multivariate analysis€¦ · multivariate analysis anova and manova ... it tests the hypothesis...

Multivariate Analysis ANOVA and MANOVA

Prof. Dr. Anselmo E de Oliveira

anselmo.quimica.ufg.br

[email protected]

http://www.quimica.ufg.br/docentes/anselmomailto:[email protected]

ANOVA

• ANalysis Of VAriance

– separar e estimar diferentes causas de variação

– testar se a alteração de um fator controlado leva a uma diferença significativa entre os valores médios obtidos

– um fator (controlado ou aleatório) além do erro aleatório da medida ANOVA One-Way

Como Funciona a ANOVA

• ANOVA divide, basicamente, a

variabilidade em variabilidade Entre

Grupos e variabilidade Dentro dos

Grupos, e compara as duas

• Quanto maior for a primeira

comparada à segunda, maior será a

evidência de que existe variabilidade

entre grupos


• Define-se a soma de quadrados total, SQT, calculada a partir de todos os dados, em que 𝑥 é a média amostral global

𝑺𝑸𝑻 = 𝒙𝒊 − 𝒙 𝟐

𝒊

• Note que a estimativa usual de variância de uma amostra é

𝝈𝟐 =𝑺𝑸𝑻𝑵− 𝟏

= 𝑴𝑸

• Podemos subdividir SQT como 𝑺𝑸𝑻 = 𝑺𝑸𝑫 + 𝑺𝑸𝑬

Soma de Quadrados Dentro do Grupo Soma de Quadrados Entre os Grupos


• SQD e SQE são definidos como

𝑺𝑸𝑫 = 𝒙𝒊 − 𝒙 𝟏𝟐

𝒈𝒑𝟏

+ 𝒙𝒊 − 𝒙 𝟐𝟐

𝒈𝒑𝟐

+⋯

com 𝑥 𝑘 é a média amostral do grupo k, e 𝑺𝑸𝑬 = 𝒏𝟏 𝒙 𝟏 − 𝒙

𝟐 + 𝒏𝟐 𝒙 𝟐 − 𝒙 𝟐 +⋯

em que nk é o tamanho amostral do

grupo k


• Após separar a variabilidade, podem-se obter estimativas independentes da variância populacional comum 𝜎2 a partir de SQE e SQD. Essas estimativas são chamadas de médias quadráticas (MQ), e obtemos as seguintes estimativas

