Transcript Logistic Regression - Michael Kalsher Home

Logistic
Regression
Adv. Experimental
Methods & Statistics
PSYC 4310 / COGS 6310
Michael J. Kalsher
Department of
Cognitive Science
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Outline
• Logistic regression: When and why
- Binary
- Multinomial
• Theory behind logistic regression
– Assessing the model
– Assessing predictors
– Things that can go wrong
• Interpreting logistic regression
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Using Logistic Regression:
When and Why?
• To predict an outcome variable that is
categorical from one or more categorical
or continuous predictor variables.
• Used because having a categorical
outcome variable violates the
assumption of linearity in normal
regression.
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Examples of its Use
• Medical Research
-
Using a database of patient information to predict the
malignancy of a tumor
If the predicted probability of malignancy for a tumor
detected in a patient is low, then the physician may decide
not to carry out expensive and painful surgery.
• Levels of Analysis
-
Predicting membership of only two categorical outcomes =
binary logistic regression.
Predicting membership of more than two categories =
multinomial regression.
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Principles Behind Logistic
Regression
• In simple linear regression we saw that the
outcome variable is predicted from the
equation: Yi = b0 + b1X1i + b2X2i + bnXni +εi
•
An assumption of the linear model is that the
relationship between variables is linear.
•
When the outcome variable is categorical, this
assumption is violated.
•
One way around this problem is to transform the data using
a logarithmic transformation that expresses the non-linear
relationship in a linear way.
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Expressing the regression
equation logarithmically: The Logit
•
Logistic regression expresses the multiple linear
regression equation in logarithmic terms (the
logit), thereby overcoming the problem of violating
the linearity assumption.
•
Instead of predicting the value of the outcome
variable (“Y”) from one or more predictor variables
(“X’s”), we predict the probability of the outcome
occurring, given known values of the predictors.
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The Logistic Regression Equation:
One Predictor
• Outcome
– We predict the probability of the outcome occurring
P(Y ) = 1+e-( b0+b1X1i )
1
• b0 and b1
•
•
•
P(Y) = probability of Y
e = the base of natural
logarithms
The other coefficients form a
linear combination much the
same as in simple regression
– Can be thought of in much the same way as multiple
regression
– Note the normal regression equation forms part of
the logistic regression equation
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The Logistic Regression Equation:
Several Predictors
• Outcome
– The equation expands to accommodate additional
predictors
– We still predict the probability of the outcome occurring
– “0” (Y very unlikely to have occurred) to “1” (Y very
likely to have occurred)
P(Y ) = 1+e-(b0+b1X1i+b2X2 i+...+bnXni )
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Estimating Parameters: Linear vs.
Logistic Regression
• In linear regression, coefficients (parameters) are
estimated using the least squares method.
• In logistic regression, maximum-likelihood
estimation is used.
– Selects coefficients that make the observed values
most likely to have occurred.
– The chosen estimates of the bs will be ones that, when
values of the predictor variables are placed in it, result
in values of Y closest to the observed values.
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Assessing the Model: the Loglikelihood statistic
Multiple regression: To assess how well our
model fits the data, we compare the observed and
predicted values to compute R2.
Note: Remember that R2 is the squared Pearson correlation between observed
values of the outcome and the values predicted by the regression model.
Logistic regression: Here the assessment of
model fit is based on summing the probabilities
associated with the predicted and actual outcomes.
The measure is termed the log-likelihood statistic.
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Assessing the Model: the Loglikelihood statistic
– Is analogous to the residual sum of squares in
multiple regression, in that it is an indicator of how
much unexplained information there is after the
model has been fitted.
– Large values indicate poorly fitting statistical models.
log  likelihood
N
 Y lnPY   1  Y ln1  PY 
i
i1
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i
i
Assessing the Model: the deviance
statistic
– The deviance, referred to as “-2LL”, is closely related
to log-likelihood:
Deviance = -2 x log-likelihood
– Has a chi-square distribution, so is easy to calculate
the significance of the value.
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The logic behind the deviance
statistic
– We’ve seen that it is useful to compare a model
against some baseline state.
• In multiple regression, the most basic model was the “mean”.
– With a categorical outcome it makes no sense to use
the mean. All we know is whether an event happened
or not.
• In logistic regression, the baseline model is the value of the
outcome that occurs most often.
• Note: This is the logistic regression model when only the
constant is included.
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The logic behind the deviance
statistic
– If we add one or more predictors to the model, we can
compute the improvement of the model as follows:
– This difference is termed a likelihood ratio.
