# Software Help 11

Software Help 11

The next two pages cover the Minitab and R commands for the procedures in this lesson.

Below is a zip file that contains all the data sets used in this lesson:

STAT501_Lesson11.zip

• hospital_infct_03.txt
• influence1.txt
• influence2.txt
• influence3.txt
• influence4.txt

# Minitab Help 11: Influential Points

Minitab Help 11: Influential Points

## Influence 2 (outlier, low leverage, not influential)

• Create a basic scatterplot.
• Perform a linear regression analysis for all the data (click "Storage" in the regression dialog and select "Fits" to save the fitted values).
• Select Data > Subset Worksheet to create a worksheet that excludes observation #21 and Perform a linear regression analysis.
• Return to the original worksheet and select Calc > Calculator to calculate fitted values based on the fitted equation for the subsetted worksheet, e.g., FITX = 1.73+5.117*x.
• Return to the scatterplot and select Editor > Calc > Calculated Line with y=FITS and x=x to add a regression line to the scatterplot.
• Select Editor > Calc > Calculated Line with y=FITX and x=x to add a regression line based on the fitted equation for the subsetted worksheet.
• Click "Storage" in the regression dialog to calculate leverages, standardized residuals, studentized (deleted) residuals, DFFITS, and Cook's distances.

## Influence 3 (high leverage, not an outlier, not influential)

• Create a basic scatterplot.
• Perform a linear regression analysis for all the data (click "Storage" in the regression dialog and select "Fits" to save the fitted values).
• Select Data > Subset Worksheet to create a worksheet that excludes observation #21 and Perform a linear regression analysis.
• Return to the original worksheet and select Calc > Calculator to calculate fitted values based on the fitted equation for the subsetted worksheet, e.g., FITX = 1.73+5.117*x.
• Return to the scatterplot and select Editor > Calc > Calculated Line with y=FITS and x=x to add a regression line to the scatterplot.
• Select Editor > Calc > Calculated Line with y=FITX and x=x to add a regression line based on the fitted equation for the subsetted worksheet.
• Click "Storage" in the regression dialog to calculate leverages, DFFITS, and Cook's distances.

## Influence 4 (outlier, high leverage, influential)

• Create a basic scatterplot.
• Perform a linear regression analysis for all the data (click "Storage" in the regression dialog and select "Fits" to save the fitted values).
• Select Data > Subset Worksheet to create a worksheet that excludes observation #21 and Perform a linear regression analysis.
• Return to the original worksheet and select Calc > Calculator to calculate fitted values based on the fitted equation for the subsetted worksheet, e.g., FITX = 1.73+5.117*x.
• Return to the scatterplot and select Editor > Calc > Calculated Line with y=FITS and x=x to add a regression line to the scatterplot.
• Select Editor > Calc > Calculated Line with y=FITX and x=x to add a regression line based on the fitted equation for the subsetted worksheet.
• Click "Storage" in the regression dialog to calculate leverages, DFFITS, and Cook's distances.

# R Help 11: Influential Points

R Help 11: Influential Points

## Influence 1 (no influential points)

• Create a scatterplot of the data.
influence1 <- read.table("~/path-to-data/influence1.txt", header=T)
attach(influence1)

plot(x, y)

detach(influence1)

## Influence 2 (outlier, low leverage, not influential)

• Create a scatterplot of the data.
• Fit a simple linear regression model to all the data.
• Fit a simple linear regression model to the data excluding observation #21.
• Add regression lines to the scatterplot, one for each model.
• Calculate leverages, standardized residuals, studentized residuals, DFFITS, and Cook's distances.
influence2 <- read.table("~/path-to-data/influence2.txt", header=T)
attach(influence2)

plot(x, y)

model.1 <- lm(y ~ x)
summary(model.1)
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   2.9576     2.0091   1.472    0.157
# x             5.0373     0.3633  13.865 2.18e-11 ***
# ---
# Residual standard error: 4.711 on 19 degrees of freedom
# Multiple R-squared:  0.9101,  Adjusted R-squared:  0.9053
# F-statistic: 192.2 on 1 and 19 DF,  p-value: 2.179e-11

