我目前正在使用2个单独的CSV数据集。我已经使用了名为PRICetable4.1的第一个数据集来可视化x值（GBA）和y值（PRICE）之间的关系。我在下面附上了这张图的图片。

我现在要做的是使用第一个CSV数据集中的三次回归模型，根据给定的x值（GBA）预测第二个CSV数据集中的y值（PREDICTED_PRICE）。是否有让我建立连接的功能？

train_X <-  PRICetable4.1$GBA
train_y <- PRICetable4.1$PRICE
test_X <- PRICetable4.1$GBA
test_y <- PRICetable4.1$PRICE

X <- train_X
view(X)
y <- train_y
View(y)

poly_order <- 3
model <- lm (y~poly(X,poly_order))
print(model)

#MSE
test_yhat <- predict(model,data.frame (X = test_X))
MSE <- mean((test_y-test_yhat )^2)
print(MSE)

#R squared
test_ymean <- mean(test_y)
test_yhatmean <- mean( test_yhat)
R_squared <- (sum((test_yhat-test_yhatmean)*(test_y-test_ymean)))^2/(sum((test_yhat-test_yhatmean)^2)*sum((test_y-test_ymean)^2))
print(R_squared)

error2 <- data.frame(MSE=c(MSE),R_squared=c(R_squared))
View(error2)

#Visualization of the model

X_new = X
View(X_new)
y_new <- predict(model,data.frame (X = X_new))
View(y_new)
PRICEmodel <- ggplot(PRICetable4.1,aes(x=GBA,y=PRICE))+geom_point(size=2)
PRICEmodel+geom_line(aes(x=X_new,y=y_new),color="Red") + labs(x="Gross building area (ft^2)",y="Price",title="Price Regression Model")

这是第一个名为PRICetable4.1的数据集（前20行）的描述

structure(list(ID = c(1L,2L,3L,4L,6L,7L,8L,9L,10L,11L,12L,13L,14L,15L,16L,17L,18L,19L,20L,21L),GBA = c(1324L,2120L,1216L,1804L,1836L,1228L,1312L,1262L,1461L,1120L,1037L,832L,1500L,920L,1565L,1134L,1184L,1420L,2082L,1422L),PRICE = c(1375000L,1467000L,549410L,1180000L,828000L,742000L,829000L,710000L,775000L,380000L,600000L,189000L,200000L,265000L,560000L,300000L,940000L,1050000L,979000L)),row.names = c(NA,20L),class = "data.frame")

这是第二个CSV数据集的名称为Test（前20行）

structure(list(ID = c(1L,class = "data.frame")
> dput(Test[1:20,])
structure(list(ID = 1:20,BATHRM = c(2L,1L,2L),HF_BATHRM = c(1L,0L,0L),HEAT = c("Forced Air","Forced Air","Warm Cool","Hot Water Rad","Ht Pump","Forced Air"),AC = c("Y","Y","N","Y"),NUM_UNITS = c(2L,1L),ROOMS = c(9L,5L,7L),BEDRM = c(3L,3L),AYB = c(1870L,1890L,1911L,1920L,1993L,1947L,1895L,1910L,1950L,1951L,1928L,1941L,2018L,1939L,1980L,1908L),YR_RMDL = c(1980L,1963L,NA,2001L,1987L,2017L,1992L,2013L,2005L,2004L,1984L),EYB = c(1967L,1982L,1957L,1972L,2003L,1958L,1964L,1954L,1960L,1961L,1991L,1975L,1960L
),STORIES = c(2,2,1.75,1,2),5036L,2082L),BLDG_NUM = c(1L,STYLE = c("2 Story","2 Story","1 Story","2 Story"),STRUCT = c("Row Inside","Row Inside","Single","Semi-Detached","Multi","Row Inside"),LANDAREA = c(1575L,1800L,1280L,5000L,10252L,3000L,1641L,1358L,6300L,1818L,3500L,5098L,1899L,5009L,1152L,2910L,1762L,1400L),ASSESSMENT_NBHD = c("Old City 2","Capitol Hill","Old City 1","Palisades","Chevy Chase","Eckington","Ledroit Park","Riggs Park","Woodridge","Lily Ponds","Fort Dupont Park","Hillcrest","Congress Heights","Capitol Hill"
),PREDICTED_PRICE = c(NA,NA)),20L
),class = "data.frame")

因此，我需要使用从第一个数据集创建的回归模型预测并填写第二个CSV数据集的“ PREDICTED_PRICE”列中的值