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Statistical Programs

Physical Address:
E. J. Iddings Agricultural Science Laboratory, Room 307
606 S Rayburn St

Mailing Address:
University of Idaho
875 Perimeter Drive MS 2337
Moscow, Idaho 83844-2337

Phone: 208-885-5930

Fax: 208-885-6654

Email: bprice@uidaho.edu

Web: uidaho.edu/cals/stats

Directions

Program Codes

A diagnostic analysis for exploring genotype-by-environment interactions which often are found in plant breeding trials. Example programs given for a national rapeseed trial. Codes written for the SAS programming language.

The model is capable of working on large scales and can be adjusted for various factors. Command line routines written in ANSI C and the Perl scripting language.

Estimation of doses in an unknown sample assuming the calibration curve known or estimated. Codes written in SAS.

Dose-response models are common in agriculture and biology. Dose-response curves are often used in bioassay to determine unknown dosage levels. The programs below illustrate the process of unknown dose estimation under two conditions:

  1. Calibration curve is known without error
  2. Calibration curve is estimated with known error

Examples are given for the logistic model:

y = 1/(1 + exp(-B*(dose - G)))

where y = the proportion of successes, B is a rate related parameter, G is the estimate of the 50th percentile and dose is the applied dose.

The Bayesian solution reparametrized the first form in terms of T1, the initial proportion of successes at dose = 0, and T2, the final proportion of successes at dose = maximum. Prior distributions for T1 and T2 are assumed uniform.

Source

Black Vine Weevil data (txt)

These programs compute Binomial, Bayesian and Bootstrap estimations of the omissional and commissiona error rates used in assessing the reliability of remotely sensed imagery. Command line routines written in ANSI C and the Perl scripting language.

Examples are given for Common Crupina, a weed found in the northwestern United States. Codes written for the SAS programming language.

Various estimation techniques: Probit, NLIN, GNLIM and Bayesian. Example programs given in SAS for egg hatch in black vine weevil. Codes written in SAS.

Dose-response models are common in agriculture and biology. The programs demonstrate various techniques of binary dose-response estimation within SAS. Examples are given for the logistic mModel:

y = 1/(1 + exp(-B*(dose - G)))

where y = the proportion of successes, B is a rate related parameter, G is the estimate of the 50th percentile and dose is the applied dose. The probit model analyzes a linearized form:

log(y/(1-y)) = A` - B*dose

Where A` is B*G from the previous form. The Bayesian solution reparametrized the first form in terms of T1, the initial proportion of successes at dose = 0, and T2, the final proportion of successes at dose = maximum. prior distributions for T1 and T2 are assumed uniform.

Source

  • Probit analysis (sas)
  • NLIN analysis (sas)
  • GNLIM analysis (sas)
  • Bayesian analysis (sas)
  • GNLIM treatment comparison analysis (sas)

Continuous Data

Poisson/Negative Binomial Count Data

Poisson/Negative Binomial Count Data — Variety Comparison

Binomial Data

Command line routines written in ANSI C and the Perl scripting language.

Simple exponential model (sas)

Segmented exponential model (sas)

Weibull model

Modified exponential model (sas)

Bayesian log-logistic model (sas)

Random effects model (sas)

Useful references

  • Bates, D.M., and D.G. Watts. 1988. Nonlinear Regression Analysis and its Applications. John Wliey and Sons. New York.
  • Finney, D. J. 1971. Probit Analysis. Cambridge University Press, London.
  • Ratkowsky, D. A. 1989. Handbook of Nonlinear Regression Models. Marcel Dekker, Inc. 241 pp.
  • SAS Inst. Inc. 2004. SAS OnlineDoc, Version 9, Cary, NC.

Requires no assumptions on sample sizes or parent distributions. Example programs given for a dairy cow study. Codes written in the ANSI C language under the GPL license.

Quadratic discriminant function which uses prior information for yellow starthistle from spatial landscape information. Provides a better classification of yellow starthistle than a uniform prior. Sample images and program given. Large images will require substantial computing resources. Codes written in the ANSI C language under the GPL license.

Contact

Statistical Programs

Physical Address:
E. J. Iddings Agricultural Science Laboratory, Room 307
606 S Rayburn St

Mailing Address:
University of Idaho
875 Perimeter Drive MS 2337
Moscow, Idaho 83844-2337

Phone: 208-885-5930

Fax: 208-885-6654

Email: bprice@uidaho.edu

Web: uidaho.edu/cals/stats

Directions