Predict using a cv.ptLasso object.
predict.cv.ptLasso.RdReturn predictions and performance measures for a test set.
Arguments
- object
Fitted
"cv.ptLasso"object.- xtest
Input matrix, matching the form used by
"cv.ptLasso"for model training.- groupstest
A vector indicating to which group each observation belongs. Coding should match that used for model training. Will be NULL for target grouped data.
- ytest
Response variable. Optional. If included,
"predict"will compute performance measures for xtest using code"type.measure" from the cvfit object.- alpha
The chosen alpha to use for prediction. May be a vector containing one value of alpha for each group. If NULL, this will rely on the choice of "alphatype".
- alphatype
Choice of '"fixed"' or '"varying"'. If '"fixed"', use the alpha that achieved best cross-validated performance. If '"varying"', each group uses the alpha that optimized the group-specific cross-validated performance.
- type
Type of prediction required. Type '"link"' gives the linear predictors for '"binomial", '"multinomial"' or '"cox"' models; for '"gaussian"' models it gives the fitted values. Type '"response"' gives the fitted probabilities for '"binomial"' or '"multinomial"', and the fitted relative-risk for '"cox"'; for '"gaussian"' type '"response"' is equivalent to type '"link"'. Note that for '"binomial"' models, results are returned only for the class corresponding to the second level of the factor response. Type '"class"' applies only to '"binomial"' or '"multinomial"' models, and produces the class label corresponding to the maximum probability.
- s
Value of the penalty parameter 'lambda' at which predictions are required. Will use the same lambda for all models; can be a numeric value, '"lambda.min"' or '"lambda.1se"'. Default is '"lambda.min"'.
- gamma
For use only when 'relax = TRUE' was specified during training. Value of the penalty parameter 'gamma' at which predictions are required. Will use the same gamma for all models; can be a numeric value, '"gamma.min"' or '"gamma.1se"'. Default is '"gamma.min"'.
- return.link
If
TRUE, will additionally return the linear link for the overall, pretrained and individual models:linkoverall,linkpreandlinkind.- ...
other arguments to be passed to the
"predict"function.
Value
A list containing the requested predictions. If ytest is included, will also return error measures.
- call
The call that produced this object.
- alpha
The value(s) of alpha used to generate predictions.
- yhatoverall
Predictions from the overall model.
- yhatind
Predictions from the individual models.
- yhatpre
Predictions from the pretrained models.
- supoverall
Indices of the features selected by the overall model.
- supind
Union of the indices of the features selected by the individual models.
- suppre.common
Features selected in the first stage of pretraining.
- suppre.individual
Union of the indices of the features selected by the pretrained models, without the features selected in the first stage.
- type.measure
If
ytestis supplied, the performance measure computed.- erroverall
If
ytestis supplied, performance for the overall model. This is a named vector containing performance for (1) the entire dataset, (2) the average performance across groups, (3) the average performance across groups weighted by group size and (4) group-specific performance.- errind
If
ytestis supplied, performance for the overall model. As described inerroverall.- errpre
If
ytestis supplied, performance for the overall model. As described inerroverall.- linkoverall
If
return.linkis TRUE, return the linear link from the overall model.- linkind
If
return.linkis TRUE, return the linear link from the individual models.- linkpre
If
return.linkis TRUE, return the linear link from the pretrained models.
