Topic 23: Diagnostics and Remedies. Outline Diagnostics –residual checks ANOVA remedial measures

Topic 23: Diagnostics and Remedies

Outline

• Diagnostics

– residual checks

• ANOVA remedial measures

Diagnostics Overview

• We will take the diagnostics and remedial measures that we learned for regression and adapt them to the ANOVA setting

• Many things are essentially the same

• Some things require modification

Residuals

• Predicted values are cell means, =

• Residuals are the differences between the observed values and the cell means Yij-

ijYi.Y

i.Y

Basic plots

• Plot the data vs the factor levels (the values of the explanatory variables)

• Plot the residuals vs the factor levels

• Construct a normal quantile plot and/or histogram of the residuals

KNNL Example

• KNNL p 777

• Compare 4 brands of rust inhibitor (X has r=4 levels)

• Response variable is a measure of the effectiveness of the inhibitor

• There are 10 units per brand (n=10)

Plots

• Data versus the factor

• Residuals versus the factor

• Normal quantile plot of the residuals

Plots vs the factor

symbol1 v=circle i=none;proc gplot data=a2; plot (eff resid)*abrand;run;

Data vs the factor

Means look different …common spread in Y’s

Residuals vs the factorOdd dist of points

QQ-plot

Due to odd (lack of and large)spread

Can try nonparametric analysis – last slides

General Summary

• Look for

–Outliers

–Variance that depends on level

–Non-normal errors

• Plot residuals vs time and other variables if available

Homogeneity tests

• Homogeneity of variance (homoscedasticity)

• H0: σ12 = σ2

2 = … = σr2

• H1: not all σi2 are equal

• Several significance tests are available

Homogeneity tests

• Text discusses Hartley, modified Levene

• SAS has several including Bartlett’s (essentially the likelihood ratio test) and several versions of Levene

Homogeneity tests

• There is a problem with assumptions–ANOVA is robust with respect to

moderate deviations from Normality–ANOVA results can be sensitive to

the homogeneity of variance assumption

• Some homogeneity tests are sensitive to the Normality assumption

Levene’s Test

• Do ANOVA on the squared residuals from the original ANOVA

• Modified Levene’s test uses absolute values of the residuals

• Modified Levene’s test is recommended• Another quick and dirty rule of thumb

2 2max( ) / min( ) 2i is s

KNNL Example

• KNNL p 785• Compare the strengths of 5 types

of solder flux (X has r=5 levels)• Response variable is the pull

strength, force in pounds required to break the joint

• There are 8 solder joints per flux (n=8)

Scatterplot

Levene’s Test

proc glm data=a1; class type; model strength=type; means type/ hovtest=levene(type=abs); run;

ANOVA Table

Source DFSum of

SquaresMean

Square F Value Pr > FModel 4 353.612085 88.4030213 41.93 <.0001Error 35 73.7988250 2.1085379Corrected Total

39 427.410910

Common variance estimated to be 2.11

Output

Levene's TestANOVA of Absolute Deviations Source DF F Value Pr > Ftype 4 3.07 0.0288Error 35

We reject the null hypothesis and assume nonconstant variance

Means and SDs

Level strengthtype N Mean Std Dev1 8 15.42 1.232 8 18.52 1.253 8 15.00 2.484 8 9.74 0.815 8 12.34 0.76

Remedies

• Delete outliers

– Is their removal important?• Use weights (weighted regression)

• Transformations

• Nonparametric procedures

What to do here?

• Not really any obvious outliers

• Do not see pattern of increasing or decreasing variance or skewed dists

• Will consider

–Weighted ANOVA

–Mixed model ANOVA

Weighted least squares

• We used this with regression–Obtain model for how the sd

depends on the explanatory variable (plotted absolute value of residual vs x)–Then used weights inversely

proportional to the estimated variance

Weighted Least Squares

• Here we can compute the variance for each level

• Use these as weights in PROC GLM

• We will illustrate with the soldering example from KNNL

Obtain the variances and weights

proc means data=a1; var strength; by type; output out=a2 var=s2;data a2; set a2; wt=1/s2;

NOTE. Data set a2 has 5 cases

Proc Means Output

Level oftype N

strength

Mean Std Dev1 8 15.4200000 1.23713956

2 8 18.5275000 1.25297076

3 8 15.0037500 2.48664397

4 8 9.7412500 0.81660337

5 8 12.3400000 0.76941536

Merge and then use the weights in PROC GLM

data a3; merge a1 a2; by type; proc glm data=a3; class type; model strength=type; weight wt; lsmeans type / cl; run;

Output

Source DFSum of

Squares Mean Square F Value Pr > FModel 4 324.213099 81.0532747 81.05 <.0001

Error 35 35.0000000 1.0000000

Corrected Total 39 359.213099

Data have been standardized to have a variance of 1

LSMEANS Output

typestrength

LSMEANStandard

Error Pr > |t|95% Confidence

Limits1 15.4200000 0.4373949 <.0001 14.53204

116.30795

92 18.5275000 0.4429921 <.0001 17.62817

819.42682

23 15.0037500 0.8791614 <.0001 13.21895

716.78854

34 9.7412500 0.2887129 <.0001 9.155132 10.32736

85 12.3400000 0.2720294 <.0001 11.78775

112.89224

9

Because of weights, standard errors simply based on sample variances of each level

