Randomized block ANOVA for trapping experiment data

Obs block treat count sqrtcount logcount y
1 1 AP 4 2.0000 1.60944 1.60944
2 1 FRAP 79 8.8882 4.38203 4.38203
3 1 IDAP 7 2.6458 2.07944 2.07944
4 1 ISAP 10 3.1623 2.39790 2.39790
5 2 AP 1 1.0000 0.69315 0.69315
6 2 FRAP 124 11.1355 4.82831 4.82831
7 2 IDAP 13 3.6056 2.63906 2.63906
8 2 ISAP 20 4.4721 3.04452 3.04452
9 3 AP 0 0.0000 0.00000 0.00000
10 3 FRAP 14 3.7417 2.70805 2.70805
11 3 IDAP . . . .
12 3 ISAP 2 1.4142 1.09861 1.09861
13 4 AP 0 0.0000 0.00000 0.00000
14 4 FRAP 15 3.8730 2.77259 2.77259
15 4 IDAP 11 3.3166 2.48491 2.48491
16 4 ISAP 7 2.6458 2.07944 2.07944
17 5 AP 0 0.0000 0.00000 0.00000
18 5 FRAP 29 5.3852 3.40120 3.40120
19 5 IDAP 7 2.6458 2.07944 2.07944
20 5 ISAP 7 2.6458 2.07944 2.07944
21 6 AP 2 1.4142 1.09861 1.09861
22 6 FRAP 70 8.3666 4.26268 4.26268
23 6 IDAP 14 3.7417 2.70805 2.70805
24 6 ISAP 20 4.4721 3.04452 3.04452
Randomized block ANOVA for trapping experiment data; Plot of y by treat identified by block y 0 1 2 3 4 5 treat AP FRAP IDAP ISAP Randomized block ANOVA for trapping experiment data block 1 2 3 4 5 6

Randomized block ANOVA for trapping experiment data

The Mixed Procedure

Model Information
Data Set WORK.TRAPEXP
Dependent Variable y
Covariance Structure Variance Components
Estimation Method REML
Residual Variance Method Profile
Fixed Effects SE Method Kenward-Roger
Degrees of Freedom Method Kenward-Roger
Class Level Information
Class Levels Values
treat 4 AP FRAP IDAP ISAP
block 6 1 2 3 4 5 6
Dimensions
Covariance Parameters 2
Columns in X 5
Columns in Z 6
Subjects 1
Max Obs per Subject 23
Number of Observations
Number of Observations Read 24
Number of Observations Used 23
Number of Observations Not Used 1
Iteration History
Iteration Evaluations -2 Res Log Like Criterion
0 1 47.44629548  
1 2 38.98690259 0.00950955
2 1 38.96571169 0.00025308
3 1 38.96519017 0.00000021
4 1 38.96518975 0.00000000
Convergence criteria met.
Covariance Parameter Estimates
Cov Parm Estimate Alpha Lower Upper
block 0.3332 0.05 0.1159 3.1475
Residual 0.1831 0.05 0.09789 0.4576
Fit Statistics
-2 Res Log Likelihood 39.0
AIC (Smaller is Better) 43.0
AICC (Smaller is Better) 43.7
BIC (Smaller is Better) 42.5
Type 3 Tests of Fixed Effects
Effect Num DF Den DF F Value Pr > F
treat 3 13.9 54.68 <.0001
Least Squares Means
Effect treat Estimate Standard
Error		 DF t Value Pr > |t| Alpha Lower Upper
treat AP 0.5669 0.2933 8.59 1.93 0.0869 0.05 -0.1016 1.2353
treat FRAP 3.7258 0.2933 8.59 12.70 <.0001 0.05 3.0574 4.3942
treat IDAP 2.2417 0.3069 9.83 7.30 <.0001 0.05 1.5562 2.9272
treat ISAP 2.2907 0.2933 8.59 7.81 <.0001 0.05 1.6223 2.9592
Differences of Least Squares Means
Effect treat _treat Estimate Standard
Error		 DF t Value Pr > |t| Adjustment Adj P Alpha Lower Upper Adj Lower Adj Upper
treat AP FRAP -3.1589 0.2470 13.9 -12.79 <.0001 Tukey-Kramer <.0001 0.05 -3.6892 -2.6287 -3.8777 -2.4402
treat AP IDAP -1.6748 0.2630 14 -6.37 <.0001 Tukey-Kramer <.0001 0.05 -2.2389 -1.1108 -2.4392 -0.9105
treat AP ISAP -1.7239 0.2470 13.9 -6.98 <.0001 Tukey-Kramer <.0001 0.05 -2.2541 -1.1936 -2.4426 -1.0051
treat FRAP IDAP 1.4841 0.2630 14 5.64 <.0001 Tukey-Kramer 0.0003 0.05 0.9200 2.0482 0.7197 2.2485
treat FRAP ISAP 1.4351 0.2470 13.9 5.81 <.0001 Tukey-Kramer 0.0002 0.05 0.9048 1.9653 0.7163 2.1538
treat IDAP ISAP -0.04903 0.2630 14 -0.19 0.8548 Tukey-Kramer 0.9976 0.05 -0.6131 0.5151 -0.8134 0.7154
Randomized block ANOVA for trapping experiment data; Panel of conditional residuals for y. The panel consists of a scatterplot of the residuals, a histogram with normal density, a Q-Q plot, and summary statistics for the residuals and the model fit. Conditional Residuals for y BIC 42.549 AICC 43.715 AIC 42.965 Objective 38.965 Fit Statistics Std Dev 0.349 Maximum 0.6813 Mean 42E-17 Minimum -0.626 Observations 23 Residual Statistics -2 -1 0 1 2 Quantile -0.75 -0.25 0.25 0.75 Residual -0.9 -0.3 0.3 0.9 Residual 0 10 20 30 Percent 0 1 2 3 4 Predicted -0.50 -0.25 0.00 0.25 0.50 0.75 Residual