Linear Modelling I

Richard MottWellcome Trust Centre for Human

Genetics

Synopsis

• Linear Regression• Correlation• Analysis of Variance• Principle of Least Squares

Correlation

Correlation and linear regression

• Is there a relationship?• How do we summarise it?• Can we predict new obs?• What about outliers?

Correlation Coefficient r• -1 < r < 1

• r=0 no relationship

• r=0.6

• r=1 perfect positive linear

• r=-1 perfect negative linear

Examples of Correlation(taken from Wikipedia)

Calculation of r• Data

Correlation in R

> cor(bioch$Biochem.Tot.Cholesterol,bioch$Biochem.HDL,use="complete")[1] 0.2577617

> cor.test(bioch$Biochem.Tot.Cholesterol,bioch$Biochem.HDL,use="complete")

Pearson's product-moment correlation

data: bioch$Biochem.Tot.Cholesterol and bioch$Biochem.HDL t = 11.1473, df = 1746, p-value < 2.2e-16alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.2134566 0.3010088 sample estimates: cor 0.2577617

> pt(11.1473,df=1746,lower.tail=FALSE) # T distribution on 1746 degrees of freedom[1] 3.154319e-28

Linear Regression

Fit a straight line to data

• a intercept• b slope• ei error

– Normally distributed– E(ei) = 0– Var(ei) = s2

Example: simulated data

R code> # simulate 30 data points> x <- rnorm(30) > e <- rnorm(30)> x <- 1:30> e <- rnorm(30,0,5)> y <- 1 + 3*x + e

> # fit the linear model> f <- lm(y ~ x)

> # plot the data and the predicted line> plot(x,y)> abline(reg=f)

> print(f)

Call:lm(formula = y ~ x)

Coefficients:(Intercept) x -0.08634 3.04747

Least Squares

• Estimate a, b by least squares

• Minimise sum of squared residuals between y and the prediction a+bx

• Minimise

Why least squares?

• LS gives simple formulae for the estimates for a, b

• If the errors are Normally distributed then the LS estimates are “optimal”In large samples the estimates converge to the true valuesNo other estimates have smaller expected errorsLS = maximum likelihood

• Even if errors are not Normal, LS estimates are often useful

Analysis of Variance (ANOVA)LS estimates have an important property: they partition the sum of squares (SS) into fitted and error components

• = +

• total SS = fitting SS + residual SS• only the LS estimates do this

Component SS Degrees of freedom

Mean Square(ratio of SS to df)

F-ratio (ratio of FMS/RMS)

Fitting SS 1

Residual SS n-2

Total SS n-1

ANOVA in R

Component SS Degrees of freedom

Mean Square F-ratio

Fitting SS 20872.7 1 20872.7 965Residual SS 605.6 28 21.6Total SS 21478.3 29

> anova(f)Analysis of Variance Table

Response: y Df Sum Sq Mean Sq F value Pr(>F) x 1 20872.7 20872.7 965 < 2.2e-16 ***Residuals 28 605.6 21.6

> pf(965,1,28,lower.tail=FALSE)[1] 3.042279e-23

Hypothesis testing• no relationship between y and x

• Assume errors ei are independent and normally distributed N(0,s2)• If H0 is true then the expected values of the sums of squares in the ANOVA are

• Expectation• Degrees of Freedom n-1 1 n-2• F ratio = (fitting MS)/(residual MS) ~ 1 under H0

• F >> 1 implies we reject H0

• F is distributed as F(1,n-2)

Degrees of Freedom• Suppose are iid N(0,1)

• Then ie n independent variables

• What about ?

• These values are constrained to sum to 0:

• Therefore the sum is distributed as if it comprised one fewer observation, hence it has n-1 df (for example, its expectation is n-1)

• In particular, if p parameters are estimated from a data set, then the residuals

have p constraints on them, so they behave like n-p independent variables

The F distribution• If e1….en are independent and identically distributed (iid)

random variables with distribution N(0,s2), then:• e1

2/s2 … en2/s2 are each iid chi-squared random variables with

chi-squared distribution on 1 degree of freedom c12

• The sum Sn = Si ei2/s2 is distributed as chi-squared cn

2

• If Tm is a similar sum distributed as chi-squared cm2, but

independent of Sn, then (Sn/n)/(Tm/m) is distributed as an F random variable F(n,m)

• Special cases:– F(1,m) is the same as the square of a T-distribution on m df– for large m, F(n,m) tends to cn

2

ANOVA – HDL example> ff <- lm(bioch$Biochem.HDL ~ bioch$Biochem.Tot.Cholesterol)> ff

Call:lm(formula = bioch$Biochem.HDL ~

bioch$Biochem.Tot.Cholesterol)

Coefficients: (Intercept) bioch$Biochem.Tot.Cholesterol 0.2308 0.4456

> anova(ff)Analysis of Variance Table

Response: bioch$Biochem.HDL Df Sum Sq Mean Sq F value

Pr(>F) bioch$Biochem.Tot.Cholesterol 1 149.660 149.660 1044 Residuals 1849 265.057 0.143

> pf(1044,1,28,lower.tail=FALSE)[1] 1.040709e-23

HDL = 0.2308 + 0.4456*Cholesterol

correlation and ANOVA

• r2 = FSS/TSS = fraction of variance explained by the model

• r2 = F/(F+n-2)– correlation and ANOVA are equivalent– Test of r=0 is equivalent to test of b=0 – T statistic in R cor.test is the square root of the ANOVA F statistic

– r does not tell anything about magnitudes of estimates of a, b– r is dimensionless

