Supported by Dr. Osamu Ogasawara and  providing  . |
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Last data update: 2014.03.03 |
Plot method for GPvamDescriptionPlot teacher effects and residuals. The caterpillar plots use a modified version of the plotCI function from R package gplots. According to that package, "Original version [of plotCI] by Bill Venables wvenable@attunga.stats.adelaide.edu.au posted to r-help on Sep. 20, 1997. Enhanced version posted to r-help by Ben Bolker ben@zoo.ufl.edu on Apr. 16, 2001. This version was modified and extended by Gregory R. Warnes greg@warnes.net. Additional changes suggested by Martin Maechler maechler@stat.math.ethz.ch integrated on July 29, 2004." Usage## S3 method for class 'GPvam' plot(x, ..., alpha) Arguments
ValueRequires user to click window or press "enter" to progress through plots. Returns caterpillar plots (via the package gplots) and residual plots. Author(s)Andrew Karl akarl@asu.edu Yan Yang Sharon Lohr Other authors as listed above for the caterpillar plots. ReferencesKarl, A., Yang, Y. and Lohr, S. (2013) Efficient Maximum Likelihood Estimation of Multiple Membership Linear Mixed Models, with an Application to Educational Value-Added Assessments Computational Statistics & Data Analysis 59, 13–27. Karl, A., Yang, Y. and Lohr, S. (2014) Computation of Maximum Likelihood Estimates for Multiresponse Generalized Linear Mixed Models with Non-nested, Correlated Random Effects Computational Statistics & Data Analysis 73, 146–162. Karl, A., Yang, Y. and Lohr, S. (2014) A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects Journal of Educational and Behavioral Statistics 38, 577–603. Lockwood, J., McCaffrey, D., Mariano, L., Setodji, C. (2007) Bayesian Methods for Scalable Multivariate Value-Added Assesment. Journal of Educational and Behavioral Statistics 32, 125–150. Mariano, L., McCaffrey, D. and Lockwood, J. (2010) A Model for Teacher Effects From Longitudinal Data Without Assuming Vertical Scaling. Journal of Educational and Behavioral Statistics 35, 253–279. McCaffrey, D. and Lockwood, J. (2011) Missing Data in Value-Added Modeling of Teavher Effects, Annals of Applied Statistics 5, 773–797 See Also
Examplesdata(vam_data) GPvam(vam_data,student.side="R",persistence="VP", fixed_effects=formula(~as.factor(year)+cont_var+0),verbose=TRUE,max.iter.EM=1) result <- GPvam(vam_data,student.side="R",persistence="VP", fixed_effects=formula(~as.factor(year)+cont_var+0),verbose=TRUE) summary(result) plot(result) Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(GPvam)
Loading required package: Matrix
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GPvam/plot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot
> ### Title: Plot method for GPvam
> ### Aliases: plot.GPvam
> ### Keywords: regression
>
> ### ** Examples
>
> data(vam_data)
> GPvam(vam_data,student.side="R",persistence="VP",
+ fixed_effects=formula(~as.factor(year)+cont_var+0),verbose=TRUE,max.iter.EM=1)
Beginning EM algorithm
Iteration Time: 1.961 seconds
done.
Object of class 'GPvam'. Use functions 'plot' and 'summary'.