– We can build up models hierarchically and make
comparisons at each step.
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Assessing the model:
The R-statistic
– is the partial correlation between the outcome
variable and each of the predictor variables.
– Varies between −1 and 1.
– If a variable has a small value of R it means
that it contributes a small amount to the model.
• Positive values
– As the predictor variable increases, likelihood of the event
occurring increases.
• Negative values
– As the predictor variable increases, the likelihood of the
outcome occurring decreases.
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Assessing the model:
Calculating the R-statistic
Where:
-2LL = the deviance for the original model
z = the Wald statistic
Note: The Wald statistic can be inaccurate under certain
conditions, so the value of R should be treated with caution!
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Analogues to
2
R:
Hosmer & Lemeshow, 1989
Hosmer and Lemeshow’s to R2L
Represents the proportional reduction in the absolute
value of the log-likelihood measure.
Measures how much the “badness of fit” improves as a
result of the inclusion of the predictor variables.
0 = predictors are useless; 1 = model predicts the outcome variable perfectly.
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Analogues to
2
R:
Cox & Snell, 1989
Cox and Snell’s R2C
R2CS = 1 - exp
(
(-2LL(new) – (-2LL (baseline)
n
Note: This is what SPSS reports.
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The Wald Statistic: Assessing the
contributions of the predictors
Wald 
b
SE b
• Similar to t-statistic in Regression.
• Tests the null hypothesis that b = 0.
• Biased when b is large (because it inflates the standard
error and increases the chances of a Type II error).
• Alternative is to enter predictors hierarchically
and look at Likelihood-ratio statistics.
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The odds ratio: exp(B)
Odds after a unit change in the predictor
Odds ratio =
Original odds
• Crucial to interpretation of logistic regression.
• Indicates the change in odds resulting from a
unit change in the predictor.
– Odds Ratio > 1 means that as the predictor increases, the odds
of the outcome occurring increase.
– Odds Ratio < 1 means that as the predictor increases, the odds
of the outcome occurring decrease.
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Methods of Entry
• Forced Entry: All variables entered
simultaneously.
• Hierarchical: Variables entered in blocks.
– Blocks should be based on past research, or theory
being tested. Good Method.
• Stepwise: Variables entered on the basis of
statistical criteria (i.e. relative contribution to
predicting outcome).
– Should be used only for exploratory analysis.
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Modeling Building: Parsimony is best
• Build an initial model that includes all
potential predictors, then systematically
remove any that don’t seem to
contribute to the model.
– predictors should not be included unless
they have explanatory benefit.
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Sources of Bias: Assumptions
– Linearity
• assumes there is a linear relationship between
any continuous predictors and the logit of the
outcome variable.
• Tested by looking at the interaction term between
the predictor and its log transformation.
– Independence of Errors
• Violating this assumption produces
overidispersion
– Multicollinearity
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Unique Problems
• Incomplete Information
• Complete Separation
• Overdispersion
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Unique Problems: Incomplete
information regarding the predictors
• Categorical Predictors:
– Predicting cancer from smoking and eating tomatoes.
– We don’t know what happens when nonsmokers eat
tomatoes because we have no data in this cell of the
design.
• Continuous variables
– Will your sample contain one, or a small number of
subjects who possess unique characteristics?
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Unique Problems:
Complete Separation
• Occurs when the outcome variable can be perfectly
predicted.
– Can occur, for example, when too many variables are
fitted to too few cases.
• Example: predicting whether a person is a burglar
based on their weight.
- Example 1: Is it a burglar? Or is it your teenage son/one of
his friends?
- Example 2: Is it a burglar or your cat?
1 = definitely a burglar; 0 = definitely not a burglar
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Complete Separation
1.0
1.0
0.8
0.8
Probability of Outcome
Probability of Outcome
Unique Problems:
0.6
0.4
0.2
0.0
0.6
0.4
0.2
0.0
20
30
40
50
60
70
80
90
0
Weight (KG)
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Weight (KG)
Relationship between weight (x-axis)
and a dichotomous outcome
variable.
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An example of complete separation.
The weights of the two categories do
not overlap.
© 2011, Michael Kalsher
Unique Problems:
Overdispersion
• Occurs when the variance is larger than
expected from the model.
• Can be caused by violating the independence
assumption.
• Tends to result in artificially small standard
errors, creating the following problems:
– Test statistic will be falsely deemed significant. Recall that
statistical tests of regression coefficients are computed by dividing
by the standard error.
– Overconfidence in the relationship between predictors and
the outcome in the population. Recall that confidence intervals
are computed from the standard error.
