topic 20: single factor analysis of variancebacraig/notes512/topic_20.pdf · one-way anova • the...

Topic 20: Single Factor

Analysis of Variance

Outline

• Single factor Analysis of Variance

–One set of treatments

•Cell means model

•Factor effects model

–Link to linear regression using indicator explanatory variables

One-Way ANOVA• The response variable Y is continuous

• The explanatory variable is categorical

–We call it a factor

–The possible values are called levels

• This approach is a generalization of the

independent two-sample pooled t-test

• In other words, it can be used when there

are more than two treatments

Data for One-Way ANOVA

• Y is the response variable

• X is the factor (it is qualitative/discrete)

– r is the number of levels

–often refer to these levels as groups

or treatments

Notation• For Yij we use

– i to denote the level of the factor

– j to denote the jth observation at

factor level i

• i = 1, . . . , r levels of factor X

• j = 1, . . . , ni observations for level i

of factor X

– Note that ni does not need to be the

same for each level

KNNL Example (p 685)

• Y is the number of cases of cereal sold

• X is the design of the cereal package

– there are four levels for X because

there are four different designs

• i =1 to 4 levels

• j =1 to ni stores with design i (ni=5,5,4,5)

• Will use n if ni the same across levels

Data for one-way ANOVA

data a1;

infile 'c:../data/ch16ta01.txt';

input cases design store;

proc print data=a1;

run;

The data

Obs cases design store

1 11 1 1

2 17 1 2

3 16 1 3

4 14 1 4

5 15 1 5

6 12 2 1

7 10 2 2

8 15 2 3

Plot the data

symbol1 v=circle i=none;

proc gplot data=a1;

plot cases*design;

run;

The scatterplot

Plot the means

proc means data=a1;

var cases; by design;

output out=a2 mean=avcases;

proc print data=a2;

symbol1 v=circle i=join;

proc gplot data=a2;

plot avcases*design;

run;

Proc Print: New Data Set

Obs design _TYPE_ _FREQ_ avcases

1 1 0 5 14.6

2 2 0 5 13.4

3 3 0 4 19.5

4 4 0 5 27.2

Proc Gplot: Means Plot

The Model

• We assume that the response variable is

–Normally distributed with a

1. mean that may depend on the level of the factor

2. constant variance

• All observations assumed independent

• NOTE: Same assumptions as linear regression except there is no assumed linear relationship between X and E(Y|X)

The scatterplot

Based on scatterplot and design:

Independence?

Constant variance?

Normally distributed?

Cell Means Model

• A “cell” refers to a level of the factor

• Yij = μi + εij

–where μi is the theoretical mean or

expected value of all observations

at level (or in cell) i

– the εij are iid N(0, σ2) which means

–Yij ~N(μi, σ2) and independent

Parameters• The parameters of the model are

– μ1, μ2, … , μr

–σ2

• Question (Version 1) – Does our

explanatory variable help explain Y?

• Question (Version 2) – Do the μi vary?

H0: μ1= μ2= … = μr = μ (a constant)

Ha: not all μ’s are the same

Estimates

• Estimate μi by the mean of the

observations at level i, (sample mean)

•

• For each level i, also get an estimate of the variance

• (sample variance)

• We combine these to get an overall estimate of σ2

• Same approach as pooled t-test

iY

i i ij iˆ Y n Y

2

2

i ij i iY n 1s Y2

is

Pooled estimate of σ2

• If the ni were all the same we would

average the

– Do not average the si

• In general we pool the , giving

weights proportional to the df, ni -1

• The pooled estimate is

2

is

2

is

2 2

i i i

2

i i T

n 1 n 1

n 1 n

s s

s r

Running proc glm

proc glm data=a1;

class design;

model cases=design;

means design;

lsmeans design / stderr;

run;

Difference 1: Need

to specify factor

variables

Difference 2: Ask

for mean estimates

Output

Class Level

Information

Class Levels Values

design 4 1 2 3 4

Number of Observations Read 19

Number of Observations Used 19

Important to check

these summaries!!!

