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fruitfly

fruitfly

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Author: Source: Unknown - Please cite: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Identifier attribute deleted. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! NAME: Sexual activity and the lifespan of male fruitflies TYPE: Designed (almost factorial) experiment SIZE: 125 observations, 5 variables DESCRIPTIVE ABSTRACT: A cost of increased reproduction in terms of reduced longevity has been shown for female fruitflies, but not for males. The flies used were an outbred stock. Sexual activity was manipulated by supplying individual males with one or eight receptive virgin females per day. The longevity of these males was compared with that of two control types. The first control consisted of two sets of individual males kept with one or eight newly inseminated females. Newly inseminated females will not usually remate for at least two days, and thus served as a control for any effect of competition with the male for food or space. The second control was a set of individual males kept with no females. There were 25 males in each of the five groups, which were treated identically in number of anaesthetizations (using CO2) and provision of fresh food medium. SOURCE: Figure 2 in the article "Sexual Activity and the Lifespan of Male Fruitflies" by Linda Partridge and Marion Farquhar. _Nature_, 294, 580-581, 1981. VARIABLE DESCRIPTIONS: Columns Variable Description ------- -------- ----------- 1- 2 ID Serial No. (1-25) within each group of 25 (the order in which data points were abstracted) 4 PARTNERS Number of companions (0, 1 or 8) 6 TYPE Type of companion 0: newly pregnant female 1: virgin female 9: not applicable (when PARTNERS=0) 8- 9 LONGEVITY Lifespan, in days 11-14 THORAX Length of thorax, in mm (x.xx) 16-17 SLEEP Percentage of each day spent sleeping SPECIAL NOTES: `Compliance' of the males in the two experimental groups was documented as follows: On two days per week throughout the life of each experimental male, the females that had been supplied as virgins to that male were kept and examined for fertile eggs. The insemination rate declined from approximately 7 females/day at age one week to just under 2/day at age eight weeks in the males supplied with eight virgin females per day, and from just under 1/day at age one week to approximately 0.6/day at age eight weeks in the males supplied with one virgin female per day. These `compliance' data were not supplied for individual males, but the authors say that "There were no significant differences between the individual males within each experimental group." STORY BEHIND THE DATA: James Hanley found this dataset in _Nature_ and was attracted by the way the raw data were presented in classical analysis of covariance style in Figure 2. He read the data points from the graphs and brought them to the attention of a colleague with whom he was teaching the applied statistics course. Dr. Liddell thought that with only three explanatory variables (THORAX, plus PARTNERS and TYPE to describe the five groups), it would not be challenging enough as a data-analysis project. He suggested adding another variable. James Hanley added SLEEP, a variable not mentioned in the published article. Teachers can contact us about the construction of this variable. (We prefer to divulge the details at the end of the data-analysis project.) Further discussion of the background and pedagogical use of this dataset can be found in Hanley (1983) and in Hanley and Shapiro (1994). To obtain the Hanley and Shapiro article, send the one-line e-mail message: send jse/v2n1/datasets.hanley to the address archive@jse.stat.ncsu.edu PEDAGOGICAL NOTES: This has been the most successful and the most memorable dataset we have used in an "applications of statistics" course, which we have taught for ten years. The most common analysis techniques have been analysis of variance, classical analysis of covariance, and multiple regression. Because the variable THORAX is so strong (it explains about 1/3 of the variance in LONGEVITY), it is important to consider it to increase the precision of between-group contrasts. When students first check and find that the distributions of thorax length, and in particular, the mean thorax length, are very similar in the different groups, many of them are willing to say (in epidemiological terminology) that THORAX is not a confounding variable, and that it can be omitted from the analysis. There is usually lively discussion about the primary contrast. The five groups and their special structure allow opportunities for students to understand and verbalize what we mean by the term "statistical interaction." There is also much debate as to whether one should take the SLEEP variable into account. Some students say that it is an `intermediate' variable. Some students formally test the mean level of SLEEP across groups, find one pair where there is a statistically significant difference, and want to treat it as a confounding variable. A few students muse about how it was measured. There is heteroscedasticity in the LONGEVITY variable. One very observant student (now a professor) argued that THORAX cannot be used as a predictor or explanatory variable for the LONGEVITY outcome since fruitflies who die young may not be fully grown, i.e., it is also an intermediate variable. One Ph.D. student who had studied entomology assured us that fruitflies do not grow longer after birth; therefore, the THORAX length is not time-dependent! Curiously, the dataset has seldom been analyzed using techniques from survival analysis. The fact that there are no censored observations is not really an excuse, and one could easily devise a way to introduce censoring of LONGEVITY. REFERENCES: Hanley, J. A. (1983), "Appropriate Uses of Multivariate Analysis," _Annual Review of Public Health_, 4, 155-180. Hanley, J. A., and Shapiro, S. H. (1994), "Sexual Activity and the Lifespan of Male Fruitflies: A Dataset That Gets Attention," _Journal of Statistics Education_, Volume 2, Number 1. SUBMITTED BY: James A. Hanley and Stanley H. Shapiro Department of Epidemiology and Biostatistics McGill University 1020 Pine Avenue West Montreal, Quebec, H3A 1A2 Canada tel: +1 (514) 398-6270 (JH) +1 (514) 398-6272 (SS) fax: +1 (514) 398-4503 INJH@musicb.mcgill.ca, StanS@epid.lan.mcgill.ca

5 features

class (target)numeric47 unique values
0 missing
PARTNERSnominal3 unique values
0 missing
TYPEnominal3 unique values
0 missing
THORAXnumeric46 unique values
0 missing
SLEEPnumeric14 unique values
0 missing
Show all 5 features

107 properties

NumberOfInstances
125
Number of instances (rows) of the dataset.
NumberOfFeatures
5
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
3
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
AutoCorrelation
-16.65
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.04
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
3.15
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
57.44
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
3
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.59
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
17.56
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.78
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
27.24
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
3
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.31
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
11.17
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.41
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.82
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
3
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.64
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.08
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
60
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
40
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.41
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.82
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.64
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.08
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.4
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
23.46
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0.01
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
15.88
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
3.15
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
57.44
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.59
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
17.56
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

18 tasks

Supervised Regression on fruitfly
4 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
Supervised Regression on fruitfly
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on fruitfly
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
Supervised Regression on fruitfly
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on fruitfly
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on fruitfly
0 runs - estimation_procedure: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on fruitfly
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class
Clustering on fruitfly
0 runs
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
Clustering on fruitfly
0 runs - estimation_procedure: 50 times Clustering
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