Example 8-1 Using the ore.predict Function on a Linear Regression Model

This example builds a linear regression model, irisModel, using the lm function on the iris data.frame. It pushes the data set to the database as the temporary table IRIS and the corresponding ore.frame proxy, IRIS. The example scores the model by invoking ore.predict on it and then combines the prediction with IRIS ore.frame object. Finally, it displays the first six rows of the resulting object.

IRISModel <- lm(Sepal.Length ~ ., data = iris)
IRIS <- ore.push(iris)
IRIS_pred <- ore.predict(IRISModel, IRIS, se.fit = TRUE, 
                            interval = "prediction")
IRIS <- cbind(IRIS, IRIS_pred)
head(IRIS)

Listing for This Example

R> IRISModel <- lm(Sepal.Length ~ ., data = iris)
R> IRIS <- ore.push(iris)
R> IRIS_pred <- ore.predict(IRISModel, IRIS, se.fit = TRUE, 
+                              interval = "prediction")
R> IRIS <- cbind(IRIS, IRIS_pred)
R> head(IRIS)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species     PRED    SE.PRED
1          5.1         3.5          1.4         0.2  setosa 5.004788 0.04479188
2          4.9         3.0          1.4         0.2  setosa 4.756844 0.05514933
3          4.7         3.2          1.3         0.2  setosa 4.773097 0.04690495
4          4.6         3.1          1.5         0.2  setosa 4.889357 0.05135928
5          5.0         3.6          1.4         0.2  setosa 5.054377 0.04736842
6          5.4         3.9          1.7         0.4  setosa 5.388886 0.05592364
  LOWER.PRED UPPER.PRED
1   4.391895   5.617681
2   4.140660   5.373027
3   4.159587   5.386607
4   4.274454   5.504259
5   4.440727   5.668026
6   4.772430   6.005342

R> head(IRIS)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species     PRED    SE.PRED LOWER.PRED UPPER.PRED
1          5.1         3.5          1.4         0.2  setosa 5.004788 0.04479188   4.391895   5.617681
2          4.9         3.0          1.4         0.2  setosa 4.756844 0.05514933   4.140660   5.373027
3          4.7         3.2          1.3         0.2  setosa 4.773097 0.04690495   4.159587   5.386607
4          4.6         3.1          1.5         0.2  setosa 4.889357 0.05135928   4.274454   5.504259
5          5.0         3.6          1.4         0.2  setosa 5.054377 0.04736842   4.440727   5.668026
6          5.4         3.9          1.7         0.4  setosa 5.388886 0.05592364   4.772430   6.005342

Example 8-2 Using the ore.predict Function on a Generalized Linear Regression Model

This example builds a generalized linear model using the infert data set and then calls the ore.predict function on the model.

infertModel <-
  glm(case ~ age + parity + education + spontaneous + induced,
  data = infert, family = binomial())
INFERT <- ore.push(infert)
INFERTpred <- ore.predict(infertModel, INFERT, type = "response",
                          se.fit = TRUE)
INFERT <- cbind(INFERT, INFERTpred)
head(INFERT)

Listing for This Example

R> infertModel <-
+   glm(case ~ age + parity + education + spontaneous + induced,
+   data = infert, family = binomial())
R> INFERT <- ore.push(infert)
R> INFERTpred <- ore.predict(infertModel, INFERT, type = "response",
+                           se.fit = TRUE)
R> INFERT <- cbind(INFERT, INFERTpred)
R> head(INFERT)
  education age parity induced case spontaneous stratum pooled.stratum
1    0-5yrs  26      6       1    1           2       1              3
2    0-5yrs  42      1       1    1           0       2              1
3    0-5yrs  39      6       2    1           0       3              4
4    0-5yrs  34      4       2    1           0       4              2
5   6-11yrs  35      3       1    1           1       5             32
6   6-11yrs  36      4       2    1           1       6             36
       PRED    SE.PRED
1 0.5721916 0.20630954
2 0.7258539 0.17196245
3 0.1194459 0.08617462
4 0.3684102 0.17295285
5 0.5104285 0.06944005
6 0.6322269 0.10117919

Example 8-3 Using the ore.predict Function on an ore.model Model

This example pushes the iris data set to the database as the temporary table IRIS and the corresponding ore.frame proxy, IRIS. The example builds a linear regression model, IRISModel2, using the ore.lm function. It scores the model and adds a column to IRIS.

IRIS <- ore.push(iris)
IRISModel2 <- ore.lm(Sepal.Length ~ ., data = IRIS)
IRIS$PRED <- ore.predict(IRISModel2, IRIS)
head(IRIS, 3)

Listing for This Example

R> IRIS <- ore.push(iris)
R> IRISModel2 <- ore.odmGLM(Sepal.Length ~ ., data = IRIS)
R> IRIS$PRED <- ore.predict(IRISModel, IRIS)
R> head(IRIS, 3)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species     PRED
1          5.1         3.5          1.4         0.2  setosa 5.004788
2          4.9         3.0          1.4         0.2  setosa 4.756844
3          4.7         3.2          1.3         0.2  setosa 4.773097