Object contains elements: loglik, teach.effects, parameters Hessian, R_i, teach.cov, mresid, cresid, y, yhat, stu.cov, num.obs, num.student, num.year, num.teach, yhat.m, sresid, yhat.s
Number of iterations: 1
Log-likelihood: -13881.6
Parameter estimates:
Estimate Standard Error
as.factor(year)1 0.7398 0.7772
as.factor(year)2 1.2155 0.7260
as.factor(year)3 0.2663 0.7796
cont_var 1.0404 0.0234
error covariance:[1,1] 34.2372 NA
error covariance:[2,1] 10.5198 NA
error covariance:[3,1] 10.5212 NA
error covariance:[2,2] 36.2946 NA
error covariance:[3,2] 12.7817 NA
error covariance:[3,3] 38.8274 NA
teacher effect from year1 28.8301 NA
teacher effect from year2 20.8080 NA
teacher effect from year3 21.6267 NA
alpha_21 0.3768 NA
alpha_31 0.3982 NA
alpha_32 0.3558 NA
Teacher Effects
teacher_year teacher effect_year EBLUP std_error
1_1(year1) 1 1(year1) 1 5.4943 1.3586
1_10(year1) 1 10(year1) 1 4.5589 1.3588
1_11(year1) 1 11(year1) 1 -1.1426 1.3588
1_12(year1) 1 12(year1) 1 1.3383 1.3589
1_13(year1) 1 13(year1) 1 -1.2107 1.3588
1_14(year1) 1 14(year1) 1 -4.9192 1.3587
1_15(year1) 1 15(year1) 1 -3.2284 1.3586
1_16(year1) 1 16(year1) 1 5.8422 1.3588
1_17(year1) 1 17(year1) 1 0.8648 1.3588
1_18(year1) 1 18(year1) 1 -5.6912 1.3591
1_19(year1) 1 19(year1) 1 -6.8144 1.3587
1_2(year1) 1 2(year1) 1 1.5248 1.3586
1_20(year1) 1 20(year1) 1 2.5639 1.3587
1_21(year1) 1 21(year1) 1 3.9056 1.3587
1_22(year1) 1 22(year1) 1 -3.6329 1.3586
1_23(year1) 1 23(year1) 1 -0.6378 1.3587
1_24(year1) 1 24(year1) 1 5.2184 1.3588
1_25(year1) 1 25(year1) 1 0.8730 1.3588
1_26(year1) 1 26(year1) 1 -8.0142 1.3586
1_27(year1) 1 27(year1) 1 -6.0333 1.3588
1_28(year1) 1 28(year1) 1 10.8885 1.3587
1_29(year1) 1 29(year1) 1 -2.2860 1.3587
1_3(year1) 1 3(year1) 1 -4.1188 1.3586
1_30(year1) 1 30(year1) 1 -1.3448 1.3586
1_31(year1) 1 31(year1) 1 4.6316 1.3592
1_32(year1) 1 32(year1) 1 3.2908 1.3586
1_33(year1) 1 33(year1) 1 12.5611 1.3586
1_34(year1) 1 34(year1) 1 -2.8988 1.3587
1_35(year1) 1 35(year1) 1 -4.9044 1.3587
1_36(year1) 1 36(year1) 1 -0.7243 1.3589
1_37(year1) 1 37(year1) 1 -1.7804 1.3588
1_38(year1) 1 38(year1) 1 9.0209 1.3588
1_39(year1) 1 39(year1) 1 -4.3703 1.3587
1_4(year1) 1 4(year1) 1 -1.2718 1.3589
1_40(year1) 1 40(year1) 1 -9.1680 1.3588
1_41(year1) 1 41(year1) 1 -1.1801 1.3587
1_42(year1) 1 42(year1) 1 -6.3993 1.3590
1_43(year1) 1 43(year1) 1 4.1618 1.3587
1_44(year1) 1 44(year1) 1 0.5171 1.3588
1_45(year1) 1 45(year1) 1 -3.2389 1.3586
1_46(year1) 1 46(year1) 1 -2.5060 1.3589
1_47(year1) 1 47(year1) 1 2.8502 1.3586
1_48(year1) 1 48(year1) 1 9.5136 1.3591
1_49(year1) 1 49(year1) 1 4.4562 1.3587
1_5(year1) 1 5(year1) 1 -1.1239 1.3586
1_50(year1) 1 50(year1) 1 -6.1486 1.3589
1_6(year1) 1 6(year1) 1 -12.4414 1.3587
1_7(year1) 1 7(year1) 1 0.4634 1.3587
1_8(year1) 1 8(year1) 1 7.0759 1.3588
1_9(year1) 1 9(year1) 1 5.6151 1.3587
2_1(year2) 2 1(year2) 2 4.0651 1.2769
2_10(year2) 2 10(year2) 2 -0.9662 1.2767