model.2 <- lm(y ~ x, subset=1:20) # exclude obs #21
summary(model.2)
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   1.7322     1.1205   1.546     0.14
# x             5.1169     0.2003  25.551 1.35e-15 ***
# ---
# Residual standard error: 2.592 on 18 degrees of freedom
# Multiple R-squared:  0.9732,  Adjusted R-squared:  0.9717
# F-statistic: 652.8 on 1 and 18 DF,  p-value: 1.353e-15

plot(x=x, y=y, col=ifelse(Row<=20, "blue", "red"),
panel.last = c(lines(sort(x), fitted(model.1)[order(x)], col="red"),
lines(sort(x[-21]), fitted(model.2)[order(x[-21])],
col="red", lty=2)))
legend("topleft", col="red", lty=c(1,2),
inset=0.02, legend=c("Red point included", "Red point excluded"))

lev <- hatvalues(model.1)
round(lev, 6)
#        1        2        3        4        5        6        7        8        9
# 0.176297 0.157454 0.127015 0.119313 0.086145 0.077744 0.065028 0.061276 0.048147
#       10       11       12       13       14       15       16       17       18
# 0.049628 0.049313 0.051829 0.055760 0.069310 0.072580 0.109616 0.127489 0.141136
#       19       20       21
# 0.140453 0.163492 0.050974
sum(lev) # 2

sta <- rstandard(model.1)
round(sta, 6)
#         1         2         3         4         5         6         7         8
# -0.826351 -0.249154 -0.435445  0.998187 -0.581904 -0.574462  0.413791 -0.371226
#         9        10        11        12        13        14        15        16
#  0.139767 -0.262514 -0.713173 -0.095897  0.252734 -1.229353 -0.683161  0.292644
#        17        18        19        20        21
#  0.262144  0.731458 -0.055615 -0.776800  3.681098

stu <- rstudent(model.1)
round(stu, 6)
#         1         2         3         4         5         6         7         8
# -0.819167 -0.242905 -0.425962  0.998087 -0.571499 -0.564060  0.404582 -0.362643
#         9        10        11        12        13        14        15        16
#  0.136110 -0.255977 -0.703633 -0.093362  0.246408 -1.247195 -0.673261  0.285483
#        17        18        19        20        21
#  0.255615  0.722190 -0.054136 -0.768382  6.690129

dffit <- dffits(model.1)
round(dffit, 6)
#         1         2         3         4         5         6         7         8
# -0.378974 -0.105007 -0.162478  0.367368 -0.175466 -0.163769  0.106698 -0.092652
#         9        10        11        12        13        14        15        16
#  0.030612 -0.058495 -0.160254 -0.021828  0.059879 -0.340354 -0.188345  0.100168
#        17        18        19        20        21
#  0.097710  0.292757 -0.021884 -0.339696  1.550500

cook <- cooks.distance(model.1)
round(cook, 6)
#        1        2        3        4        5        6        7        8
# 0.073076 0.005800 0.013794 0.067493 0.015960 0.013909 0.005954 0.004498
#        9       10       11       12       13       14       15       16
# 0.000494 0.001799 0.013191 0.000251 0.001886 0.056275 0.018262 0.005272
#       17       18       19       20       21
# 0.005021 0.043960 0.000253 0.058968 0.363914

detach(influence2)

## Influence 3 (high leverage, not an outlier, not influential)

• Create a scatterplot of the data.
• Fit a simple linear regression model to all the data.
• Fit a simple linear regression model to the data excluding observation #21.
• Add regression lines to the scatterplot, one for each model.
• Calculate leverages, DFFITS, and Cook's distances.
influence3 <- read.table("~/path-to-data/influence3.txt", header=T)
attach(influence3)

plot(x, y)

model.1 <- lm(y ~ x)
summary(model.1)
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   2.4679     1.0757   2.294   0.0333 *
# x             4.9272     0.1719  28.661   <2e-16 ***
# ---
# Residual standard error: 2.709 on 19 degrees of freedom
# Multiple R-squared:  0.9774,  Adjusted R-squared:  0.9762
# F-statistic: 821.4 on 1 and 19 DF,  p-value: < 2.2e-16