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A Sample Problem:
•
•
•
•
Andy Field’s NOT Kalsher’s
Predictors of a treatment intervention.
Participants: 113 adults with a “medical” problem
Outcome: Cured (1) or not cured (0).
Predictors:
– Intervention: intervention or no treatment.
– Duration: the number of days before treatment that
the patient had the problem.
• In SPSS:
– Outcome = Categorical variable (cured/not cured)
– Intervention = Categorical variable (intervention/no treatment
– Duration = Continuous variable (# days person had the problem)
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Building the Model: Hierarchical
Approach
We’re most interested in
whether the intervention
has an effect.
Ultimately, though, we
want to know which
model best fits the data.
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Logistic Regression: The General Procedure
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Specifying models using the
Logistic Regression dialog box
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We have only one
categorical predictor
variable.
“Indicator” means that
standard dummy variable
coding will be used.
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Change “Last” to “First”,
then click on “Change”,
then click on “continue”.
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Initial Output:
Remember the coding
scheme!
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Output:
Overall Model Summary Statistics
Represents the difference between
Model 1 and the intercept (no
predictors).
Here, we’re interested in the
improvement of Model 2 over Model 1
(given by the chi-square for “Block”).
Obtained by taking the difference
between the model chi-square for the
two models (9.928 – 9.926 = 0.002,
which is non-significant (.964).
Ditto for Model 3.
Conclusion: Duration and Duration x
Intervention interaction add nothing to
the model. Proceed with Model 1.
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Back to Model 1:
Some Options
Bootstrapping allows us
to estimate the properties
of the sampling distribution
from the sample data. We
can use this procedure to
establish 95% confidence
intervals for the parameter
being estimated and also
calculation the std.dev. Of
the parameter estimates
and use this as the
standard error.
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Back to Model 1:
Some Options
Predicted Values options are
unique to logistic regression.
Predicted Probabilities
Probabilities of Y occurring,
given the values of each
predictor for a given case.
Predicted group membership
Tells us which of the two
outcome categories a
participant is most likely to
belong to based on the model.
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Back to Model 1:
Some Options
Histograms of the actual and
predicted values of the
outcome variable, useful for
assessing the fit of the model
to the observed data.
Confidence interval
for the odds ratio.
Assesses how well the
chosen model fits the data.
Cases for which the
standardized residuals are
larger than the level specified
(here, 2 standard deviations.
Iteration history is the only
way to get SPSS to display
the initial -2LL and we need
this value to compute R.
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Interpreting Logistic Regression
Summary Statistics for the Model
Represents the difference between Model 1 (i.e.,
presence/absence of the intervention) and the intercept (no
predictors).
The model is highly significant.
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Interpreting Logistic Regression
Classification Table
The Model uses whether a patient had an intervention, or not, to
predict whether they were cured or not.
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Predicts that the 57 people who received the intervention will
be cured.
Predicts that the 56 people who did not receive the
intervention will not be cured.
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Interpreting Logistic Regression
Classification Table
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The Model
-Correctly classifies 32 patients who were not cured, but misclassifies
16 others (correctly classifies 66.7% ).
-Correctly classifies 41 patients who were cured, but misclassifies 24
others (correctly classifies 63.1%).
-The overall “weighted” accuracy is 64.6%.
-With only the constant is included, accuracy is 57.5% (cured/total)
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Interpreting Logistic Regression
Model Summary
• Wald Statistic tells us whether the b coefficient for the predictor is
significantly different from zero (and significantly predicting the outcome).
• Interpretation of the coefficients: “The change in the logit of the
outcome associated with a one-unit change in the predictor.”
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The odds ratio: Exp(B)
Odds after a unit change in the predictor
Odds ratio =
Original odds
• Indicates the change in odds resulting from a
unit change in the predictor.
– Odds Ratio greater than 1 means that as the predictor increases,
the odds of the outcome occurring increase.
– Odds Ratio less than 1 means that as the predictor increases,
the odds of the outcome occurring decrease.
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ncluded, and block 1 describes the model after Intervention is included. As such, block 1
s the main bit in which we’re interested. The bit of the block 0 output that does come in
useful is in Output 19.3, and will be there only if you selected Iteration history in Figure
19.10. This table tells us the initial 2LL, which is 154.084. We’ll use this value later so
Output:
Block 0
don’t forget
it.