2_11(year2) 2 11(year2) 2 -5.0962 1.2768
2_12(year2) 2 12(year2) 2 10.3181 1.2767
2_13(year2) 2 13(year2) 2 -10.5016 1.2767
2_14(year2) 2 14(year2) 2 8.2059 1.2769
2_15(year2) 2 15(year2) 2 0.4114 1.2772
2_16(year2) 2 16(year2) 2 1.7010 1.2767
2_17(year2) 2 17(year2) 2 -6.2009 1.2768
2_18(year2) 2 18(year2) 2 -5.9841 1.2770
2_19(year2) 2 19(year2) 2 1.7499 1.2769
2_2(year2) 2 2(year2) 2 -2.5258 1.2768
2_20(year2) 2 20(year2) 2 -2.1726 1.2768
2_21(year2) 2 21(year2) 2 -1.7815 1.2768
2_22(year2) 2 22(year2) 2 -0.7233 1.2767
2_23(year2) 2 23(year2) 2 6.6650 1.2768
2_24(year2) 2 24(year2) 2 -6.3348 1.2767
2_25(year2) 2 25(year2) 2 1.6013 1.2773
2_26(year2) 2 26(year2) 2 -4.4419 1.2769
2_27(year2) 2 27(year2) 2 -1.1115 1.2768
2_28(year2) 2 28(year2) 2 -4.5496 1.2768
2_29(year2) 2 29(year2) 2 7.0083 1.2770
2_3(year2) 2 3(year2) 2 2.4056 1.2769
2_30(year2) 2 30(year2) 2 1.3443 1.2768
2_31(year2) 2 31(year2) 2 -0.6239 1.2767
2_32(year2) 2 32(year2) 2 1.4729 1.2768
2_33(year2) 2 33(year2) 2 -1.6942 1.2768
2_34(year2) 2 34(year2) 2 -0.9964 1.2767
2_35(year2) 2 35(year2) 2 -1.7018 1.2767
2_36(year2) 2 36(year2) 2 3.4983 1.2769
2_37(year2) 2 37(year2) 2 -5.3257 1.2768
2_38(year2) 2 38(year2) 2 3.5808 1.2767
2_39(year2) 2 39(year2) 2 -5.2428 1.2770
2_4(year2) 2 4(year2) 2 -5.4451 1.2772
2_40(year2) 2 40(year2) 2 -1.8464 1.2768
2_41(year2) 2 41(year2) 2 0.4236 1.2768
2_42(year2) 2 42(year2) 2 1.6709 1.2769
2_43(year2) 2 43(year2) 2 0.4155 1.2768
2_44(year2) 2 44(year2) 2 8.9165 1.2768
2_45(year2) 2 45(year2) 2 -1.4727 1.2770
2_46(year2) 2 46(year2) 2 -1.9719 1.2768
2_47(year2) 2 47(year2) 2 -2.8640 1.2767
2_48(year2) 2 48(year2) 2 2.8426 1.2770
2_49(year2) 2 49(year2) 2 -0.8208 1.2767
2_5(year2) 2 5(year2) 2 10.0016 1.2769
2_50(year2) 2 50(year2) 2 -0.7592 1.2767
2_6(year2) 2 6(year2) 2 3.6474 1.2770
2_7(year2) 2 7(year2) 2 -5.0309 1.2772
2_8(year2) 2 8(year2) 2 3.3579 1.2768
2_9(year2) 2 9(year2) 2 2.8822 1.2773
3_1(year3) 3 1(year3) 3 1.5789 1.2832
3_10(year3) 3 10(year3) 3 -1.0952 1.2835
3_11(year3) 3 11(year3) 3 0.3660 1.2835
3_12(year3) 3 12(year3) 3 -2.3912 1.2832
3_13(year3) 3 13(year3) 3 -3.3489 1.2831
3_14(year3) 3 14(year3) 3 -2.9877 1.2835
3_15(year3) 3 15(year3) 3 -0.0139 1.2831
3_16(year3) 3 16(year3) 3 6.1656 1.2831
3_17(year3) 3 17(year3) 3 3.5150 1.2832
3_18(year3) 3 18(year3) 3 8.6850 1.2832
3_19(year3) 3 19(year3) 3 -0.2732 1.2831
3_2(year3) 3 2(year3) 3 1.1045 1.2832
3_20(year3) 3 20(year3) 3 -3.1988 1.2836
3_21(year3) 3 21(year3) 3 3.7753 1.2832
3_22(year3) 3 22(year3) 3 -0.7740 1.2832
3_23(year3) 3 23(year3) 3 4.1196 1.2835
3_24(year3) 3 24(year3) 3 -0.1181 1.2831
3_25(year3) 3 25(year3) 3 0.6988 1.2832
3_26(year3) 3 26(year3) 3 7.3457 1.2844
3_27(year3) 3 27(year3) 3 4.9773 1.2831
3_28(year3) 3 28(year3) 3 -9.1270 1.2832
3_29(year3) 3 29(year3) 3 0.8161 1.2831
3_3(year3) 3 3(year3) 3 -5.0616 1.2834
3_30(year3) 3 30(year3) 3 -3.3560 1.2835
3_31(year3) 3 31(year3) 3 -6.9014 1.2835