model.2 <- lm(y ~ x, subset=1:20) # exclude obs #21
summary(model.2)
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   1.7322     1.1205   1.546     0.14
# x             5.1169     0.2003  25.551 1.35e-15 ***
# ---
# Residual standard error: 2.592 on 18 degrees of freedom
# Multiple R-squared:  0.9732,  Adjusted R-squared:  0.9717
# F-statistic: 652.8 on 1 and 18 DF,  p-value: 1.353e-15

plot(x=x, y=y, col=ifelse(Row<=20, "blue", "red"),
panel.last = c(lines(sort(x), fitted(model.1)[order(x)], col="red"),
lines(sort(x[-21]), fitted(model.2)[order(x[-21])],
col="red", lty=2)))
legend("topleft", col="red", lty=c(1,2),
inset=0.02, legend=c("Red point included", "Red point excluded"))

lev <- hatvalues(model.1)
round(lev, 6)
#        1        2        3        4        5        6        7        8        9
# 0.153481 0.139367 0.116292 0.110382 0.084374 0.077557 0.066879 0.063589 0.050033
#       10       11       12       13       14       15       16       17       18
# 0.052121 0.047632 0.048156 0.049557 0.055893 0.057574 0.078121 0.088549 0.096634
#       19       20       21
# 0.096227 0.110048 0.357535
sum(lev) # 2

dffit <- dffits(model.1)
round(dffit, 6)
#         1         2         3         4         5         6         7         8
# -0.525036 -0.083882 -0.182326  0.758981 -0.218230 -0.201548  0.277728 -0.082294
#         9        10        11        12        13        14        15        16
#  0.138643 -0.022210 -0.184873  0.055235  0.197411 -0.424484 -0.172490  0.299173
#        17        18        19        20        21
#  0.309606  0.630493  0.149474 -0.250945 -1.238416

cook <- cooks.distance(model.1)
round(cook, 6)
#        1        2        3        4        5        6        7        8
# 0.134157 0.003705 0.017302 0.241690 0.024433 0.020879 0.038412 0.003555
#        9       10       11       12       13       14       15       16
# 0.009943 0.000260 0.017379 0.001605 0.019748 0.081344 0.015289 0.044620
#       17       18       19       20       21
# 0.047961 0.173901 0.011656 0.032322 0.701965

detach(influence3)

## Influence 4 (outlier, high leverage, influential)

• Create a scatterplot of the data.
• Fit a simple linear regression model to all the data.
• Fit a simple linear regression model to the data excluding observation #21.
• Add regression lines to the scatterplot, one for each model.
• Calculate leverages, DFFITS, and Cook's distances.
influence4 <- read.table("~/path-to-data/influence4.txt", header=T)
attach(influence4)

plot(x, y)

model.1 <- lm(y ~ x)
summary(model.1)
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   8.5046     4.2224   2.014 0.058374 .
# x             3.3198     0.6862   4.838 0.000114 ***
# ---
# Residual standard error: 10.45 on 19 degrees of freedom
# Multiple R-squared:  0.5519,  Adjusted R-squared:  0.5284
# F-statistic: 23.41 on 1 and 19 DF,  p-value: 0.0001143

model.2 <- lm(y ~ x, subset=1:20) # exclude obs #21
summary(model.2)
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   1.7322     1.1205   1.546     0.14
# x             5.1169     0.2003  25.551 1.35e-15 ***
# ---
# Residual standard error: 2.592 on 18 degrees of freedom
# Multiple R-squared:  0.9732,  Adjusted R-squared:  0.9717
# F-statistic: 652.8 on 1 and 18 DF,  p-value: 1.353e-15

plot(x=x, y=y, col=ifelse(Row<=20, "blue", "red"),
panel.last = c(lines(sort(x), fitted(model.1)[order(x)], col="red"),
lines(sort(x[-21]), fitted(model.2)[order(x[-21])],
col="red", lty=2)))
legend("topleft", col="red", lty=c(1,2),
inset=0.02, legend=c("Red point included", "Red point excluded"))

