strip-plot designs
DESCRIPTION
Strip-Plot Designs. Sometimes called split-block design For experiments involving factors that are difficult to apply to small plots Three sizes of plots so there are three experimental errors The interaction is measured with greater precision than the main effects. S3 S1 S2. - PowerPoint PPT PresentationTRANSCRIPT
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Strip-Plot Designs Sometimes called split-block design For experiments involving factors that are
difficult to apply to small plots Three sizes of plots so there are three
experimental errors The interaction is measured with greater
precision than the main effects
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For example: Three seed-bed preparation methods Four nitrogen levels Both factors will be applied with large scale machinery
S3 S1 S2
N1
N2
N0
N3
S1 S3 S2
N2
N3
N1
N0
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Advantages --- Disadvantages
Advantages– Permits efficient application of factors that would be
difficult to apply to small plots
Disadvantages– Differential precision in the estimation of interaction
and the main effects– Complicated statistical analysis
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Strip-Plot Analysis of Variance
Source df SS MS FTotal rab-1 SSTotBlock r-1 SSR MSR A a-1 SSA MSA FA
Error(a) (r-1)(a-1) SSEA MSEA Factor A error B b-1 SSB MSB FB
Error(b) (r-1)(b-1) SSEB MSEB Factor B error AB (a-1)(b-1) SSAB MSAB FAB
Error(ab) (r-1)(a-1)(b-1) SSEAB MSEAB Subplot error
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Computations
SSTot
SSR
SSA
SSEA
SSB
SSEB
SSAB
SSEAB SSTot-SSR-SSA-SSEA-SSB-SSEB-SSAB
There are three error terms - one for each main plot and interaction plot
2i j k ijkY Y
2..kkab Y Y
2i..irb Y Y
2i.ki kb Y Y SSA SSR
2. j.jra Y Y
2. jkj ka Y Y SSB SSR
2ij.i jr Y Y SSA SSB
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F Ratios F ratios are computed somewhat differently
because there are three errors
FA = MSA/MSEA tests the sig. of the A main effect
FB = MSB/MSEB tests the sig. of the B main effect
FAB = MSAB/MSEAB tests the sig. of the AB interaction
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Standard Errors of Treatment Means
AMSErb
BMSEra
ABMSEr
Factor A Means
Factor B Means
Treatment AB Means
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SE of Differences for Main Effects
Differences between 2 A means
with (r-1)(a-1) df
Differences between 2 B means with (r-1)(b-1) df
2 A* MSErb
2 B* MSEra
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SE of Differences Differences between A means at same level of B
Difference between B means at same level of A
Difference between A and B means at diff. levels
For sed that are calculated from >1 MSE, t tests and df are approximated
2 1 AB A* b MSE MSErb
2 1 AB B* a MSE MSEra
2 AB A B* ab a b MSE a * MSE b * MSErab
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Interpretation
Much the same as a two-factor factorial: First test the AB interaction
– If it is significant, the main effects have no meaning even if they test significant
– Summarize in a two-way table of AB means
If AB interaction is not significant– Look at the significance of the main effects– Summarize in one-way tables of means for factors
with significant main effects
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Numerical Example
A pasture specialist wanted to determine the effect of phosphorus and potash fertilizers on the dry matter production of barley to be used as a forage– Potash: K1=none, K2=25kg/ha, K3=50kg/ha– Phosphorus: P1=25kg/ha, P2=50kg/ha– Three blocks– Farm scale fertilization equipment
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K3 K1 K2
K1 K3 K2
K2 K1 K3
P1
P2
P2
P1
P2
P1
56 32 49
67 54 58
38 62 50
52 72 64
54 44 51
63 54 68
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Raw data - dry matter yields
Treatment I II IIIP1K1 32 52 54P1K2 49 64 63P1K3 56 72 68P2K1 54 38 44P2K2 58 50 54P2K3 67 62 51
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Construct two-way tables
K I II III Mean1 43.0 45.0 49.045.672 53.5 57.0 58.556.333 61.5 67.0 59.562.67Mean 52.67 56.33 55.6754.89
Potash x BlockP I II III Mean1 45.67 62.67 61.6756.672 59.67 50.00 49.6753.11Mean 52.67 56.33 55.6754.89
Phosphorus x Block
P K1 K2 K3 Mean1 46.00 58.67 65.33 56.672 45.33 54.00 60.00 53.11Mean 45.67 56.33 62.67 54.89
Potash x Phosphorus
SSEA =2*devsq(range) – SSR – SSA
SSR=6*devsq(range)
SSA=6*devsq(range)Main effect of Potash
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Construct two-way tables
K I II III Mean1 43.0 45.0 49.045.672 53.5 57.0 58.556.333 61.5 67.0 59.562.67Mean 52.67 56.33 55.6754.89
P I II III Mean1 45.67 62.67 61.6756.672 59.67 50.00 49.6753.11Mean 52.67 56.33 55.6754.89
P K1 K2 K3 Mean1 46.00 58.67 65.33 56.672 45.33 54.00 60.00 53.11Mean 45.67 56.33 62.67 54.89
Potash x Block Phosphorus x Block
Potash x Phosphorus
SSEB =3*devsq(range) – SSR – SSB
SSB=9*devsq(range)Main effect of Phosphorus
SSAB=3*devsq(range) – SSA – SSB
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ANOVA
Source df SS MS FTotal 17 1833.78Block 2 45.78 22.89Potash (K) 2 885.78 442.89 22.64**Error(a) 4 78.22 19.56Phosphorus (P) 1 56.89 56.89 0.16nsError(b) 2 693.78 346.89KxP 2 19.11 9.56 0.71nsError(ab) 4 54.22 13.55
See Excel worksheet calculations
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Interpretation
Only potash had a significant effect on barley dry matter production
Each increment of added potash resulted in an increase in the yield of dry matter (~340 g/plot per kg increase in potash
The increase took place regardless of the level of phosphorus
Potash None 25 kg/ha 50 kg/ha SEMean Yield 45.67 56.33 62.67 1.80
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Repeated measurements over time We often wish to take repeated measures on experimental units to
observe trends in response over time. – Repeated cuttings of a pasture– Multiple harvests of a fruit or vegetable crop during a season– Annual yield of a perennial crop– Multiple observations on the same animal (developmental responses)
Often provides more efficient use of resources than using different experimental units for each time period.