Mixed Model ANOVA

• Relax the assumption of constant variance rather than including a “known” weight

• This involves moving to a mixed model procedure

• Topic will not be on exam but wanted you to be aware of these model capabilities

SAS Codeproc glimmix data=a1;

class type;

model strength=type / ddfm=kr;

random residual / group=type;

run;This allows the variance to differ in each level and a degrees of freedom adjustment is used to account for this

GLIMMIX OUTPUTFit Statistics

-2 Res Log Likelihood 122.11AIC (smaller is better) 132.11AICC (smaller is better) 134.18BIC (smaller is better) 139.88CAIC (smaller is better) 144.88HQIC (smaller is better) 134.79Generalized Chi-Square 35.00Gener. Chi-Square / DF 1.00

Covariance Parameter Estimates

Cov Parm Group EstimateStandard

ErrorResidual (VC) type 1 1.5305 0.8181Residual (VC) type 2 1.5699 0.8392Residual (VC) type 3 6.1834 3.3052Residual (VC) type 4 0.6668 0.3564Residual (VC) type 5 0.5920 0.3164

Type III Tests of Fixed Effects

EffectNum

DFDen DF F Value Pr > F

type 4 14.81 71.78 <.0001

Really 3 groups of variances

SAS Codeproc glimmix data=a1;

class type;

model strength=type / ddfm=kr;

random residual / group=type1;

run;Type1 was created to identify Type 1 and 2, Type 3, and Type

4 and 5 as 3 groups

GLIMMIX OUTPUTFit Statistics

-2 Res Log Likelihood 122.13AIC (smaller is better) 128.13AICC (smaller is better) 128.91BIC (smaller is better) 132.80CAIC (smaller is better) 135.80HQIC (smaller is better) 129.74Generalized Chi-Square 35.00Gener. Chi-Square / DF 1.00

Covariance Parameter Estimates

Cov Parm Group EstimateStandard

ErrorResidual (VC) Grp 1 1.5502 0.5859Residual (VC) Grp 2 6.1834 3.3052Residual (VC) Grp 3 0.6294 0.2379

Type III Tests of Fixed Effects

EffectNum

DFDen DF F Value Pr > F

type 4 19.8 77.68 <.0001

Better BIC but same general type conclusion

Transformation Guides

• When σi2 is proportional to μi, use

• When σi is proportional to μi, use log(y)

• When σi is proportional to μi2, use 1/y

• For proportions, use arcsin( )–arsin(sqrt(y)) in a SAS data step

• Box-Cox transformation

Y

Y

Example

• Consider study on KNNL pg 790

• Y: time between computer failures

• X: three locations data a3;

infile 'u:\.www\datasets512\CH18TA05.txt'; input time location interval; symbol1 v=circle; proc gplot; plot time*location; run;

Scatterplot

Outlier or skewed distribution? Can consider transformation first

Box-Cox Transformation

• Can consider regression

and 1-b1 is the power to raise Y by

• Can try various “convenient” powers

• Can use SAS directly to calculate the power

log( ) vs log( )i is y

E(logsig) = 0.90 + .79 logmu

Power should be 1-.79 ≈ 0.20

Using SAS

proc transreg data=a3;

model boxcox(time / lambda=-2 to 2

by .2) = class(location);

run;

OutputBox-Cox Transformation Information for time

Lambda R-Square Log Like-1.0 0.24 -73.040-0.8 0.27 -67.330-0.6 0.31 -62.316-0.5 0.33 -60.144-0.4 0.35 -58.239-0.2 0.38 -55.346 *0.0 + 0.39 -53.830 *0.2 0.38 -53.769 <0.4 0.36 -55.118 *0.5 0.34 -56.2730.6 0.32 -57.7120.8 0.29 -61.3141.0 0.25 -65.675

< - Best Lambda* - 95% Confidence Interval+ - Convenient Lambda

Transforming data in SAS

data a3;

set a3;

transtime = time**0.20;

symbol1 v=circle i=none;

proc gplot;

plot transtime*location;

run;

Much more constant spread in data!

Nonparametric approach

• Based on ranks

• See KNNL section 18.7, p 795

• See the SAS procedure NPAR1WAY

Rust Inhibitor Analysis

Source DFSum of

Squares Mean Square F Value Pr > FModel 3 15953.4660 5317.82200 866.12 <.0001Error 36 221.03400 6.13983Corrected Total 39 16174.5000

Highly significant F test. Even if there is a violation of Normality, the evidence is

overwhelming

Nonparametric AnalysisWilcoxon Scores (Rank Sums) for Variable eff

Classified by Variable abrand

abrand NSum ofScores

ExpectedUnder H0

Std DevUnder H0

MeanScore

1 10 128.0 205.0 32.014119 12.802 10 355.0 205.0 32.014119 35.503 10 255.0 205.0 32.014119 25.504 10 82.0 205.0 32.014119 8.20

Average scores were used for ties.

Kruskal-Wallis TestChi-Square 33.7041

DF 3

Pr > Chi-Square <.0001

Last slide

• We’ve finished most of Chapters 17 and 18.

• We used program topic23.sas to generate the output.