lev <- hatvalues(model.1)
round(lev, 6)
#        1        2        3        4        5        6        7        8        9
# 0.158964 0.143985 0.119522 0.113263 0.085774 0.078589 0.067369 0.063924 0.049897
#       10       11       12       13       14       15       16       17       18
# 0.052019 0.047667 0.048354 0.049990 0.057084 0.058943 0.081446 0.092800 0.101587
#       19       20       21
# 0.101146 0.116146 0.311532
sum(lev) # 2

dffit <- dffits(model.1)
round(dffit, 6)
#         1          2          3          4          5          6          7
# -0.402761  -0.243756  -0.205848   0.037612  -0.131355  -0.109593   0.040473
#         8          9         10         11         12         13         14
# -0.042401   0.060224   0.009181   0.005430   0.078165   0.127828   0.007230
#        15         16         17         18         19         20         21
#  0.073067   0.280501   0.323599   0.436114   0.308869   0.249206 -11.467011

cook <- cooks.distance(model.1)
round(cook, 6)
#        1        2        3        4        5        6        7        8
# 0.081718 0.030755 0.021983 0.000746 0.009014 0.006290 0.000863 0.000947
#        9       10       11       12       13       14       15       16
# 0.001907 0.000044 0.000016 0.003203 0.008478 0.000028 0.002804 0.039575
#       17       18       19       20       21
# 0.052293 0.091802 0.048085 0.031938 4.048013

detach(influence4)

## Foot length and height (outlier, high leverage, influential)

• Create a scatterplot of the data.
• Fit a simple linear regression model to all the data.
• Fit a simple linear regression model to the data excluding observation #28.
• Calculate DFFITS and Cook's distance for obs #28.
heightfoot <- read.table("~/path-to-data/height_foot.txt", header=T)
attach(heightfoot)

plot(height, foot)

model.1 <- lm(foot ~ height)
summary(model.1)
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept) 10.93577    4.43778   2.464 0.019477 *
# height       0.23344    0.06151   3.795 0.000643 ***
# ---
# Residual standard error: 1.286 on 31 degrees of freedom
# Multiple R-squared:  0.3173,  Adjusted R-squared:  0.2952
# F-statistic: 14.41 on 1 and 31 DF,  p-value: 0.0006428

which(height>80) # 28
model.2 <- lm(foot ~ height, subset=(1:33)[-28]) # exclude obs #28
summary(model.2)
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)  0.25313    4.33232   0.058    0.954
# height       0.38400    0.06038   6.360 5.12e-07 ***
# ---
# Residual standard error: 1.028 on 30 degrees of freedom
# Multiple R-squared:  0.5741,  Adjusted R-squared:  0.5599
# F-statistic: 40.45 on 1 and 30 DF,  p-value: 5.124e-07

dffit <- dffits(model.1)
dffit[28] # -3.200223

cook <- cooks.distance(model.1)
cook[28] # 3.274466

detach(heightfoot)

## Hospital infection risk (two outliers, high leverages)

• Load the infection risk data.
• Fit a simple linear regression model to all the data.
• Create a scatterplot of the data and add the regression line.
• Display influence measures for influential points, including DFFITS, Cook's distances, and leverages (hat).
infectionrisk <- read.table("~/path-to-data/infectionrisk.txt", header=T)
attach(infectionrisk)

model <- lm(InfctRsk ~ Stay)
summary(model)

plot(x=Stay, y=InfctRsk,
panel.last = lines(sort(Stay), fitted(model)[order(Stay)]))

summary(influence.measures(model))
#     dfb.1_ dfb.Stay dffit   cov.r   cook.d hat
# 2   -0.13   0.09    -0.23    0.94_*  0.02   0.01
# 34  -0.04   0.05     0.05    1.07_*  0.00   0.05
# 40  -0.20   0.17    -0.27    0.94_*  0.04   0.01
# 47   0.85  -0.90    -0.92_*  1.30_*  0.42   0.25_*
# 53  -0.16   0.20     0.30    0.94_*  0.04   0.02
# 93  -0.14   0.09    -0.25    0.92_*  0.03   0.01
# 104 -0.11   0.12     0.14    1.07_*  0.01   0.05_*
# 112  0.64  -0.68    -0.70_*  1.19_*  0.24   0.18_*

detach(infectionrisk)

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