p2_ID +6.704 0.372 18.023 2.221e-67 ***
#> funcY1:time2:p2_ID +4.976 0.372 13.378 3.818e-39 ***
#> funcY2:time2:p2_ID +2.707 0.372 7.279 4.817e-13 ***
#> funcY3:time2:p2_ID +6.806 0.372 18.297 3.09e-69 ***
#> funcY1:time1:p3_ID +2.665 0.404 6.602 5.191e-11 ***
#> funcY2:time1:p3_ID -0.766 0.404 -1.896 0.05807 +
#> funcY3:time1:p3_ID +3.321 0.404 8.226 3.472e-16 ***
#> funcY1:time2:p3_ID +4.394 0.404 10.883 7.839e-27 ***
#> funcY2:time2:p3_ID -3.350 0.404 -8.298 1.925e-16 ***
#> funcY3:time2:p3_ID +3.008 0.404 7.452 1.369e-13 ***
#> funcY1:time1:p4_ID -0.900 0.453 -1.984 0.04738 *
#> funcY2:time1:p4_ID +0.325 0.453 0.717 0.4734
#> funcY3:time1:p4_ID +4.924 0.453 10.859 1.005e-26 ***
#> funcY1:time2:p4_ID -2.504 0.453 -5.522 3.79e-08 ***
#> funcY2:time2:p4_ID +2.019 0.453 4.453 8.958e-06 ***
#> funcY3:time2:p4_ID +3.544 0.453 7.816 8.783e-15 ***
#> funcY1:time1:AV +2.892 0.984 2.938 0.003342 **
#> funcY2:time1:AV +7.938 0.984 8.065 1.254e-15 ***
#> funcY3:time1:AV +6.184 0.984 6.283 4.07e-10 ***
#> funcY1:time2:AV +34.080 0.984 34.624 6.175e-206 ***
#> funcY2:time2:AV +4.827 0.984 4.904 1.016e-06 ***
#> funcY3:time2:AV +18.944 0.984 19.247 7.827e-76 ***
#>
#> Signif codes: 0-0.001 '***', 0.001-0.01 '**', 0.01-0.05 '*', 0.05-0.1 '+', 0.1-1.0 ' '
#>
#> Degrees of freedom: 2016 total; 1986 residual
#> Residual standard error: 1.951116
#>
#> $Multivariate
#> Marginal variance covariance matrix
#> [,1] [,2] [,3]
#> [1,] 4.9140 2.3738 -1.3783
#> [2,] 2.3738 3.0605 -1.2758
#> [3,] -1.3783 -1.2758 4.0093
#> Standard Deviations: 2.2168 1.7494 2.0023
#>
#> $`Repeated Measure`
#> Marginal variance covariance matrix
#> [,1] [,2]
#> [1,] 4.6263 1.4625
#> [2,] 1.4625 4.6263
#> Standard Deviations: 2.1509 2.1509
#>
#> $Combined
#> Marginal variance covariance matrix
#> Y1:1 Y1:2 Y2:1 Y2:2 Y3:1 Y3:2
#> Y1:1 3.80690 1.20340 2.33020 0.73662 -1.18210 -0.37368
#> Y1:2 1.20340 3.80690 0.73662 2.33020 -0.37368 -1.18210
#> Y2:1 2.33020 0.73662 3.80690 1.20340 -1.38640 -0.43828
#> Y2:2 0.73662 2.33020 1.20340 3.80690 -0.43828 -1.38640
#> Y3:1 -1.18210 -0.37368 -1.38640 -0.43828 3.80690 1.20340
#> Y3:2 -0.37368 -1.18210 -0.43828 -1.38640 1.20340 3.80690
#> Standard Deviations: 1.9511 1.9511 1.9511 1.9511 1.9511 1.9511
To predict for any data from this model, which has custom class DImulti, we use the predict() function, which is formatted as below, where object is the DImulti model object, newdata is a dataframe or tibble containing the community designs that you wish to predict from, if left NULL then the data used to train the model will be predicted from instead, and stacked is a boolean which determines whether the output from this function will be given in a stacked/long format (TRUE) or wide format (FALSE).
The first option for prediction is to simply provide the model object to the function to predict from the dataframe we used to train it (simMVRM). By default, the prediction dataframe is output in a stacked format, as it is more commonly used for plotting than a wide output.
#> plot Yvalue Ytype
#> 1 1 -1.4073525 Y1:1
#> 2 1 0.1419124 Y1:2
#> 3 1 0.3535514 Y2:1
#> 4 1 2.6296879 Y2:2
#> 5 1 0.9463605 Y3:1
#> 6 1 -0.5521430 Y3:2
If we would rather a wide output, which can be easier to infer from without plotting, we can set stacked = FALSE.
#> plot Y1:1 Y1:2 Y2:1 Y2:2 Y3:1 Y3:2
#> 1 1 -1.407353 0.1419124 0.3535514 2.629688 0.9463605 -0.552143
#> 2 2 -1.407353 0.1419124 0.3535514 2.629688 0.9463605 -0.552143
#> 3 3 -1.407353 0.1419124 0.3535514 2.629688 0.9463605 -0.552143
#> 4 4 4.769928 4.9758685 4.2730527 2.707455 6.7037708 6.805558
#> 5 5 4.769928 4.9758685 4.2730527 2.707455 6.7037708 6.805558
#> 6 6 4.769928 4.9758685 4.2730527 2.707455 6.7037708 6.805558
We can also provide some subset of the original dataset rather than using it all.
