December 2 , 2014
You can forward your questions to
Ann Greenwood (ann.greenwood at popdata.bc.ca) or Vincenza Gruppuso (vincenza at uvic.ca).
Q&A will begin immediately after the presentation.
You can follow the presentation and review previous lectures at ialsa.github.io/COAG-colloquium-2014F/
# loads basic NLSY97-religiosity data as defined in COAG-Colloquium-2014F repository
dsL <- readRDS("./Data/Derived/dsL.rds")
str(dsL)
'data.frame': 107772 obs. of 13 variables: $ id : int 1 1 1 1 1 1 1 1 1 1 ... $ year : int 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ... $ sex : int 2 2 2 2 2 2 2 2 2 2 ... $ race : int 4 4 4 4 4 4 4 4 4 4 ... $ bmonth : int 9 9 9 9 9 9 9 9 9 9 ... $ byear : int 1981 1981 1981 1981 1981 1981 1981 1981 1981 1981 ... $ attend : int 1 6 2 1 1 1 1 1 1 1 ... $ age : int 19 20 21 22 23 24 25 26 27 28 ... $ sexF : Ord.factor w/ 3 levels "Male"<"Female"<..: 2 2 2 2 2 2 2 2 2 2 ... $ raceF : Ord.factor w/ 4 levels "Black"<"Hispanic"<..: 4 4 4 4 4 4 4 4 4 4 ... $ bmonthF : Ord.factor w/ 12 levels "Jan"<"Feb"<"Mar"<..: 9 9 9 9 9 9 9 9 9 9 ... $ attendF : Ord.factor w/ 8 levels "Never"<"Once or Twice"<..: 1 6 2 1 1 1 1 1 1 1 ... $ attendPR: int 7 7 7 7 7 7 7 7 7 7 ...
dsM <- dplyr::filter(dsL, id == 1) %>% dplyr::select(id, year, attend, attendF) dsM
id year attend attendF 1 1 2000 1 Never 2 1 2001 6 About once/week 3 1 2002 2 Once or Twice 4 1 2003 1 Never 5 1 2004 1 Never 6 1 2005 1 Never 7 1 2006 1 Never 8 1 2007 1 Never 9 1 2008 1 Never 10 1 2009 1 Never 11 1 2010 1 Never 12 1 2011 1 Never
How often did you attend a worhsip service during the last year?
attendLevels attendLabels 1 8 Everyday 2 7 Several times/week 3 6 About once/week 4 5 About twice/month 5 4 About once/month 6 3 Less than once/month 7 2 Once or Twice 8 1 Never
"./Scripts/Data/dsL.R"
download the files to work along at GitHub
modelA
Generalized least squares fit by REML
Model: attend ~ 1
Data: dsM
Log-restricted-likelihood: -3727
Coefficients:
(Intercept)
2.477
Degrees of freedom: 1860 total; 1859 residual
Residual standard error: 1.793
\({y_{it}} = {\beta _0} + {\varepsilon _{it}}\)
modelB
Generalized least squares fit by REML
Model: attend ~ 1 + time
Data: dsM
Log-restricted-likelihood: -3719
Coefficients:
(Intercept) time
2.7882 -0.0566
Degrees of freedom: 1860 total; 1858 residual
Residual standard error: 1.782
\({y_{it}} = {\beta _0} + {\beta _1}tim{e_t} + {\varepsilon _{it}}\)
print(mA)
modelA logLik -3727 deviance 7453 AIC 7457 BIC 7468 df.resid 1859 N 1860 p 1 ids 155
modelA
Generalized least squares fit by REML
Model: attend ~ 1
Data: dsM
Log-restricted-likelihood: -3727
Coefficients:
(Intercept)
2.477
Degrees of freedom: 1860 total; 1859 residual
Residual standard error: 1.793
\({y_{it}} = {\beta _0} + {\varepsilon _{it}}\)
print(mB)
modelB logLik -3719 deviance 7438 AIC 7444 BIC 7461 df.resid 1858 N 1860 p 2 ids 155
modelB
Generalized least squares fit by REML
Model: attend ~ 1 + time
Data: dsM
Log-restricted-likelihood: -3719
Coefficients:
(Intercept) time
2.7882 -0.0566
Degrees of freedom: 1860 total; 1858 residual
Residual standard error: 1.782
\({y_{it}} = {\beta _0} + {\beta _1}tim{e_t} + {\varepsilon _{it}}\)
Two recurring issues
Press (W): wide screen | Press (P): view by column
Press (P): view by column
Two recurring issues
Putting it all together
model-SPECIFY.R - contains model specifications of models as gls/lmer syntax.
model-ESTIMATE.R - contains the loop that cycles through all available model specifications. Includes the following operations:
1. Derives the dataset to be used
2. Estimates models:
- lme4::lmer - if a model contains random effects
- nlme::gls - if a model contains only fixed effects
3. Post-process model solution and save datasets:
- dsmInfo.rds - model fit and information indices
- dsFERE.rds - fixed (FE) and random effects (RE) descriptors
- dsp.rds - predicted values from the model
model-COLLECT-SOLUTIONS.R - combines datasets containing model solutions
graph-FERE.R - creates a tile graph of model solution
graph-PREDICT.R - creates a line graph of predicted trajectories
graph-FIT.R - creates a bar graph of model performance indices
graph-FIT-CUSTOM.R - creates a bar graph of fit for each group
dplyr code so select different dataAnother example of a sequence for self-study.
You can forward questions to Ann Greenwood (ann.greenwood@popdata.bc.ca) or Vincenza Gruppuso (vincenza@uvic.ca) during the live Q&A. They are standing by to take your questions.