Make predictions from a ggmix_fit object

Similar to other predict methods, this functions predicts fitted values, coefficients and more from a fitted ggmix_fit object.

# S3 method for ggmix_fit
predict(
  object,
  newx,
  s = NULL,
  type = c("link", "response", "coefficients", "all", "nonzero", "individual"),
  covariance,
  ...
)

# S3 method for ggmix_fit
coef(object, s = NULL, type, ...)

Arguments

object	Fitted ggmix_fit model object from the ggmix function
newx	matrix of values for x at which predictions are to be made. Do not include the intercept. Must be a matrix. This argument is not used for type = c("coefficients","nonzero","all"). This matrix must have the same number of columns originally supplied to the ggmix fitting function.
s	Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
type	Type of prediction required. Type "link" gives the fitted values \(X \beta\). Type "response" is equivalent to type "link". Type "coefficients" computes the coefficients at the requested values for s and returns the regression coefficients only, including the intercept. Type "all" returns both the regression coefficients and variance components at the requested value of s. Type "nonzero" returns a 1 column matrix of the the nonzero fixed effects, as well as variance components for each value of s. If more than one s is provided, then "nonzero" will return a list of 1 column matrices. Default: "link"
covariance	covariance between test and training individuals. if there are q testing individuals and N-q training individuals, then this covariance matrix is q x (N-q)
...	additional arguments to pass to predict function

Value

The object returned depends on type.

Details

s is the new vector at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the predict function will use linear interpolation to make predictions. The new values are interpolated using a fraction of predicted values from both left and right lambda indices. coef(...) is equivalent to predict(ggmix_fit, type="coefficients",...). To get individual level predictions at each value of lambda, you must provide the lambda sequence to the s argument. You can pass either a ggmix_fit or ggmix_gic object. See examples for more details.

Examples

data("admixed")
fitlmm <- ggmix(x = admixed$xtrain, y = admixed$ytrain,
                kinship = admixed$kin_train,
                estimation = "full")
bicGGMIX <- gic(fitlmm,
                an = log(length(admixed$ytrain)))
plot(bicGGMIX)
coef(bicGGMIX, s = "lambda.min")
#> 51 x 1 sparse Matrix of class "dgCMatrix"
#>                        1
#> (Intercept)  0.005271703
#> X23          .          
#> X36          .          
#> X38          .          
#> X40          .          
#> X53          .          
#> X74          .          
#> X83          .          
#> X114         .          
#> X139         .          
#> X143         .          
#> X168        -0.086106135
#> X176         .          
#> X244         .          
#> X246         .          
#> X249         .          
#> X266         .          
#> X271         .          
#> X273         .          
#> X282         .          
#> X286         .          
#> X300         .          
#> X302        -0.194013522
#> X312         .          
#> X315         .          
#> X330         .          
#> X336         .          
#> X344         .          
#> X348         .          
#> X352         .          
#> X375         .          
#> X383         .          
#> X403         .          
#> X404         .          
#> X420         .          
#> X422         .          
#> X431         .          
#> X435         .          
#> X441         .          
#> X447         .          
#> X468         .          
#> X485         .          
#> X503        -0.134716832
#> X512         .          
#> X515         .          
#> X516         .          
#> X407         .          
#> X507         .          
#> X524         1.476145530
#> X538        -0.826482104
#> X243         .          
yhat_test <- predict(bicGGMIX, s="lambda.min",
                     newx = admixed$xtest, type = "individual",
                     covariance = admixed$kin_test_train)
cor(yhat_test, admixed$ytest)
#> [1] 0.9135335
yhat_test_population <- predict(bicGGMIX, s="lambda.min",
                                newx = admixed$xtest,
                                type = "response")