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Lecture 5: SLR Diagnostics (Continued) Correlation Introduction to Multiple Linear Regression BMTRY 701 Biostatistical Methods II

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Lecture 5: SLR Diagnostics (Continued) Correlation Introduction to Multiple Linear Regression. BMTRY 701 Biostatistical Methods II. From last lecture. What were the problems we diagnosed? We shouldn’t just give up! Some possible approaches for improvement - PowerPoint PPT Presentation

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• Lecture 5:SLR Diagnostics (Continued)CorrelationIntroduction to Multiple Linear RegressionBMTRY 701 Biostatistical Methods II

• From last lectureWhat were the problems we diagnosed?

We shouldnt just give up!

Some possible approaches for improvementremove the outliers: does the model change?

transform LOS: do we better adhere to model assumptions?

• Outlier QuandryTo remove or not to remove outliers

Are they real data?

If they are truly reflective of the data, then what does removing them imply?

Use caution!better to be true to the datahaving a perfect model should not be at the expense of using real data!

• Removing the outliers: How to?I am always reluctant.

my approach in this example:remove each separatelyremove both togethercompare each model with the model that includes outliers

How to decide: compare slope estimates.

• SENIC Data> par(mfrow=c(1,2))> hist(data\$LOS)> plot(data\$BEDS, data\$LOS)

• How to fit regression removing outlier(s)?> keep.remove.both
• Regression Fitting

reg

• How much do our inferences change?Why is 18 a bigger outlier than 20?

regremovebothremove 20remove 181 estimate0.004060.002990.003930.00314se(1)0.000860.000700.000730.00085

% change0(ref)26%3%23%

• Leverage and InfluenceLeverage is a function of the explanatory variable(s) alone and measures the potential for a data point to affect the model parameter estimates. Influence is a measure of how much a data point actually does affect the estimated model.Leverage and influence both may be defined in terms of matricesMore later in MLR (MPV ch. 6)

• Graphically

• R code

par(mfrow=c(1,1))plot(data\$BEDS, data\$LOS, pch=16)# old plain old regression modelabline(reg, lwd=2)# plot 20 to show which point we are removing, then# add regression linepoints(data\$BEDS[keep.remove.20==0], data\$LOS[keep.remove.20==0], col=2, cex=1.5, pch=16)abline(reg.remove.20, col=2, lwd=2)# plot 18 and then add regressionlinepoints(data\$BEDS[keep.remove.18==0], data\$LOS[keep.remove.18==0], col=4, cex=1.5, pch=16)abline(reg.remove.18, col=4, lwd=2)# add regression line where we removed both outliersabline(reg.remove.both, col=5, lwd=2)# add a legend to the plotlegend(1,19, c("reg","w/out 18","w/out 20","w/out both"), lwd=rep(2,4), lty=rep(1,4), col=c(1,2,4,5))

• What to do?Lets try something elseWhat was our other problem?heteroskedasticity (great wordtry that at scrabble)

non-normality of outliers

Common way to solve: transform the outcome

• Determining the TransformationBox-Cox transformation approach

Finds the best power transformation to achieve closest distribution to normality

Can apply toa variableto a linear regression modelWhen applied to a regression model, result tells you what is the best power transform of Y to achieve normal residuals

• Review of power transformationAssume we want to transform YBox-Cox considers Ya for all values of aSolution is the a that provides the most normal looking YaPractical powersa = 1: identitya = : square-roota = 0: loga = -1: 1/Y. usually we also take negative so that order is maintained (see example)Often not practical interpretation: Y-0.136

• Box-Cox for linear regressionlibrary(MASS)

bc

• Transformty
• New regression: transform is -1/LOSplot(data\$BEDS, ty, pch=16)reg.ty
• More interpretable?LOS is often analyzed in the literatureCommon transform is logit is well-known that LOS is skewed in most applicationsmost people take the logpeople are used to seeing and interpreting it on the log scaleHow good is our model if we just take the log?

• Regression with log(LOS)

• Lets compare: residual plots

• Lets compare: distribution of residuals

• Lets Compare: |Residuals|p=0.59p=0.12

• Lets Compare: QQ-plot

• R code

logy

• Regression results> summary(reg.ty)

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.169e-01 2.522e-03 -46.371 < 2e-16 ***data\$BEDS 3.953e-05 7.957e-06 4.968 2.47e-06 ***---

> summary(reg.logy)

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1512591 0.0251328 85.596 < 2e-16 ***data\$BEDS 0.0003921 0.0000793 4.944 2.74e-06 ***---

• Lets compare: results untransformed

• R code

par(mfrow=c(1,2))plot(data\$BEDS, data\$LOS, pch=16)abline(reg, lwd=2)lines(sort(data\$BEDS), -1/sort(reg.ty\$fitted.values),lwd=2, lty=2)lines(sort(data\$BEDS), exp(sort(reg.logy\$fitted.values)), lwd=2, lty=3)

plot(data\$BEDS, data\$LOS, pch=16, ylim=c(7,12))abline(reg, lwd=2)lines(sort(data\$BEDS), -1/sort(reg.ty\$fitted.values),lwd=2, lty=2)lines(sort(data\$BEDS), exp(sort(reg.logy\$fitted.values)), lwd=2, lty=3)

• So, what to do?What are the pros and cons of each transform?

