Differences

This shows you the differences between two versions of the page.

--- r:multiple_regression [2019/11/08 09:28] – hkimscil
+++ r:multiple_regression [2020/08/31 12:31] – hkimscil
@@ Line 253: / Line 253: @@
 coefficients(lm.stock)
 </code>
+<code>> names(lm.stock)
+ [1] "coefficients"  "residuals"     "effects"       "rank"          "fitted.values"
+ [6] "assign"        "qr"            "df.residual"   "xlevels"       "call"
+[11] "terms"         "model"      </code>
 <code>> lm.stock <- lm(Stock_Index_Price~Interest_Rate+Unemployment_Rate, data=dat.stock)
@@ Line 512: / Line 518: @@
 </code>
+Alternatively,
+<code>
+> install.packages("lmtest")
+> library(lmtest)
+> lrtest(lm.stock.int, lm.stock)
+Likelihood ratio test
+Model 1: Stock_Index_Price ~ Interest_Rate
+Model 2: Stock_Index_Price ~ Interest_Rate + Unemployment_Rate
+  #Df  LogLik Df  Chisq Pr(>Chisq)
+   3 -136.94
+   4 -134.61  1 4.6576    0.03092 *
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+> </code>
+====== e.g. 5 ======
+<code>
+#packages we will need to conduct to create and graph our data
+library(MASS) #create data
+library(car) #graph data
+py1 =.6 #Cor between X1 (Practice Time) and Memory Errors
+py2 =.4 #Cor between X2 (Performance Anxiety) and Memory Errors
+p12= .3 #Cor between X1 (Practice Time) and X2 (Performance Anxiety)
+Means.X1X2Y<- c(10,10,10) #set the means of X and Y variables
+CovMatrix.X1X2Y <- matrix(c(1,p12,py1,
+                            p12,1,py2,
+                            py1,py2,1),3,3) # creates the covariate matrix
+#build the correlated variables. Note: empirical=TRUE means make the correlation EXACTLY r.
+# if we say empirical=FALSE, the correlation would be normally distributed around r
+set.seed(42)
+CorrDataT<-mvrnorm(n=100, mu=Means.X1X2Y,Sigma=CovMatrix.X1X2Y, empirical=TRUE)
+#Convert them to a "Data.Frame" & add our labels to the vectors we created
+CorrDataT<-as.data.frame(CorrDataT)
+colnames(CorrDataT) <- c("Practice","Anxiety","Memory")
+#make the scatter plots
+scatterplot(Memory~Practice,CorrDataT, smoother=FALSE)
+scatterplot(Memory~Anxiety,CorrDataT, smoother=FALSE)
+scatterplot(Anxiety~Practice,CorrDataT, smoother=FALSE)
+# Pearson Correlations
+ry1<-cor(CorrDataT$Memory,CorrDataT$Practice)
+ry2<-cor(CorrDataT$Memory,CorrDataT$Anxiety)
+r12<-cor(CorrDataT$Anxiety,CorrDataT$Practice)
+</code>