Interpreting Logistic Regression
OU
Note: We can use the -2LL value (154.084) to calculate R
19.6.2. Model summary ➁
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Interpreting Logistic Regression
Effect Size
Where:
• -2LL = the deviance for the
original model
• z = the Wald statistic
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Hosmer and Lemeshow’s
Measure (1989)
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Interpreting Logistic Regression
Effect Size
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Summary
• The overall fit of the final model is shown by −2LL and its
associated chi-square statistic.
– If the significance of the chi-square statistic is less than .05,
then the model is a significant fit of the data.
• Check the table labelled Variables in the equation to see the
regression parameters for any predictors in the model.
• Look at the Wald statistic and its significance.
• Use the odds ratio, Exp(B), for interpretation.
– OR > 1, then as the predictor increases, the odds of the
outcome occurring increase.
– OR < 1, then as the predictor increases, the odds of the
outcome occurring decrease.
– The confidence interval of Exp(B) should not cross 1.
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Reporting the Analysis
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Example: Factors predicting successful soccer penalty
kicks
• Outcome: Perfect for logistic regression, since the
outcome is a dichotomy: Whether the penalty kick
was successful or not.
• Predictors:
– Previous research suggests two factors:
• Whether the kicker is a worrier (measured by the Penn State
Worry Questionnaire – PSWQ)
• Past success at scoring penalty kicks
– State Anxiety may also play a role.
• SPSS:
–
–
–
–
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Scored: 0 = penalty missed; 1 = penalty scored
PSWQ: Measure of the degree to which a player worries.
Anxious: Measure of state anxiety before a penalty kick.
Previous: Percentage of previous penalty kicks made.
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Testing for linearity of the logit
In this example, we have three continuous variables (PASQ, Anxious, Previous)
We need to check that each one is linearly related to the log of the outcome
variable (Scored).Run the logistic regression, but include predictors that are the
interaction between each predictor and the log of itself.
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1. For each predictor, create a new variable that is the log of
the original variable.
Example: For PSWQ, create a new variable called LnPSWQ
by entering this name into the Target Variable box and then
“click” on “Type and Label …”
2. When the “Type and
Label” dialogue box
appears, you may choose
to give each predictor the
same, or a different, name
(e.g., Ln(PSWQ). Then
click “Continue”
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3. Replace the “?” with PSWQ by selecting it from the variable list at
left and clicking the blue horizontal arrow. Click “OK” and then
repeat for each of the other continuous variables (Anxiety, Previous)
1. In the “Function group”
box, click on “Arithmetic”,
2. In the “Functions and Special
Variables” box click on “Ln” and
transfer it to the command area
using the blue “up” arrow.
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Rerun the analysis, as before, except …
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… force all three variables (PSWQ, Anxiety, Previous)
into the “Covariates” box, along with …
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Use the >a*b> button to move the
interaction terms: PSWQ &
LnPSWQ; Anxiety & LnAnxiety;
Previous & LnPrevious into the
Covariates box.
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Any interaction that is significant indicates the main effect
has violated the assumption of linearity of the logit. The
results show that the assumption has been met.
Note: multicollinearity tests (tolerance, VIF) must be tested by running a linear
regression analysis using the same outcome and predictor variables.
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Multinomial logistic regression
• Used to predict membership of more than two
categories.
• Analysis breaks the outcome variable down into a
series of two-category comparisons.
– Say you have three outcome categories: A, B and C.
Analysis will consist of two comparisons of your choosing:
• Compare everything against the 1st category (A vs. B & A vs. C)
• Or your last category (A vs. C and B vs. C)
• Or a custom category (B vs. A and B vs. C)
• The important parts of the analysis and output are
much the same as for binary logistic regression
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Example: How successful are chat lines?
• The chat lines used by 348 men and 672 women in a nightclub were recorded.
• Outcome: Whether the chat line resulted in one of the
following three events:
– The person got no response or the recipient walked away.
– The person got the recipient’s phone number.
– The person left the night-club with the recipient.
• Predictors:
– The content of the chat lines were rated for:
• Funniness (0 = not at all funny, 10 = funniest thing I have ever heard)
• Sexuality (0 =no sexual content; 10 = very sexually direct)
• Moral values (0 = reflects poor moral values; 10 = high moral values).
– Gender of recipient (0 = female; 1 = male)
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Success is the outcome variable and is comprised of three categories,
or levels. Click on “Reference Category” to select the baseline
category. Here, the first category (complete rejection) seems like a
good choice. Change the default (“Last Category”) to “First Category”.
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The “Reference Category” has been changed to the First Category
(complete rejection)
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Next, click on “Model”
Categorical Predictors go here
Continuous Predictors go here
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So we specify a
custom model.