Should we transform at all?!

• Switching Gears: CorrelationPearson CorrelationMeasures linear association between two variablesA natural by-product of linear regressionNotation: r or (rho)

• Correlation versus slope?Measure different aspects of the association between X and Y

Slope: measures if there is a linear trendCorrelation: provides measure of how close the datapoints fall to the line

Statistical significance is IDENTICALp-value for testing that correlation is 0 is the SAME as the p-value for testing that the slope is 0.

• Example: Same slope, different correlationr = 0.46, b1=2r = 0.95, b1=2

• Example: Same correlation, different sloper = 0.46, b1=4r = 0.46, b1=2

• Correlation Scaled version of Covariance between X and YRecall Covariance:

Estimating the Covariance:

• Correlation

• InterpretationCorrelation tells how closely two variables track one anotherProvides information about ability to predict Y from XRegression output:look for R2for SLR, sqrt(R2) = correlationCan have low correlation yet significant associationWith correlation, 95% confidence interval is helpful

• LOS ~ BEDS> summary(lm(data\$LOS ~ data\$BEDS))

Call:lm(formula = data\$LOS ~ data\$BEDS)

Residuals: Min 1Q Median 3Q Max -2.8291 -1.0028 -0.1302 0.6782 9.6933

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 8.6253643 0.2720589 31.704 < 2e-16 ***data\$BEDS 0.0040566 0.0008584 4.726 6.77e-06 ***---Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1

Residual standard error: 1.752 on 111 degrees of freedomMultiple R-squared: 0.1675, Adjusted R-squared: 0.16 F-statistic: 22.33 on 1 and 111 DF, p-value: 6.765e-06

• 95% Confidence Interval for CorrelationThe computation of a confidence interval on the population value of Pearson's correlation () is complicated by the fact that the sampling distribution of r is not normally distributed. The solution lies with Fisher's z' transformation described in the section on the sampling distribution of Pearson's r. The steps in computing a confidence interval for are:Convert r to z' Compute a confidence interval in terms of z' Convert the confidence interval back to r. freeware!http://www.danielsoper.com/statcalc/calc28.aspxhttp://glass.ed.asu.edu/stats/analysis/rci.htmlhttp://faculty.vassar.edu/lowry/rho.html

• log(LOS) ~ BEDS> summary(lm(log(data\$LOS) ~ data\$BEDS))

Call:lm(formula = log(data\$LOS) ~ data\$BEDS)

Residuals: Min 1Q Median 3Q Max -0.296328 -0.106103 -0.005296 0.084177 0.702262

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1512591 0.0251328 85.596 < 2e-16 ***data\$BEDS 0.0003921 0.0000793 4.944 2.74e-06 ***---Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1

Residual standard error: 0.1618 on 111 degrees of freedomMultiple R-squared: 0.1805, Adjusted R-squared: 0.1731 F-statistic: 24.44 on 1 and 111 DF, p-value: 2.737e-06

• Multiple Linear RegressionMost regression applications include more than one covariateAllows us to make inferences about the relationship between two variables (X and Y) adjusting for other variablesUsed to account for confounding.Especially important in observational studiessmoking and lung cancerwe know people who smoke tend to expose themselves to other risks and harmsif we didnt adjust, we would overestimate the effect of smoking on the risk of lung cancer.

• Importance of including important covariatesIf you leave out relevant covariates, your estimate of 1 will be biased

How biased?

Assume: true model:

fitted model:

• Fun derivation

• Fun derivation

• Fun derivation

• ImplicationsThe bias is a function of the correlation between the two covariates, X1 and X2If the correlation is high, the bias will be highIf the correlation is low, the bias may be quite small.If there is no correlation between X1 and X2, then omitting X2 does not bias inferencesHowever, it is not a good model for prediction if X2 is related to Y

• Example: LOS ~ BEDS analysis.> cor(cbind(data\$BEDS, data\$NURSE, data\$LOS)) [,1] [,2] [,3][1,] 1.0000000 0.9155042 0.4092652[2,] 0.9155042 1.0000000 0.3403671[3,] 0.4092652 0.3403671 1.0000000

• R code

reg.beds

• SLRs

Call:lm(formula = log(data\$LOS) ~ data\$BEDS)

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1512591 0.0251328 85.596 < 2e-16 ***data\$BEDS 0.0003921 0.0000793 4.944 2.74e-06 ***---

Call:lm(formula = log(data\$LOS) ~ data\$NURSE)

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1682138 0.0250054 86.710 < 2e-16 ***data\$NURSE 0.0004728 0.0001127 4.195 5.51e-05 ***---

• BEDS + NURSE> summary(reg.beds.nurse)

Call:lm(formula = log(data\$LOS) ~ data\$BEDS + data\$NURSE)

Residuals: Min 1Q Median 3Q Max -0.291537 -0.108447 -0.006711 0.087594 0.696747

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1522361 0.0252758 85.150