𝝈𝒆𝒏𝒕𝒓𝒆𝟐 =

𝑺𝑸𝑬𝒎− 𝟏

𝝈𝒅𝒆𝒏𝒕𝒓𝒐𝟐 =

𝑺𝑸𝑫𝑵−𝒎

em que m é o número de grupos e N é o tamanho amostral total

Comparação de várias médias

• Problema: estabilidade de um

reagente fluorescente

condições medidas 𝒙 𝒎

A

recém preparada 102, 100, 101 101

B

armazenada, 1h, escuro 101, 101, 104 102

C

armazenada, 1h, sombra 97, 95, 99 97

D

armazenada, 1h, luz 90, 92, 94 92

𝒙 = 𝟗𝟖


• SQD

𝑺𝑸𝑫 = 𝒙𝒊 − 𝒙 𝟏𝟐

𝒈𝒑𝟏

+ 𝒙𝒊 − 𝒙 𝟐𝟐

𝒈𝒑𝟐

+⋯

𝑺𝑸𝑫 = 𝟏𝟎𝟐 − 𝟏𝟎𝟏

𝟐 + 𝟏𝟎𝟎 − 𝟏𝟎𝟏 𝟐 + 𝟏𝟎𝟏 − 𝟏𝟎𝟏 𝟐𝒂𝒎𝒐𝒔𝒕𝒓𝒂 𝑨

+ 𝟏𝟎𝟏 − 𝟏𝟎𝟐 𝟐 + 𝟏𝟎𝟏 − 𝟏𝟎𝟐 𝟐 + 𝟏𝟎𝟒 − 𝟏𝟎𝟐 𝟐𝒂𝒎𝒐𝒔𝒕𝒓𝒂 𝑩

+ 𝟗𝟕 − 𝟗𝟕 𝟐 + 𝟗𝟓 − 𝟗𝟕 𝟐 + 𝟗𝟗 − 𝟗𝟕 𝟐𝒂𝒎𝒐𝒔𝒕𝒓𝒂 𝑪

+ 𝟗𝟎 − 𝟗𝟐 𝟐 + 𝟗𝟐 − 𝟗𝟐 𝟐 + 𝟗𝟒 − 𝟗𝟐 𝟐𝒂𝒎𝒐𝒔𝒕𝒓𝒂 𝑫

𝑺𝑸𝑫 = 𝟐 + 𝟔 + 𝟖 + 𝟖 = 𝟐𝟒


• SQE 𝑺𝑸𝑬 = 𝒏𝟏 𝒙 𝟏 − 𝒙

𝟐 + 𝒏𝟐 𝒙 𝟐 − 𝒙 𝟐 +⋯

no exemplo n1 = n2 = n3 = n4 = 3

𝑺𝑸𝑬 = 𝟑 𝟏𝟎𝟏 − 𝟗𝟖

𝟐 + 𝟑 𝟏𝟎𝟐 − 𝟗𝟖 𝟐 + 𝟑 𝟗𝟕 − 𝟗𝟖 𝟐 + 𝟑 𝟗𝟐 − 𝟗𝟖 𝟐

𝑺𝑸𝑬 = 𝟐𝟕 + 𝟒𝟖 + 𝟑 + 𝟏𝟎𝟖 = 𝟏𝟖𝟔


• SQT

𝑺𝑸𝑻 = 𝑺𝑸𝑫 + 𝑺𝑸𝑬

𝑺𝑸𝑻 = 𝟐𝟒 + 𝟏𝟖𝟔 = 𝟐𝟏𝟎

• 𝝈𝒆𝒏𝒕𝒓𝒆𝟐 =

𝑺𝑸𝑬

𝒎−𝟏

𝝈𝒆𝒏𝒕𝒓𝒆𝟐 =

𝟏𝟖𝟔

𝟒 − 𝟏= 𝟔𝟐

• 𝝈𝒅𝒆𝒏𝒕𝒓𝒐𝟐 =

𝑺𝑸𝑫

𝑵−𝒎

𝝈𝒅𝒆𝒏𝒕𝒓𝒐𝟐 =

𝟐𝟒

𝟏𝟐 − 𝟒= 𝟑


• Teste da Hipótese

– hipótese nula, H0: todas as amostras

pertencem à uma mesma população com

média e variância 𝝈𝟐

– estimativa de 𝝈𝟐

• variação dentro das amostras: 𝝈𝒅𝒆𝒏𝒕𝒓𝒐𝟐

• variação entre as amostras: 𝝈𝒆𝒏𝒕𝒓𝒆𝟐


• Teste da Hipótese

hipótese nula, H0 :

verdadeira • estimativas de 𝝈𝟐 não devem

diferir significativamente

falsa

• 𝝈𝒆𝒏𝒕𝒓𝒆𝟐 > 𝝈𝒅𝒆𝒏𝒕𝒓𝒐

𝟐

•𝝈𝒆𝒏𝒕𝒓𝒆𝟐

𝝈𝒅𝒆𝒏𝒕𝒓𝒐𝟐 > 𝟏

𝝈𝒆𝒏𝒕𝒓𝒆𝟐 − 𝝈𝒅𝒆𝒏𝒕𝒓𝒐

𝟐 = 𝟎


• Teste da Hipótese e Teste F

– pra saber se um valor é maior do que um

outro uma cauda, teste F

se Fcalc > Ftab hipótese nula é descartada

como Fcalc é maior do que Ftab,

com 95% de confiança, as

médias das amostras diferem

significativamente

Tabela F, P = 0,05

• é o número do grau de liberdade •F é sempre maior do que 1

http://home.comcast.net/~sharov/PopEcol/tables/f005.htmlhttp://home.comcast.net/~sharov/PopEcol/tables/f005.htmlhttp://home.comcast.net/~sharov/PopEcol/tables/f005.htmlhttp://home.comcast.net/~sharov/PopEcol/tables/f005.htmlhttp://home.comcast.net/~sharov/PopEcol/tables/f005.htmlhttp://home.comcast.net/~sharov/PopEcol/tables/f005.html

Tabela ANOVA

fonte de variação SQ MQ F

entre as amostras 186 3 62 20,7

dentro das amostras 24 8 3

Total 210 11

Octave

• Faz o teste F para comparar se as médias são estatisticamente iguais

• Não mostra a tabela

• Calcula o valor de F

• Calcula o valor de p

– H0: as médias são iguais?

– p < 0,05 hipótese nula é falsa

• Calcula gle (graus de liberdade entre) e gld (graus de liberdade dentro)

• Matriz Y: grupos em colunas

Octave

> [p,F,gle,gld] = anova(Y)

Aplicativo Java

http://www.psych.utah.edu/stat/introstats/anovaflash.html

MANOVA

MANOVA: Multivariate Analysis of Variance

http://www.google.com.br/url?sa=t&rct=j&q=manova site:usc.edu&source=web&cd=1&ved=0CCYQFjAA&url=http://www-bcf.usc.edu/~mmclaugh/550x/PPTslides/WeekElevenSlides/MANOVA.ppt&ei=vGmVT6ebGpSQ8wS_7JiqBA&usg=AFQjCNGuG6lhm_BflMGHKaNLQYzrIYNKwg

MANOVA: What Kinds of Hypotheses Can it Test?