May also provide more precise estimation of time trends by reducing random error among experimental units – effect is similar to blocking
Problem: observations over time are not assigned at random to experimental units.– Observations on the same plot will tend to be positively correlated– Violates the assumption that errors (residuals) are independent
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Analysis of repeated measurements The simplest approach is to treat sampling times
as sub-plots in a split-plot experiment. – Some references recommend use of strip-plot rather
than a split-plot Univariate adjustments can be made Multivariate procedures can be used to adjust for
the correlations among sampling periods Mixed Model approaches can be used to adjust
for the correlations among sampling periods
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Split-plot in time In a sense, a split-plot is a specific case of repeated
measures, where sub-plots represent repeated measurements on a common main plot
Analysis as a split-plot is valid only if all pairs of sub-plots in each main plot can be assumed to be equally correlated– Compound symmetry– Sphericity
When time is a sub-plot, correlations may be greatest for samples taken at short time intervals and less for distant sampling periods, so assumptions may not be valid– Not a problem when there are only two sampling periods
Formal names for required assumptions
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Univariate adjustments for repeated measures
Fit a smooth curve to the time trends and analyze a derived variable– average– maximum response– area under curve– time to reach the maximum
Use polynomial contrasts to evaluate trends over time (linear, quadratic responses) and compare responses for each treatment
Reduce df for subplots, interactions, and subplot error terms to obtain more conservative F tests
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Multivariate adjustments for repeated measures
In PROC GLM, each repeated measure is treated like an additional variable in a multivariate analysis:model yield1 yield2 yield3 yield4=variety/nouni;repeated harvest / printe;
MANOVA approach is very conservative– Effectively controls Type I error– Power may be low
• Many parameters are estimated so df for error may be too low• Missing values result in an unnecessary loss of available
information No real benefit compared to a Mixed Model approach
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Covariance Structure for Residuals
1 2 1 2 1 2
2 2 2Y Y Y Y Y ,Y2 sed2 se2 se2 covariance
1 2
2 2Y Y
1 2Y ,Y 0 1 2
2Y Y
2MSEr
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Covariance Structure for Residuals
No correlation (independence)– 4 measurements per subject– All covariances = 0
Compound symmetry (CS)– All covariances (off-diagonal elements) are the same– Often applies for split-plot designs (sub-plots within main plots are equally
correlated)
2
22
2
2
1 0 0 00 0 00 1 0 00 0 00 0 1 00 0 00 0 0 10 0 0
2
11
11
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Covariance Structure for Residuals
Autoregressive (AR)– Applies to time series analyses– For a first-order AR(1) structure, the
within subject correlations drop off exponentially as the number of time lags between measurements increases (assuming time lags are all the same)
Unstructured (UN)– Complex and computer intensive– No particular pattern for the
covariances is assumed– May have low power due to loss of df
for error
2 3
22
2
3 2
11
11
12 13 14
12 23 242
13 23 34
14 24 34
11
11
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Mixed Model adjustment for error structure
Stage one: estimate covariance structure for residuals1. Determine which covariance structures would make sense for
the experimental design and type of data that is collected2. Use graphical methods to examine covariance patterns over time3. Likelihood ratio tests of more complex vs simpler models4. Information content
= (-2 res log likelihood)simple model
minus (-2 res log likelihood)complex model
df = difference in # parameters estimated
AIC, AICC, BIC – information contentadjust for loss in power due to loss of df in more complex models
Null model - no adjustment for correlated errors
2
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Mixed Model adjustment for error structure
Stage two:– include appropriate covariance structure in the model– use Generalized Least Squares methodology to evaluate
treatment and time effects Computer intensive
– use PROC MIXED or GLIMMIX in SAS