1. Basics of regression analysis I Purpose of linear models Least-squares solution for linear models Analysis of diagnostics

2. Reason for linear models Purpose of regression is to reveal statistical relations between input and output variables. Statistics cannot reveal functional relationship. It is purpose of other scientific studies. Statistics can help validation of various functional relationship (models). Let us assume that we suspect that functional relationship has a form: where  is a vector of unknown parameters, x=(x 1,x 2,,,x p ) a vector of controllable parameters, and y is output,  is an error associated with the experiment. Then we can set up experiments for various values of x and get output (or response) for them. If the number of experiments is n then we will have n output values. Denote them as a vector y=(y 1,y 2,,,y n ). Purpose of statistics is to evaluate parameter vector using input and output values. If function f is a linear function of the parameters and errors are additive then we are dealing with linear model. For this model we can write Linear model is linearly dependent on parameters but not on input variables. For example is a linear model. But is not.

3. Assumptions Basic assumptions for analysis of linear model are: 1)the model is linear in parameters 2)the error structure is additive 3)Random errors have 0 mean, equal variances and they are uncorrelated. These assumptions are sufficient to deal with linear models. Uncorrelated with equal variance assumptions (number 3) can be removed. Then the treatments becomes a little bit more complicated. Note that for general solution, normal distribution of errors assumption is not used. This assumption is necessary to design test statistics. If this assumption does not work then we can use bootstrap to design test statistic. These assumptions can be written in a vector form: where y, 0, I,  are vectors and X is a matrix. This matrix is called a design matrix, input matrix etc. I is nxn identity matrix.

4. One parameter case When we have one variable (x) for predictor and one for response then problem becomes considerable simpler. In this case we have: Now let us assume that we have observations for n values of x (x 1,,,x n ) the response vector is (y 1,,,y n ). If we assume that errors are independent, have equal variances and normally distributed then maximum likelihood becomes least-square: If we solve this minimisation problem we get for estimations for coefficients:

5. One parameter case If you divide denominator and numerator by n 2 and use definitions of correlation and standard deviations then the second equation can be written as: Slope of the line is correlation between input and output variables. If slope is equal 0 (i.e. input and output variables are uncorrelated) then intercept becomes mean value of the observations

6. Solution for the general case Solution to the least-squares with linear model and and with given assumptions is: Let us show this. If we use the form of the model and write least squares equation (since we want to find solution with minimum least-squares error): and get the first and solve the equation then we can see that this solution is correct. If we use the formula for the solution and the expression of y then we can write: So solution is unbiased. Variance of the estimation is: Here we used the form of the solution and the assumption number 3)

7. Variance To calculate covariance matrix for estimated parameters we need to be able to calculate  2. Since it is the variance of the error term we can find it using the form of the solution. For the estimated error (denoted by r) we can write: If we use: It gives Since the matrix M is idempotent and symmetric, i.e. M 2 =M=M T, we can write: Where n is the number of the observations and p is the number of the fitted parameters. Then for unbiased estimator for the variance of the residual we can write:

8. Singular case: SVD and psuedoinversion The above form of the solution is true if matrices X and X T X are non-singular. I.e. the rank of the matrix X is equal to the number of parameters. If it is not true then either singular value decomposition or eignevalue filtering techniques are used. Fortunately most good properties of the linear model remains. Singular value decomposition (SVD): Any nxp matrix can be decomposed in a form: Where U is nxn and V is pxp orthogonal matrices (inverse is equal to transpose). D is nxp diagonal matrix of the singular values. If X is singular then number of non-zero diagonal elements of D is less than p. Then for X T X we can write: D T D is pxp diagonal matrix. If the matrix is non-singular then we can write: Since D T D is a diagonal matrix therefore its inverse is also diagonal matrix. Main trick used in SVD technique for equation solution is that when diagonals are 0 or close to 0 then instead of their inversion zero is used. I.e. pseudo inverse is calculated using:

9. Singular case: Ridge regression Another technique to deal with singular case is ridge regression. In this technique a constant value added to the diagonal terms X T X before inverting it. Mathematically it is equivalent to Tikhonov regularisation for ill-posed problems. In this technique one of the problems is how to find the regularisation parameter - . A value for this parameter can be found using cross validation. That is usually done by trial and error.