SAS 9.4 default output

for MEANS statement

MEANS statement output

Level of

design N

cases

Mean Std Dev

1 5 14.6000000 2.30217289

2 5 13.4000000 3.64691651

3 4 19.5000000 2.64575131

4 5 27.2000000 3.96232255

Table of sample means and

sample standard deviations

SAS 9.4 default output

for LSMEANS statement

LSMEANS statement

output

design cases LSMEAN

Standard

Error Pr > |t|

1 14.6000000 1.4523544 <.0001

2 13.4000000 1.4523544 <.0001

3 19.5000000 1.6237816 <.0001

4 27.2000000 1.4523544 <.0001

Provides estimates based on

model (i.e., constant variance)

Notation for ANOVA

i ij ij

ij Ti j

T ii

Y n (trt sample mean)

Y n (overall sample mean)

n n (total number of observations)

Y

Y

i iiWhen n n for all i, r YY

ANOVA Table

Source df SS MS

Model r-1 SSR/dfR

Error nT-r SSE/dfE

Total nT-1 SSTO/dfT

2

ii jY Y

2

ij ii jY Y

2

iji jY Y

ANOVA SAS Output

Source DF

Sum of

Squares

Mean

Square F Value Pr > F

Model 3 588.2210526 196.0736842 18.59 <.0001

Error 15 158.2000000 10.5466667

Cor Total 18 746.4210526

R-Square Coeff Var Root MSE cases Mean

0.788055 17.43042 3.247563 18.63158

Expected Mean Squares

• E(MSR) > E(MSE) when the group means are different

• See KNNL p 694 – 698 for more details

• In more complicated models, the EMS tell us how to construct the F test

2

22

i ii

i i Ti

E MSE

E MSR n 1

where n n

r

F test

• F* = MSR/MSE

• H0: μ1 = μ2 = … = μr

• Ha: not all of the μi are equal

• Under H0, F* ~ F(r-1, nT-r)

• Reject H0 when F* is large

• Typically report the P-value

Maximum Likelihood

Approach1. proc mixed data=a1;

class design;

model cases=design;

lsmeans design;

2. proc glimmix data=a1;

class design;

model cases=design / dist=normal;

lsmeans design;

run;

GLIMMIX Output

Model Information

Data Set WORK.A1

Response Variable cases

Response Distribution Gaussian

Link Function Identity

Variance Function Default

Variance Matrix Diagonal

Estimation Technique Restricted Maximum

Likelihood

Degrees of Freedom Method Residual

GLIMMIX Output

Fit Statistics

-2 Res Log Likelihood 84.12

AIC (smaller is better) 94.12

AICC (smaller is better) 100.79

BIC (smaller is better) 97.66

CAIC (smaller is better) 102.66

HQIC (smaller is better) 94.08

Pearson Chi-Square 158.20

Pearson Chi-Square / DF 10.55

GLIMMIX OutputType III Tests of Fixed Effects

Effect

Num

DF

Den

DF F Value Pr > F

design 3 15 18.59 <.0001

design Least Squares Means

design Estimate

Standard

Error DF t Value Pr > |t|

1 14.6000 1.4524 15 10.05 <.0001

2 13.4000 1.4524 15 9.23 <.0001

3 19.5000 1.6238 15 12.01 <.0001

4 27.2000 1.4524 15 18.73 <.0001

Factor Effects Model

• A reparameterization of the cell means

model

• Useful way at looking at more

complicated models

• Null hypotheses are easier to state

• Yij = μ + i + εij

– the εij are iid N(0, σ2)

Parameters

• The parameters of the model are

– μ, 1, 2, … , r

– σ2

• The cell means model had r + 1 parameters

– r μ’s and σ2

• The factor effects model has r + 2 parameters

– μ, the r ’s, and σ2

– Cannot uniquely estimate all parameters

An example

• Suppose r=3; μ1 = 10, μ2 = 20, μ3 = 30

• What is an equivalent set of parameters

for the factor effects model?

• We need to have μ + i = μi so…

1. μ = 0, 1 = 10, 2 = 20, 3 = 30

2. μ = 20, 1 = -10, 2 = 0, 3 = 10

3. μ = 5000, 1 = -4990, 2 = -4980, 3 = -4970

all provide the same means

Problem with factor effects?

