linearregressionmodel.java

化学图形处理软件

     * Sets parameters required for building a linear model or using one for prediction.
     * <p/>
     * This function allows the caller to set the various parameters available
     * for the lm() and predict.lm() R routines. See the R help pages for the details of the available
     * parameters.
     *
     * @param key A String containing the name of the parameter as described in the
     *            R help pages
     * @param obj An Object containing the value of the parameter
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the type of the supplied value does not match the
     *          expected type
     */
    public void setParameters(String key, Object obj) throws QSARModelException {
        // since we know the possible values of key we should check the coresponding
        // objects and throw errors if required. Note that this checking can't really check
        // for values (such as number of variables in the X matrix to build the model and the
        // X matrix to make new predictions) - these should be checked in functions that will
        // use these parameters. The main checking done here is for the class of obj and
        // some cases where the value of obj is not dependent on what is set before it

        if (key.equals("y")) {
            if (!(obj instanceof Double[])) {
                throw new QSARModelException("The class of the 'y' object must be Double[]");
            }
        }
        if (key.equals("x")) {
            if (!(obj instanceof Double[][])) {
                throw new QSARModelException("The class of the 'x' object must be Double[][]");
            }
        }
        if (key.equals("weights")) {
            if (!(obj instanceof Double[])) {
                throw new QSARModelException("The class of the 'weights' object must be Double[]");
            }
        }
        if (key.equals("interval")) {
            if (!(obj instanceof String)) {
                throw new QSARModelException("The class of the 'interval' object must be String");
            }
            if (!(obj.equals("confidence") || obj.equals("prediction"))) {
                throw new QSARModelException("The type of interval must be: prediction or confidence");
            }
        }
        if (key.equals("newdata")) {
            if (!(obj instanceof Double[][])) {
                throw new QSARModelException("The class of the 'newdata' object must be Double[][]");
            }
        }
        this.params.put(key, obj);
    }


    /**
     * Uses a fitted model to predict the response for new observations.
     * <p/>
     * This function uses a previously fitted model to obtain predicted values
     * for a new set of observations. If the model has not been fitted prior to this
     * call an exception will be thrown. Use <code>setParameters</code>
     * to set the values of the independent variable for the new observations and the
     * interval type.
     *
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the model has not been built prior to a call
     *          to this method. Also if the number of independent variables specified for prediction
     *          is not the same as specified during model building
     */
    public void predict() throws QSARModelException {

        if (modelObject == null)
            throw new QSARModelException("Before calling predict() you must fit the model using build()");

        Double[][] newx = (Double[][]) params.get("newdata");
        if (newx[0].length != nvar) {
            throw new QSARModelException("Number of independent variables used for prediction must match those used for fitting");
        }

        String pn = loadParametersIntoRSession();
        REXP ret = rengine.eval("predictLM(\"" + getModelName() + "\", " + pn + ")");
        if (ret == null) throw new QSARModelException("Error occured in prediction");

        // remove the parameter list
        rengine.eval("rm(" + pn + ")");

        modelPredict = ret.asList();
    }

    /**
     * Get the R object obtained from <code>predict.lm()</code>.
     *
     * @return The result of the prediction. Contains a number of fields corresponding to
     *         predicted values, SE and other items depending on the parameters that we set.
     *         Note that the call to <code>predict.lm()</code> is performde with <code>se.fit = TRUE</code>
     */
    public RList getModelPredict() {
        return modelPredict;
    }

    /**
     * Returns an <code>RList</code> object summarizing the linear regression model.
     * <p/>
     * The return object can be queried via the <code>RList</code> methods to extract the
     * required components.
     *
     * @return A summary for the linear regression model
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the model has not been built prior to a call
     *          to this method
     */
    public RList summary() throws QSARModelException {
        if (modelObject == null)
            throw new QSARModelException("Before calling summary() you must fit the model using build()");

        REXP ret = rengine.eval("summary(" + getModelName() + ")");
        if (ret == null) {
            logger.debug("Error in summary()");
            throw new QSARModelException("Error in summary()");
        }
        return ret.asList();
    }


    /**
     * Loads an LinearRegressionModel object from disk in to the current session.
     *
     * @param fileName The disk file containing the model
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the model being loaded is not a linear regression model
     *          object  or the file does not exist
     */
    public void loadModel(String fileName) throws QSARModelException {
        File f = new File(fileName);
        if (!f.exists()) throw new QSARModelException(fileName + " does not exist");

        rengine.assign("tmpFileName", fileName);
        REXP ret = rengine.eval("loadModel(tmpFileName)");
        if (ret == null) throw new QSARModelException("Model could not be loaded");

        String name = ret.asList().at("name").asString();
        if (!isOfClass(name, "lm")) {
            removeObject(name);
            throw new QSARModelException("Loaded object was not of class \'lm\'");
        }

        modelObject = ret.asList().at("model").asList();
        setModelName(name);
        nvar = getCoefficients().length - 1; // since the intercept is also returned
    }

    /**
     * Loads an LinearRegressionModel object from a serialized string into the current session.
     *
     * @param serializedModel A String containing the serialized version of the model
     * @param modelName       A String indicating the name of the model in the R session
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the model being loaded is not a linear regression model
     *          object
     */
    public void loadModel(String serializedModel, String modelName) throws QSARModelException {
        rengine.assign("tmpSerializedModel", serializedModel);
        rengine.assign("tmpModelName", modelName);
        REXP ret = rengine.eval("unserializeModel(tmpSerializedModel, tmpModelName)");

        if (ret == null) throw new QSARModelException("Model could not be unserialized");

        String name = ret.asList().at("name").asString();
        if (!isOfClass(name, "lm")) {
            removeObject(name);
            throw new QSARModelException("Loaded object was not of class \'lm\'");
        }

        modelObject = ret.asList().at("model").asList();
        setModelName(name);
        nvar = getCoefficients().length - 1; // as the intercept is also returned
    }

// Autogenerated code: assumes that 'modelObject' is
// a RList object


    /**
     * Gets the <code>assign</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the assign field
     */
    public int[] getAssign() {
        return modelObject.at("assign").asIntArray();
    }

    /**
     * Gets the <code>coefficients</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the coefficients field
     */
    public double[] getCoefficients() {
        return modelObject.at("coefficients").asDoubleArray();
    }

    /**
     * Gets the <code>df.residual</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the df.residual field
     */
    public int getDfResidual() {
        return modelObject.at("df.residual").asInt();
    }

    /**
     * Gets the <code>effects</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the effects field
     */
    public double[] getEffects() {
        return modelObject.at("effects").asDoubleArray();
    }

    /**
     * Gets the <code>fitted.values</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the fitted.values field
     */
    public double[] getFittedValues() {
        return modelObject.at("fitted.values").asDoubleArray();
    }

    /**
     * Gets the <code>model</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the model field
     */
    public RList getModel() {
        return modelObject.at("model").asList();
    }

    /**
     * Gets the <code>qr</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the qr field
     */
    public RList getQr() {
        return modelObject.at("qr").asList();
    }

    /**
     * Gets the <code>rank</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the rank field
     */
    public int getRank() {
        return modelObject.at("rank").asInt();
    }

    /**
     * Gets the <code>residuals</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the residuals field
     */
    public double[] getResiduals() {
        return modelObject.at("residuals").asDoubleArray();
    }

    /**
     * Gets the <code>xlevels</code> field of an <code>'lm'</code> object.
     *
     * @return The value of the xlevels field
     */
    public RList getXlevels() {
        return modelObject.at("xlevels").asList();
    }


}