Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we...

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Predictive Models Lab RNR/Geog 420/520 Spring

description

Broad Model Types zDeductive models are based on reasoning in which the conclusion follows necessarily the presented premises zInductive models base validity on observations about part of a class as evidence for a proposition about the whole class

Transcript of Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we...

Page 1: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Predictive Models Lab

RNR/Geog 420/520Spring

Page 2: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Predictive ModelsImportant to understand what we are

attempting to predict These models predict location This prediction is based on reasoned or

measured relationshipsNo predictive model is perfectSome are more efficient than others

Page 3: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Broad Model TypesDeductive models are based on

reasoning in which the conclusion follows necessarily the presented premises

Inductive models base validity on observations about part of a class as evidence for a proposition about the whole class

Page 4: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Components of the Model

Variables --- DEM, relief, slope, shelter, ridge

Study Group --- 75 archaeological sites

Control Group --- 250 random point locations

Suitable Statistical model --- logistic regression

Page 5: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Model Assumptions

The Processes Ancient Humans Used to Select Site Locations Were Not Random

Part of the Site Selection Process Involved Selection for Favored Environmental Zones

Consequently, It Should be Possible to Identify Specific Environmental Signatures for Specific Groups of Archaeological Sites

Page 6: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Creating the Regression Models in GRID

Sample values of variables at site and non-site locations

Subject SAMPLE results to regression in GRID (with some cautions)

Results of the regression include coefficients and a constant, or y-intercept

Model made by multiplying variables by coefficients --- sum of these variables is the model

Model then scaled between 0 and 1 to create a probability surface

Page 7: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

SampleDep hdem hridge …1 840 149 0 852 1551 854 151 0 805 1341 853 062…

Page 8: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Logistic Regression in GRIDGrid: |> regression hsam.txt logistic brief <|coef # coef------ ---------------- 0 -3.797 1 -0.001 2 0.014 3 0.006 4 0.000 5 0.055------ ----------------RMS Error = 0.393Chi-Square = 51.608

Page 9: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Making Probability Models

X 0.097414 =

X 0.064497 =

X 0.001453 =

X -0.011092 =

X 0.007072 =

X 0.004799 =

X 0.014648 =

Relief

Shelter

Dist. ToSoil 1

Elevation

RidgeIndex

Aspect

Distanceto Wadi

EnvironmentalVariables

RegressionCoefficients

WeightedVariables

ProbabilityModel

Sum ofWeightedVariables

Corrected Y-Intercept

Page 10: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Probability Models

Group 1 Group 2

Page 11: Predictive Models Lab RNR/Geog 420/520 Spring. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location.

Model Strength

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

5

10

15

20

25

Perc

ent o

f Sam

ple

Probability Score

Umayyad Group 1

Non-Sites

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

10

20

30

40

50

Perc

ent o

f Sam

ple

Probability Score

Umayyad Group 2

Non-Sites

83.0100171

gain24.0

58441

gain

modelwithinphenomenatotalofpercentagemodel withinarea total ofpercentagegain 1


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