3_32(year3) 3 32(year3) 3 6.3012 1.2836
3_33(year3) 3 33(year3) 3 -6.4033 1.2832
3_34(year3) 3 34(year3) 3 2.6047 1.2833
3_35(year3) 3 35(year3) 3 -15.7624 1.2832
3_36(year3) 3 36(year3) 3 1.3826 1.2832
3_37(year3) 3 37(year3) 3 -2.0216 1.2832
3_38(year3) 3 38(year3) 3 3.2722 1.2831
3_39(year3) 3 39(year3) 3 -0.2108 1.2832
3_4(year3) 3 4(year3) 3 -7.2141 1.2831
3_40(year3) 3 40(year3) 3 5.6683 1.2831
3_41(year3) 3 41(year3) 3 -2.9088 1.2832
3_42(year3) 3 42(year3) 3 1.6636 1.2838
3_43(year3) 3 43(year3) 3 -4.8645 1.2831
3_44(year3) 3 44(year3) 3 -2.8043 1.2840
3_45(year3) 3 45(year3) 3 -2.3467 1.2832
3_46(year3) 3 46(year3) 3 -2.1302 1.2834
3_47(year3) 3 47(year3) 3 3.0368 1.2831
3_48(year3) 3 48(year3) 3 -2.1933 1.2832
3_49(year3) 3 49(year3) 3 5.9394 1.2832
3_5(year3) 3 5(year3) 3 0.0635 1.2831
3_50(year3) 3 50(year3) 3 5.1128 1.2831
3_6(year3) 3 6(year3) 3 3.0760 1.2831
3_7(year3) 3 7(year3) 3 2.6713 1.2833
3_8(year3) 3 8(year3) 3 2.1322 1.2834
3_9(year3) 3 9(year3) 3 1.4344 1.2833
> ## No test:
> result <- GPvam(vam_data,student.side="R",persistence="VP",
+ fixed_effects=formula(~as.factor(year)+cont_var+0),verbose=TRUE)
Beginning EM algorithm
Iteration Time: 1.662 seconds
iter: 2
log-likelihood: -12519.9513384
change in loglik: 1361.6523564
fixed effects: 0.7398 1.2155 0.2663 1.0404
R_i:
[,1] [,2] [,3]
[1,] 34.2372 10.5198 10.5212
[2,] 10.5198 36.2946 12.7817
[3,] 10.5212 12.7817 38.8274
[,1] [,2] [,3]
[1,] 1.0000 0.2984 0.2886
[2,] 0.2984 1.0000 0.3405
[3,] 0.2886 0.3405 1.0000
G:
[1] 28.8301 20.8080 21.6267
alphas:
[1] 1.0000 0.3768 0.3982 1.0000 0.3558 1.0000
Iteration Time: 3.186 seconds
iter: 3
log-likelihood: -12423.2035217
change in loglik: 96.7478167
fixed effects: 0.7447 1.2147 0.2676 1.0142
R_i:
[,1] [,2] [,3]
[1,] 46.9644 22.9488 23.6294
[2,] 22.9488 48.7570 24.0400
[3,] 23.6294 24.0400 51.0614
[,1] [,2] [,3]
[1,] 1.0000 0.4796 0.4825
[2,] 0.4796 1.0000 0.4818
[3,] 0.4825 0.4818 1.0000
G:
[1] 27.4204 18.8016 20.0552
alphas:
[1] 1.0000 0.4010 0.4314 1.0000 0.3736 1.0000
Iteration Time: 2.156 seconds
iter: 4
log-likelihood: -12422.7537933
change in loglik: 0.4497283
fixed effects: 0.7465 1.2144 0.2681 1.0046
R_i:
[,1] [,2] [,3]
[1,] 47.4964 23.7903 24.6052
[2,] 23.7903 49.4482 24.7245
[3,] 24.6052 24.7245 51.8471
[,1] [,2] [,3]
[1,] 1.0000 0.4909 0.4958
[2,] 0.4909 1.0000 0.4883
[3,] 0.4958 0.4883 1.0000
G:
[1] 26.7477 18.3171 19.8012
alphas:
[1] 1.0000 0.3952 0.4241 1.0000 0.3701 1.0000
Iteration Time: 1.544 seconds
iter: 5
log-likelihood: -12422.7457002
change in loglik: 0.0080932
fixed effects: 0.7467 1.2144 0.2681 1.0036
R_i:
[,1] [,2] [,3]
[1,] 47.5200 23.8603 24.6896
[2,] 23.8603 49.4792 24.7501
[3,] 24.6896 24.7501 51.8974
[,1] [,2] [,3]
[1,] 1.0000 0.4921 0.4972
[2,] 0.4921 1.0000 0.4884
[3,] 0.4972 0.4884 1.0000
G:
[1] 26.6231 18.2553 19.7490
alphas:
[1] 1.0000 0.3941 0.4223 1.0000 0.3696 1.0000
Iteration Time: 0.775 seconds
Algorithm converged.
