> summary(INT)
      Lone          Soc.Anx            GIU             LIU        
 Min.   : 6.00   Min.   : 3.000   Min.   : 7.00   Min.   : 0.000  
 1st Qu.:11.00   1st Qu.: 6.500   1st Qu.:14.00   1st Qu.: 1.000  
 Median :14.00   Median : 9.000   Median :17.00   Median : 4.000  
 Mean   :15.09   Mean   : 8.405   Mean   :17.16   Mean   : 5.748  
 3rd Qu.:18.00   3rd Qu.:10.000   3rd Qu.:20.00   3rd Qu.: 9.000  
 Max.   :31.00   Max.   :17.000   Max.   :27.00   Max.   :26.000  
      SIU       
 Min.   :20.00  
 1st Qu.:31.50  
 Median :38.00  
 Mean   :38.72  
 3rd Qu.:45.50  
 Max.   :66.00  
> dim(INT)
[1] 111   5
> cor(INT)
             Lone      Soc.Anx         GIU        LIU          SIU
Lone    1.0000000  0.295580990  0.31150040 0.22702970  0.200060929
Soc.Anx 0.2955810  1.000000000  0.11699527 0.11573076 -0.004006136
GIU     0.3115004  0.116995272  1.00000000 0.07370225 -0.015521531
LIU     0.2270297  0.115730762  0.07370225 1.00000000  0.558813056
SIU     0.2000609 -0.004006136 -0.01552153 0.55881306  1.000000000
> r.crit(df=109)
      df    alpha   1-tail   2-tail 
109.0000   0.0500   0.1569   0.1865 
> 
> lm.INT.0=lm(Lone~1,data=INT)
> lm.INT.1=lm(Lone~Soc.Anx,data=INT)
> lm.INT.2=lm(Lone~Soc.Anx+GIU+LIU+SIU,data=INT)
> 
> anova(lm.INT.0)   # get SS.total
Analysis of Variance Table

Response: Lone
           Df Sum Sq Mean Sq F value Pr(>F)
Residuals 110 3151.1  28.646               
> anova(lm.INT.0,lm.INT.1)   # evaluate Soc.Anx
Analysis of Variance Table

Model 1: Lone ~ 1
Model 2: Lone ~ Soc.Anx
  Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
1    110 3151.1                                
2    109 2875.8  1    275.31 10.435 0.001635 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 275.31/3151.1   # R Square and R Square Change for Soc.Anx
[1] 0.08736949
> anova(lm.INT.1,lm.INT.2)   # evaluate the internet usage block
Analysis of Variance Table

Model 1: Lone ~ Soc.Anx
Model 2: Lone ~ Soc.Anx + GIU + LIU + SIU
  Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
1    109 2875.8                                
2    106 2480.3  3    395.47 5.6336 0.001273 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 395.47/3151.1   # R Square Change for the internet usage block
[1] 0.1255022
> 0.0874+0.1255   # R Square for the internet usage block
[1] 0.2129
> 
> 
> 
> summary(LONE)   # I called my data frame LONE instead of loneliness
   embarrass         emotiso        socialiso 
 Min.   : 32.00   Min.   : 0.00   Min.   : 0  
 1st Qu.: 52.75   1st Qu.: 5.75   1st Qu.: 2  
 Median : 65.00   Median :11.00   Median : 6  
 Mean   : 64.90   Mean   :12.07   Mean   : 7  
 3rd Qu.: 76.25   3rd Qu.:16.00   3rd Qu.:10  
 Max.   :111.00   Max.   :40.00   Max.   :31  
> dim(LONE)
[1] 112   3
> cor(LONE)
          embarrass   emotiso socialiso
embarrass 1.0000000 0.2875657 0.4173950
emotiso   0.2875657 1.0000000 0.6397209
socialiso 0.4173950 0.6397209 1.0000000
> r.crit(110)
      df    alpha   1-tail   2-tail 
110.0000   0.0500   0.1562   0.1857 
> 
> with(LONE, mediate(x=embarrass, m=socialiso, y=emotiso))
Test of Simple Mediation Effect
            X on M   M on Y    total   direct indirect
statistic 0.171136 0.784504 0.146975 0.012719 0.134257
std.err   0.035525 0.100945 0.046673 0.041388 0.032789
p.value   0.000005 0.000000 0.002109 0.759201 0.000042
Sobel.z       n 
   4.09  112.00 
Bootstrap Resampling Confidence Intervals for Indirect Effect
***not requested***
> 
> 0.134257/0.146975*100   # percent mediation
[1] 91.34683
> 
> 
> 
> 259.64/1256.97   # R Square and R Square Change for hostil
[1] 0.2065602
> 546.13/1256.97   # R Square Change for physag+verbag
[1] 0.4344813
>