Examples
#### Gaussian example
set.seed(1234)
out = gaussian.example.data()
x = out$x; y=out$y; groups = out$group;
outtest = gaussian.example.data()
xtest=outtest$x; ytest=outtest$y; groupstest=outtest$groups;
# Model fitting
# By default, use the single value of alpha that had the best CV performance on the entire dataset:
cvfit = cv.ptLasso(x, y, groups = groups, family = "gaussian", type.measure = "mse")
pred = predict(cvfit, xtest, groupstest, ytest=ytest, s="lambda.min")
pred
#>
#> Call:
#> predict.cv.ptLasso(object = cvfit, xtest = xtest, groupstest = groupstest,
#> ytest = ytest, s = "lambda.min")
#>
#>
#> alpha = 0.5
#>
#> Performance (Mean squared error):
#>
#> allGroups mean group_1 group_2 group_3 group_4 group_5 r^2
#> Overall 755.7 755.7 836.0 554.9 565.4 777.9 1044.0 0.5371
#> Pretrain 500.2 500.2 539.0 443.8 553.5 502.5 462.4 0.6936
#> Individual 532.8 532.8 584.1 443.2 567.2 550.5 518.9 0.6736
#>
#> Support size:
#>
#> Overall 64
#> Pretrain 92 (21 common + 71 individual)
#> Individual 109
# For each group, use the value of alpha that had the best CV performance for that group:
pred = predict(cvfit, xtest, groupstest, ytest=ytest, s="lambda.min", alphatype = "varying")
pred
#>
#> Call:
#> predict.cv.ptLasso(object = cvfit, xtest = xtest, groupstest = groupstest,
#> ytest = ytest, alphatype = "varying", s = "lambda.min")
#>
#>
#>
#> alpha:
#> [1] 0.5 0.5 0.1 1.0 0.2
#>
#>
#> Performance (Mean squared error):
#> overall mean wtdMean group_1 group_2 group_3 group_4 group_5
#> Overall 755.7 755.7 755.7 836.0 554.9 565.4 777.9 1044.0
#> Pretrain 508.5 508.5 508.5 539.0 443.8 554.0 550.5 455.1
#> Individual 532.8 532.8 532.8 584.1 443.2 567.2 550.5 518.9
#>
#>
#> Support size:
#>
#> Overall 64
#> Pretrain 97 (21 common + 76 individual)
#> Individual 109
# Specify a single value of alpha and use lambda.1se.
pred = predict(cvfit, xtest, groupstest, ytest=ytest, s="lambda.1se",
alphatype = "varying", alpha = .3)
pred
#>
#> Call:
#> predict.cv.ptLasso(object = cvfit, xtest = xtest, groupstest = groupstest,
#> ytest = ytest, alpha = 0.3, alphatype = "varying", s = "lambda.1se")
#>
#>
#>
#> alpha = 0.3
#>
#> Performance (Mean squared error):
#>
#> allGroups mean group_1 group_2 group_3 group_4 group_5 r^2
#> Overall 789.1 789.1 723.7 490.3 609.6 897.3 1224.8 0.5166
#> Pretrain 530.6 530.6 531.1 463.8 572.2 524.4 561.6 0.6750
#> Individual 564.4 564.4 536.7 468.7 643.8 572.0 600.8 0.6543
#>
#> Support size:
#>
#> Overall 21
#> Pretrain 56 (21 common + 35 individual)
#> Individual 80
# Specify a vector of choices for alpha:
pred = predict(cvfit, xtest, groupstest, ytest=ytest, s="lambda.min",
alphatype = "varying", alpha = c(.1, .2, .3, .4, .5))
pred
#>
#> Call:
#> predict.cv.ptLasso(object = cvfit, xtest = xtest, groupstest = groupstest,
#> ytest = ytest, alpha = c(0.1, 0.2, 0.3, 0.4, 0.5), alphatype = "varying",
#> s = "lambda.min")
#>
#>
#> alpha:
#> [1] 0.1 0.2 0.3 0.4 0.5
#>
#>
#> Performance (Mean squared error):
#> overall mean wtdMean group_1 group_2 group_3 group_4 group_5
#> Overall 755.7 755.7 755.7 836.0 554.9 565.4 777.9 1044.0
#> Pretrain 524.5 524.5 524.5 649.9 464.0 552.1 494.3 462.4
#> Individual 532.8 532.8 532.8 584.1 443.2 567.2 550.5 518.9
#>
#>
#> Support size:
#>
#> Overall 64
#> Pretrain 91 (21 common + 70 individual)
#> Individual 109