iter: 6
log-likelihood: -12422.7449652
change in loglik: 0.0007350
fixed effects: 0.7467 1.2144 0.2681 1.0035
R_i:
[,1] [,2] [,3]
[1,] 47.5584 23.9029 24.7380
[2,] 23.9029 49.5189 24.7895
[3,] 24.7380 24.7895 51.9465
[,1] [,2] [,3]
[1,] 1.0000 0.4926 0.4977
[2,] 0.4926 1.0000 0.4888
[3,] 0.4977 0.4888 1.0000
gamma_teach_year 1
[1] 26.5964
gamma_teach_year 2
[1] 18.2483
gamma_teach_year 3
[1] 19.7387
done.
> summary(result)
Number of observations: 3750
Number of years: 3
Number of students: 1250
Number of teachers in year 1 : 50
Number of teachers in year 2 : 50
Number of teachers in year 3 : 50
Number of EM iterations: 6
-2 log-likelihood 24845.49
AIC 24877.49
AICc 24877.64
Covariance matrix for current and future year
effects of year 1 teachers.
year1
year1 26.5964
with correlation matrix
year1
year1 1
Covariance matrix for current and future year
effects of year 2 teachers.
year2
year2 18.2483
with correlation matrix
year2
year2 1
Covariance matrix for current and future year
effects of year 3 teachers.
year3
year3 19.7387
with correlation matrix
year3
year3 1
Block of error covariance matrix (R):
[,1] [,2] [,3]
[1,] 47.5584 23.9029 24.7380
[2,] 23.9029 49.5189 24.7895
[3,] 24.7380 24.7895 51.9465
with correlation matrix
[,1] [,2] [,3]
[1,] 1.0000 0.4926 0.4977
[2,] 0.4926 1.0000 0.4888
[3,] 0.4977 0.4888 1.0000
Parameter estimates:
Estimate Standard Error
as.factor(year)1 0.7467 0.7550
as.factor(year)2 1.2144 0.6979
as.factor(year)3 0.2681 0.7622
cont_var 1.0035 0.0243
error covariance:[1,1] 47.5584 NA
error covariance:[2,1] 23.9029 NA
error covariance:[3,1] 24.7380 NA
error covariance:[2,2] 49.5189 NA
error covariance:[3,2] 24.7895 NA
error covariance:[3,3] 51.9465 NA
teacher effect from year1 26.5964 NA
teacher effect from year2 18.2483 NA
teacher effect from year3 19.7387 NA
alpha_21 0.3939 NA
alpha_31 0.4219 NA
alpha_32 0.3695 NA
Distribution of marginal residuals
Min. 1st Qu. Median Mean 3rd Qu. Max.
-33.89000 -5.78300 -0.05905 0.00000 5.85500 30.30000
Distribution of raw conditional residuals
Min. 1st Qu. Median Mean 3rd Qu. Max.
-26.51000 -4.67600 -0.09345 0.00000 4.64400 23.72000
Distribution of scaled conditional residuals
Min. 1st Qu. Median Mean 3rd Qu. Max.
-3.113000 -0.666000 -0.005821 0.000000 0.662500 4.308000
>
> plot(result)
> ## End(No test)
>
>
>
>
>
> dev.off()
null device
1
>
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Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and