By default, SPSS examines the main effects of
the predictor variables. But the most interesting
information often comes from the interactions.
For example, funny chat lines may be more
effective with women than with men (Gender x
Funny Interaction).
Similarly chat lines with a high sexual content
may be more effective when used on men, but
not women (Sex x Gender Interaction).
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Highlight all of the predictors.
Use the drop-down list to select “Main Effects”
Then move the predictors to the “Forced Entry
Terms” box.
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Select each desired
interaction term and
move them to the
“Stepwise Terms” box
as shown here.
Use the “Forward
entry” Stepwise
method
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Produces Cox-Snell and
Negelkerke R2 statistics
(effect size estimates).
Produces table of
observed and expected
frequencies.
Produces table summarizing
the predictors entered/removed
at each step.
Produces Pearson and
likelihood ratio chisquare statistics for the
model.
Produces table comparing the
model(s) to the baseline model.
The model overall is tested
using likelihood ratio statistics,
but this option computes the
same test for individual effects
in the model.
Produces the beta values, test
statistics and confidence
intervals for predictors in the
model—very important!
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Criteria options: Leave the defaults in
place unless you receive an error
message: “failing to converge”, then
increase maximum interactions. But
may not solve the problem.
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Options: The Scale drop-down box can be
useful if overdispersion is a problem since it
reduces the standard errors used to test the
significance and construct the confidence
intervals of the parameter estimates. Use the
deviance or Pearson options.
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Save: Can save predicted probabilities and predicted group
membership (same as in binary logistic regression, except they
are called Estimated response probabilities and Predicted
category.
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Indicates a significant decrease in
unexplained variance from the baseline
model to the final model. added.
Because we requested a stepwise analysis for our interaction terms, we get the “Step
Summary” summarizing the steps in the analysis. After the main effects were entered
(Model 0), the Gender x Funny and the Gender x Sex interactions were entered stepwise
and contributed significantly.
Note that the AIC (Akaike’s information criterion) and BIC (Schwarz’s Bayesian
information criterion) get smaller as the terms are added, evidence that the model is a
better fit as these terms are added.
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From the model summary, we know that the model is significantly better than no model,
but is it a good fit to the data? The Pearson and Deviance statistics test whether the
predicted values from the model differ significantly from the observed values. If these
statistics are not significant then the model is a good fit. Here, we have conflicting
results.
One possibility is that the Pearson statistic is inflated by many empty cells. Another
possibility is overdispersion. Overdispersion is present if the ratio of the Goodness-of-Fit
statistic to its degrees of freedom is greater than 1 (the disperson parameter) and is
problematic if greater than 2. Neither value is particularly high.
Φpearson = Χ2Pearson = 886.62 = 1.44
df
614
ΦDeviance = Χ2Deviance = 617.48 = 1.01
df
614
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Likelihood Ratio Tests
Tell us which predictors significantly predict the outcome category
Effects on the success rates of chat lines:
Main effects:
Interaction effects
Good-Mate: Χ2(2) = 6.32, p = .04
Gender x Funny: Χ2(2) = 35.81, p < .001
Gender: Χ2(2) = 18.54, p = .04
Gender x Sex: Χ2(2) = 13.45, p = .001
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Parameter Estimates
More detailed information concerning the effects
Compares “Got
Phone Number”
against “Rejection”
Compares “Goes
Home with Person”
against “Rejection”
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Individual Effects: A few examples
Good-Mate: Whether the chat line showed good morals significantly predicted whether
you get a phone number or rejected, b = 0.13, Wald Χ2(1) = 6.02, p = .014 The odds
ratio tells us that as this variable increases by one unit, the change in the odds of
getting a phone number is 1.14 (i.e., more likely to get a phone number if you use a
chat line that demonstrates good morals).
Funny x Gender: The success of funny chat lines depended on whether they were
delivered to a man or a woman,, b = 0.49, Wald Χ2(1) = 12.37, p <.001 The odds ratio
tells us that as gender changes from female (0) to male (1), in combination with
“funniness” increasing, the change in odds of giving out a phone number compared to
not is 1.64. As funniness increases, women become more likely to give out their phone
number than men.
Sex x Gender: The success of chat lines with sexual content depended on whether
they delivered to a man or a woman, b = -0.35, Wald Χ2(1) = 10.82, p =.001 The odds
ratio tells us that as gender changes from female (0) to male (1), in combination with
“sexual content” increasing, the change in odds of giving out a phone number
compared to not is 0.71. As the sexual content of a chat line increases, women
become less likely than men to give out their phone number.
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Reporting the Results
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