10. Standard diagnostics Before starting to model 1)Visualisation of data: 1)plotting predictor vs observations. These plots may give a clue about the relationship, outliers 2)Smootheners After modelling and fitting 2)Fitted values vs residuals. It may help to identify outliers, correctness of the model 3)Normal QQ plot of residuals. It may help to check distribution assumptions 4)Cook’s distance, reveal outliers, checks correctness of the model 5)Model assumptions - t tests given by default print of lm Checking model and designing tests 3)Cross-validation. If you have a choice of models then cross-validation may help to choose the “best” model 4)Bootstrap. Validity of the model can be checked if the distribution of statistic of interest is available. Or these distributions could be generated using bootstrap

11. Visualisation prior to modelling Different type of datasets may require different visualisation tools. For simple visualisation either plot(data) or pairs(data,panel=panel.smooth) could be used. Visualisation prior to modelling may help to propose model (form of the functional relationship between input and output, probability distribution of observation etc) For example for dataset women - where weights and heights for 15 cases have been measured. plot and pairs commands produce these plots:

12. After modelling: linear models After modelling the results should be analysed. For example attach(women) lm1 = lm(weight~height) It means that we want a liner model (we believe that dependence of weight on height is linear) weight=  0 +  1 *height Results could be viewed using lm1 summary(lm1) The last command will produce significant of various coefficients also. Significance levels produced by summary should be considered carefully. If there are many coefficients then the chance that one “significant” effect is observed is very high.

13. After modelling: linear models It is a good idea to plot data and fitted model, and differences between fitted and observed values on the same graph. For linear models with one predictor it can be done using: plot(weight,height) abline(lm1) segements(weight,fitted(lm1),weight,height) This plot already shows some systematic differences. It is an indication that model may need to be revised.

14. Checking validity of the model: standard tools Plotting fitted values vs residual, QQ plot and Cook’s distance can give some insight into model and how to improve it. Some of these plots can be done using plot(lm1)

15. Confidence and prediction bands Confidence bands are those where true line would belong with 1-α probability Prediction bands are bands where future observation would full with probability of 1-α: Prediction bands are wider than confidence bands.

16. Prediction and confidence bands lm1 = lm(height~weight) pp = predict(lm1,interval='p') pc = predict(lm1,interval='c') plot(weight,height,ylim=range(height,pp)) n1=order(weight) matlines(weight[n1],pp[n1,],lty=c(1,2,2),col='red') matlines(weight[n1],pc[n1,],lty=c(1,3,3),col='red’) These commands produce two sets of bands: narrow and wide. Narrow band corresponds to confidence bands and wide band is prediction band

17. Bootstrap Simplest application of bootstrap for this problem is as follows: 1)Calculate residuals using 2)Sample with replacement from the residual vector and denote them r random 3)Design new “observations” using 4)Estimate parameters 5)Repeat steps 2 3 and 4 6)Estimate bootstrap estimation, variances, covariance matrix or the distribution Another technique for bootstrapping is: Resample observations and corresponding row of the design matrix simultaneously - (y i,x 1i,x 2i,,,,x pi ),i=1,n. It meant to be less sensitive to misspecified models. Note that for some samples, the matrix may become singular and problem may become ill defined. If it happens then ridge regression or similar techniques need to be used

18. Bootstrap prediction lines Bootstrap could be used to plot distribution of fitted model. This function is on: http://www.ysbl.york.ac.uk/~garib/mres_course/2008/boot_lm_lines.r If we apply this function for the dataset women we will get the lines shown on the figure below. boot_lm(women,flm0,1000) Functions boot_lm and flm0 are available from the course’s website

19. Analysis of diagnostics Residuals and hat matrix: Residuals are differences between observation and fitted values: H is called a hat matrix. Diagonal terms h i are leverage of the observations. If these values are close to one then that fitted value is determined by this observation. Sometimes h i ’=h i /(1-h i ) is used to enhance high leverages. Q-Q plot can be used to check normality assumption. If assumption on distribution is correct then this plot should be nearly linear. If the distribution is normal then tests designed for normal distributions can be used. Otherwise bootstrap may be more useful to derive the desired distributions.

20. Analysis of diagnostics: Cont. Other analysis tools include: Where h i is leverage, h i ’ is enhanced leverage, s 2 is unbiased estimator of  2, s i 2 is unbiased estimator of  2 after removal of i-th observation

22. R-squared One of the statistics used for goodness of fit is R-squared. It is defined as: Adjusted R-squared: n - the number of observations, p - the number of parameters

23. Several potential problems with linear model and simple regression analysis 1)Interpretation Regression analysis can tell us that there is a relationship between input and output. It does not say one causes another 2)Outliers Use robust M-estimators 3)Singular or near singular cases Ridge regression or SVD. In underdetermined systems Partial least squares may be helpful 4)Distribution assumptions Generalised linear models Random or mixed effect models Data transformation 5)Non-linear model Box-Cox transformation Non-linear model fitting for example using R command nlm

24. References 1.Berthold, M. and Hand, DJ (2003) “Intelligent data analysis” 2.Stuart, A., Ord, JK, and Arnold, S. (1991) “Kendall’s advanced Theory of statistics. Volume 2A. Classical Inference and the Linear models” Arnold publisher, London, Sydney, Auckland 3.Dalgaard, P (2002) “Introductory statistics with R”, Springer Publishers