#> plot Yvalue Ytype
#> 1 1 -1.4073525 Y1:1
#> 2 1 0.3535514 Y2:1
#> 3 1 0.9463605 Y3:1
#> 4 4 4.7699282 Y1:1
#> 5 4 4.2730527 Y2:1
#> 6 4 6.7037708 Y3:1
#> 7 7 2.6653214 Y1:1
#> 8 7 -0.7655644 Y2:1
#> 9 7 3.3207690 Y3:1
#> 10 10 -0.8997879 Y1:1
#> 11 10 0.3252207 Y2:1
#> 12 10 4.9242752 Y3:1
#> 13 21 2.9732237 Y1:1
#> 14 21 3.9330478 Y2:1
#> 15 21 5.7183530 Y3:1
Or we can use a dataset which follows the same format as simMVRM but is entirely new data. If no information is supplied for which ecosystem functions or time points from which you wish to predict, then all will be included automatically.
newSim <- data.frame(plot = c(1, 2),
p1 = c(0.25, 0.6),
p2 = c(0.25, 0.2),
p3 = c(0.25, 0.1),
p4 = c(0.25, 0.1))
predict(modelFinal, newdata = newSim)#> plot Yvalue Ytype
#> 1 1 2.366443 Y1:1
#> 2 1 14.531716 Y1:2
#> 3 1 4.023354 Y2:1
#> 4 1 2.811557 Y2:2
#> 5 1 6.292853 Y3:1
#> 6 1 10.305704 Y3:2
#> 7 2 1.124742 Y1:1
#> 8 2 11.152407 Y1:2
#> 9 2 3.324757 Y2:1
#> 10 2 3.385953 Y2:2
#> 11 2 4.526481 Y3:1
#> 12 2 7.178993 Y3:2
Otherwise, only the ecosystem functions/time points specified will be predicted from. As our dataset is in a wide format, we will need to supply some arbitrary value to our desired ecosystem function column.
newSim <- data.frame(plot = c(1, 2),
p1 = c(0.25, 0.6),
p2 = c(0.25, 0.2),
p3 = c(0.25, 0.1),
p4 = c(0.25, 0.1),
Y1 = 0)
predict(modelFinal, newdata = newSim)#> plot Yvalue Ytype
#> 1 1 2.366443 Y1:1
#> 2 1 14.531716 Y1:2
#> 3 2 1.124742 Y1:1
#> 4 2 11.152407 Y1:2
In the case that some information is missing from this new data, the function will try to set a value for the column and will inform the user through a warning printed to the console.
newSim <- data.frame(p1 = c(0.25, 0.6),
p2 = c(0.25, 0.2),
p3 = c(0.25, 0.1),
p4 = c(0.25, 0.1))
predict(modelFinal, newdata = newSim)#> Warning in predict.DImulti(modelFinal, newdata = newSim): The column containing
#> unit_IDs has not been supplied through newdata. This column is required as a
#> grouping factor for the covarying responses, although its value does not matter
#> as there is no between subject effect included. Defaulting to row numbers.
#> plot Yvalue Ytype
#> 1 1 2.366443 Y1:1
#> 2 1 14.531716 Y1:2
#> 3 1 4.023354 Y2:1
#> 4 1 2.811557 Y2:2
#> 5 1 6.292853 Y3:1
#> 6 1 10.305704 Y3:2
#> 7 2 1.124742 Y1:1
#> 8 2 11.152407 Y1:2
#> 9 2 3.324757 Y2:1
#> 10 2 3.385953 Y2:2
#> 11 2 4.526481 Y3:1
#> 12 2 7.178993 Y3:2
You may wish to merge your predictions to your newdata dataframe for plotting, printing, or further analysis. As the function DImulti(), and as a consequence, the function predict.DImulti(), sorts the data it is provided, to ensure proper labelling, you may not be able to directly use cbind() to append the predictions to your dataset. In this case, ensure the unit_IDs column contains unique identifiers for your data rows and that you specify stacked to correctly match your data layout. Then use the function merge().
newSim <- data.frame(plot = c(1, 2),
p1 = c(0.25, 0.6),
p2 = c(0.25, 0.2),
p3 = c(0.25, 0.1),
p4 = c(0.25, 0.1))
preds <- predict(modelFinal, newdata = newSim, stacked = FALSE)
merge(newSim, preds, by = "plot")#> plot p1 p2 p3 p4 Y1:1 Y1:2 Y2:1 Y2:2 Y3:1
#> 1 1 0.25 0.25 0.25 0.25 2.366443 14.53172 4.023354 2.811557 6.292853
#> 2 2 0.60 0.20 0.10 0.10 1.124742 11.15241 3.324757 3.385953 4.526481
#> Y3:2
#> 1 10.305704
#> 2 7.178993
In the case that your newdata contains non-unique unit_IDs values and stacked = FALSE, any rows with common unit_IDs will be aggregated using the mean() function.
newSim <- data.frame(plot = c(1, 1),
p1 = c(0.25, 0.6),
p2 = c(0.25, 0.2),
p3 = c(0.25, 0.1),
p4 = c(0.25, 0.1))
predict(modelFinal, newdata = newSim, stacked = FALSE)#> plot Y1:1 Y1:2 Y2:1 Y2:2 Y3:1 Y3:2
#> 1 1 1.745592 12.84206 3.674056 3.098755 5.409667 8.742349