Mix Model Analysis

PLSC 795 LABORATORY OF FIELD DESIGN I

Dr. Richard HorsleyDr. Ana Maria Heilman

CONTENT OUTLINE MIXED MODEL ANALYSIS AND APLICATIONS

RANDOM FACTORS

EXAMPLE DATA: RCBD FACTORIAL

VISUALIZATIONS

ANALYZE RELATIONS

Eateria 27 | Brand Guidelines 02

RANDOM FACTORS

Mixed model analysis is appropriate when one or more of our factors are considered random

JMP.com

PLSC 795 | Laboratory of Field Design I 03

A factor is considered random if the levels of the factor are randomly selected from a large population

JMP.com

RCBD FACTORIAL ARRANGEMENT: NITROGEN LEVELS AND BARLEY VARIETIES

In this example dataset, factor A has four Nitrogen levels and factor B has 6 levels equivalent to six barley varieties.

How do we determine which factor is fix or random?

A = Fixed EffectB = Random Effect

VISUALIZE YOUR DATA -DISTRIBUTIONS

VISUALIZE YOUR DATA -GRAPH BUILDER

ANALYZE RELATIONSHIPS -FIT MODEL

• Histograms - "Used for continuous variables. Useful for understanding the distribution of your data.⚬ Use a histogram find the following information in your data:￭ Average Value￭ Variation￭ Extreme values"

• Bar Charts - "Used for categorical variables. A bar chart looks similar to a histogram, but in this case a bar chart shows a bar for every level of the variable, while the histogram shows a range of the values for the variable being analyzed." - jmp.com

"Use the graph Builder to create and modify your graphs. Change the variables by dragging and dropping them in and out of the graph." - jmp.com

"Scatterplots and other graphs can help you visualize relationships between variables. The next step is to analyze those relationships so that you can describe them numerically." - jmp.com

04PLSC 795 | Laboratory of Field Design I

RCBD FACTORIAL ARRANGEMENT: NITROGEN LEVELS AND BARLEY VARIETIES

In this example dataset, factor A has four Nitrogen levels and factor B has 6 levels or varieties.

How do we determine which factor is fix or random?

A = Fixed EffectB = Random Effect

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ANALYZE RELATIONSHIPS MODEL = NUMERICAL DESCRIPTION OF THE RELATIONSHIP BETWEEN VARIABLES

PREDICTORS AND RESPONSE VARIABLES

A model predicts the average value of one variable (Y) from the value of another variable (X). The X variable is called a predictor.

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1) Response variables

2) Predictor variables

A = FIXED EFFECTB = RANDOM EFFECT

AXB = INTERACTION IS RANDOM

PLSC 795 | Laboratory of Field Design I