• A MANOVA or multivariate analysis of variance is a way to test the hypothesis that one or more independent variables (IV), or factors, have an effect on a set of two or more dependent variables (DV) – For example, you might wish to test the hypothesis that

sex and ethnicity interact to influence a set of job-related outcomes including attitudes toward co-workers, attitudes toward supervisors, feelings of belonging in the work environment, and identification with the corporate culture

– As another example, you might want to test the hypothesis that three different methods of teaching writing result in significant differences in ratings of student creativity, student acquisition of grammar, and assessments of writing quality by an independent panel of judges

Why Should You Do a MANOVA?

• You do a MANOVA instead of a series of one-at-a-time ANOVAs for two main reasons – Supposedly to reduce the experiment-wise level of Type I

error 8 F tests at 0.05 each means the experiment-wise probability of making a Type I error (rejecting the null hypothesis when it is in fact true) is 40%! The so-called overall test or omnibus test protects against this inflated error probability only when the null hypothesis is true. If you follow up a significant multivariate test with a bunch of ANOVAs on the individual variables without adjusting the error rates for the individual tests, there’s no “protection”

– Another reasons to do MANOVA. None of the individual ANOVAs may produce a significant main effect on the DV, but in combination they might, which suggests that the variables are more meaningful taken together than considered separately

• MANOVA takes into account the intercorrelations among the DVs

Type I and II error

• Type I error – A type I error occurs when one rejects the null hypothesis

when it is true

• Type II error – A type II error occurs when one rejects the alternative

hypothesis (fails to reject the null hypothesis) when the alternative hypothesis is true

If there is a diagnostic value demarcating the choice of two means, moving

it to decrease type I error will increase type II error (and vice-versa)

http://www.cs.uni.edu/~campbell/stat/inf5.html

Assumptions of MANOVA

1.Multivariate normality – All of the DVs must be distributed normally (can visualize

this with histograms; tests are available for checking this out)

– Any linear combination of the DVs must be distributed normally • Check out pairwise relationships among the DVs for

nonlinear relationships using scatter plots


– All subsets of the variables must have a multivariate normal distribution • These requirements are rarely if ever tested in practice

• MANOVA is assumed to be a robust test that can stand up to departures from multivariate normality in terms of Type I error rate

Log-likelihood density (log scale) using multivariate normal distribution (correlated)

http://cxc.harvard.edu/contrib/sherpa/scipy11/


– Statistical power (power to detect a main or interaction effect) may be reduced when distributions are very plateau-like (platykurtic)

– If the classes in the center of the distribution have more or less the same frequency, the resulting histogram looks like a plateau

Assumptions of MANOVA, cont’d

2. Homogeneity of the covariance matrices – In ANOVA we talked about the need for the

variances of the dependent variable to be equal across levels of the independent variable • In MANOVA, the univariate requirement of equal

variances has to hold for each one of the dependent variables

– In MANOVA we extend this concept and require that the “covariance matrices” be homogeneous • Computations in MANOVA require the use of matrix

algebra, and each Person’s “score” on the dependent variables is actually a “vector” of scores on DV1, DV2, DV3, …, DVn

• The matrices of the covariances -the variance shared between any two variables- have to be equal across all levels of the independent variable


– This homogeneity assumption is tested with a test that is similar to Levene’s test for the ANOVA case. It is called Box’s M, and it works the same way: it tests the hypothesis that the covariance matrices of the dependent variables are significantly different across levels of the independent variable

• Putting this in English, what you don’t want is the case where if your

independent variable (IV), was, for example, ethnicity, all the people

in the “other” category had scores on their 6 dependent variables

clustered very tightly around their mean, whereas people in the

“white” category had scores on the vector of 6 dependent variables

clustered very loosely around the mean. You don’t want a

leptokurtic set of distributions for one level of the IV and a

platykurtic set for another level


• If Box’s M is significant, it means you have violated an assumption of

MANOVA. This is not much of a problem if you have equal cell sizes

and large N; it is a much bigger issue with small sample sizes

and/or unequal cell sizes (in factorial anova if there are unequal cell

sizes the sums of squares for the three sources (two main effects and

interaction effect) won’t add up to the Total Sum of Squares, SS)


3. Independence of observations – Subjects’ scores on the dependent measures

should not be influenced by or related to scores of other subjects in the condition or level

– Can be tested with an intraclass correlation coefficient if lack of independence of observations is suspected

MANOVA no Action®
http://www.portalaction.com.br/552-manova

multivariate analysis€¦ · multivariate analysis anova and manova ... it tests the hypothesis...

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