• These parameters are not estimable or not well defined (i.e., not unique)

– There are many solutions to the least squares problem

– There is an X΄X matrix for this parameterization that does not have an inverse (perfect multicollinearity)

• We addressed similar situation in multiple regression. Parameter estimators provided by SAS are biased

Factor effects solution

• Put a constraint on the i

• Common to assume Σi i = 0

• This effectively reduces the number

of parameters by one

• Numerous other constraints possible

–Σi i = 100

– r = 0

Consequences

• Regardless of constraint, we always

have μi = μ + i

• The constraint Σi i = 0 implies

–μ = (Σi μi)/r (unweighted overall mean)

– i = μi – μ (group effect)

• The “unweighted” complicates

things when the ni are not all equal;

see KNNL p 702-708

Hypotheses

• H0: μ1 = μ2 = … = μr

• H1: not all of the μi are equal

are translated into

• H0: 1 = 2 = … = r = 0

• H1: at least one i is not 0

Estimates of parameters

• With the constraint Σi i = 0

ii

i

i i

ˆ

(if n n)

ˆ ˆ

r

Y

Y

Y

Solution used by SAS

• Recall, X΄X does not have an inverse

• We can use a generalized inverse in

its place

• (X΄X)- is the standard notation for

generalized inverse

• There are many generalized inverses,

each corresponding to a different

constraint

Solution used by SAS

• (X΄X)- used in proc glm corresponds to the constraint r = 0

• Recall that μ and the i are not estimable

• But the linear combinations μ + i are estimable

• These are estimated by the cell means (i.e., sample means)

Cereal package example

• Y is the number of cases of cereal sold

• X is the design of the cereal package

• i =1 to 4 levels

• j =1 to ni stores with design i

SAS coding for X•Class statement generates r explanatory

variables

•The ith explanatory variable is equal to 1 if the observation is from the ith group

•In other words, the rows of X are

1 1 0 0 0 for design=1

1 0 1 0 0 for design=2

1 0 0 1 0 for design=3

1 0 0 0 1 for design=4

Some SAS options

proc glm data=a1;

class design;

model cases=design

/xpx inverse solution;

run;

xpx OutputThe X'X Matrix

Int d1 d2 d3 d4 casesInt 19 5 5 4 5 354d1 5 5 0 0 0 73d2 5 0 5 0 0 67d3 4 0 0 4 0 78d4 5 0 0 0 5 136cases 354 73 67 78 136 7342

Contains

X’Y

Also Y’Y

inverse Output

X'X Generalized Inverse (g2)

Int d1 d2 d3 d4 casesInt 0.2 -0.2 -0.2 -0.2 0 27.2d1 -0.2 0.4 0.2 0.2 0 -12.6d2 -0.2 0.2 0.4 0.2 0 -13.8d3 -0.2 0.2 0.2 0.45 0 -7.7d4 0 0 0 0 0 0cases 27.2 -12.6 -13.8 -7.7 0 158.2

Inverse Matrix

•Actually, this matrix is

(X΄X)- (X΄X)- X΄Y

Y΄X(X΄X)- Y΄Y-Y΄X(X΄X)- X΄Y

•Parameter estimates are in upper

right corner, SSE is lower right

corner (last column on previous page)

solution Output:

Parameter estimates

StPar Est Err t PInt 27.2 B 1.45 18.73 <.0001d1 -12.6 B 2.05 -6.13 <.0001d2 -13.8 B 2.05 -6.72 <.0001d3 -7.7 B 2.17 -3.53 0.0030d4 0.0 B . . .

Caution Message

NOTE: The X'X matrix has beenfound to be singular, and ageneralized inverse was usedto solve the normal equations.Terms whose estimates arefollowed by the letter 'B' arenot uniquely estimable.

Interpretation

• If r = 0 (in our case, 4 = 0), then the

corresponding estimate should be zero

• The intercept μ is then estimated by the

sample mean of Group 4

• Since μ + i is the mean of group i, the i

are estimated as the differences between

the sample mean of Group i and the

sample mean of Group 4

Recall the means output

Level ofdesign N Mean Std Dev

1 5 14.6 2.32 5 13.4 3.63 4 19.5 2.64 5 27.2 3.9

Parameter estimates

based on means

Level ofdesign Mean

= 27.2 = 27.2

1 14.6 = 14.6-27.2 = -12.6

2 13.4 = 13.4-27.2 = -13.8

3 19.5 = 19.5-27.2 = -7.7

4 27.2 = 27.2-27.2 = 0

1

2

3

4

Last slide

• Read KNNL Chapter 16 up to 16.10

• We used programs topic20.sas to generate the output for today

• Will focus more on the relationship between regression and one-way ANOVA in next topic