Effect of sample size

Total Cholesterol vs HDL dataExample R session to sample subsets of data and compute correlations

seqq <- seq(10,300,5)corr <- matrix(0,nrow=length(seqq),ncol=2)colnames(corr) <- c( "sample size", "P-value")n <- 1for(i in seqq) {

res <- rep(0,100)for(j in 1:100) {s <- sample(idx,i)data <- bioch[s,]co <- cor.test(data$Biochem.Tot.Cholesterol, data$Biochem.HDL,na="pair")res[j] <- co$p.value}m <- exp(mean(log(res)))cat(i, m, "\n")corr[n,] <- c(i, m)n <- n+1

}

Calculating the right sample size n

• The R library “pwr” contains functions to compute the sample size for many problems, including correlation pwr.r.test() and ANOVA pwr.anova.test()

Problems with non-linearityAll plots have r=0.8 (taken from Wikipedia)

Non-Parametric CorrelationSpearman Rank Correlation Coefficient

• Replace observations by their ranks• eg x= ( 5, 1, 4, 7 ) -> rank(x) = (3,1,2,4)• Compute sum of squared differences between

ranks

• in R:– cor( x, y, method=“spearman”)– cor.test(x,y,method=“spearman”)

Spearman Correlation> cor.test(xx,y, method=“pearson”)

Pearson's product-moment correlation

data: xx and y t = 0.0221, df = 28, p-value = 0.9825alternative hypothesis: true correlation is not equal

to 0 95 percent confidence interval: -0.3566213 0.3639062 sample estimates: cor 0.004185729

> cor.test(xx,y,method="spearman")

Spearman's rank correlation rho

data: xx and y S = 2473.775, p-value = 0.01267alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.4496607

Multiple Correlation• The R cor function can be used to compute pairwise correlations

between many variables at once, producing a correlation matrix.

• This is useful for example, when comparing expression of genes across subjects.

• Gene coexpression networks are often based on the correlation matrix.

• in Rmat <- cor(df, na=“pair”)

– computes the correlation between every pair of columns in df, removing missing values in a pairwise manner

– Output is a square matrix correlation coefficients

One-Way ANOVA

• Model y as a function of a categorical variable taking p values– eg subjects are classified into p families– want to estimate effect due to each family and

test if these are different– want to estimate the fraction of variance

explained by differences between families – ( an estimate of heritability)

One-Way ANOVA

LS estimators

average over group i

One-Way ANOVA

• Variance is partitioned in to fitting and residual SS

total SS

n-1

fitting SSbetween groups

p-1

residual SSwith groups

n-p degrees of freedom

One-Way ANOVA

Component SS Degrees of freedom

Mean Square(ratio of SS to df)

F-ratio (ratio of FMS/RMS)

Fitting SS p-1

Residual SS n-p

Total SS n-1

Under Ho: no differences between groupsF ~ F(p-1,n-p)

One-Way ANOVA in Rfam <- lm( bioch$Biochem.HDL ~ bioch$Family )> anova(fam)Analysis of Variance Table

Response: bioch$Biochem.HDL Df Sum Sq Mean Sq F value Pr(>F) bioch$Family 173 6.3870 0.0369 3.4375 < 2.2e-16 ***Residuals 1727 18.5478 0.0107 ---Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 >

Component SS Degrees of freedom

Mean Square(ratio of SS to df)

F-ratio (ratio of FMS/RMS)

Fitting SS 6.3870 173 0.0369 3.4375

Residual SS 18.5478 1727 0.0107

Total SS 24.9348 1900

Non-ParametricOne-Way ANOVA

• Kruskall-Wallis Test• Useful if data are highly non-Normal– Replace data by ranks– Compute average rank within each group– Compare averages– kruskal.test( formula, data )

Factors in R

• Grouping variables in R are called factors• When a data frame is read with read.table()– a column is treated as numeric if all non-missing entries are

numbers– a column is boolean if all non-missing entries are T or F (or

TRUE or FALSE)– a column is treated as a factor otherwise– the levels of the factor is the set of distinct values– A column can be forced to be treated as a factor using the

function as.factor(), or as a numeric vector using as.numeric()

Linear Modelling in R

• The R function lm() fits linear models• It has two principal arguments (and some

optional ones)• f <- lm( formula, data )– formula is an R formula– data is the name of the data frame containing the

data (can be omitted, if the variables in the formula include the data frame)

formulae in R• Biochem.HDL ~ Biochem$Tot.Cholesterol– linear regression of HDL on Cholesterol – 1 df

• Biochem.HDL ~ Family– one-way analysis of variance of HDL on Family– 173 df

• The degrees of freedom are the number of independent parameters to be estimated

ANOVA in R• f <- lm(Biochem.HDL ~ Tot.Cholesterol, data=biochem)• [OR f <- lm(biochem$Biochem.HDL ~ biochem$Tot.Cholesterol)]

• a <- anova(f)

• f <- lm(Biochem.HDL ~ Family, data=biochem)• a <- anova(f)

The Chi-Squared Test• Data are a table of counts, with the row and column margins

fixed.

• Ho: the counts in each cell are consistent with the rows and columns acting independently

n11r1c1/N

n12r1c2/N

r1 = n11+ n12

n21r2c1/N

n22r2c2/N

r2 = n21+ n22

c1

= n11+ n21

c2

= n12+ n22

N

The Chi-Squared Test

Heuristic Justification: The number of counts in a given cell ij follow a Multinomial Distribution, with expectation Npij and variance Npij (1-pij). Then (O-E)