SMHS BigDataBigSci SEM Ex2 - Revision history

Pineaumi: /* Output */

2016-05-23T19:10:55Z

‎Output

Imoubara: /* Output */

2016-05-23T13:41:07Z

‎Output

Dinov at 12:42, 19 May 2016

2016-05-19T12:42:40Z

Imoubara at 19:46, 18 May 2016

2016-05-18T19:46:28Z

Imoubara: /* Structural Model */

2016-05-18T19:37:58Z

‎Structural Model

Imoubara: /* Validation of the measurement model */

2016-05-18T19:36:17Z

‎Validation of the measurement model

Imoubara at 16:00, 16 May 2016

2016-05-16T16:00:32Z

Dinov at 17:06, 5 May 2016

2016-05-05T17:06:37Z

Dinov at 16:56, 5 May 2016

2016-05-05T16:56:08Z

Dinov at 16:53, 5 May 2016

2016-05-05T16:53:08Z

Show changes

@@ Line 343: / Line 343: @@
 # The last section contains the parameter estimates, standard errors (if the information matrix is expected or observed, and if the standard errors are standard, robust, or based on the bootstrap).  Then, it tabulates all free (and fixed) parameters that were included in the model.  Typically, first the latent variables are shown, followed by covariances and (residual) variances.  The first column (Estimate) contains the (estimated or fixed) parameter value for each model parameter;  the second column (Std.err) contains  the  standard  error  for  each  estimated  parameter;  the  third  column  (Z-value)  contains  the  Wald statistic (which is simply obtained by dividing the parameter value by its standard error), and the last column contains the p-value for testing the null hypothesis that the parameter equals zero in the population.
-<b>Note:</b> You can get this type of error <b>“…system is computationally singular: reciprocal condition…”,</b> which indicates that the design matrix is not invertible. Thus, it can't be used to develop a regression model. This is due to linearly dependent columns, i.e. strongly correlated variables. Resolve pairwise covariances (or correlations) of your variables to investigate if there are any variables that can potentially be removed. You're looking for covariances (or correlations) >> 0. We can also automate this variable selection by using a forward stepwise regression.
+<b>Note:</b> You can get this type of error ''<b>“…system is computationally singular: reciprocal condition…”,</b>'' which indicates that the design matrix is not invertible. Thus, it can't be used to develop a regression model. This is due to linearly dependent columns, i.e. strongly correlated variables. Resolve pairwise covariances (or correlations) of your variables to investigate if there are any variables that can potentially be removed. You're looking for covariances (or correlations) >> 0. We can also automate this variable selection by using a forward stepwise regression.
   # Graphical fit model visualization

@@ Line 333: / Line 333: @@
 ====Output====
 parts of the Lavaan SEM output
-<li> First six lines are called the header contains the following information:</li>
+*First six lines are called the header contains the following information:
-<li> lavaan version number</li>
+*lavaan version number
-<li> lavaan converge info (normal or not), and # iterations needed</li>
+*lavaan converge info (normal or not), and # iterations needed
-<li> the number of observations that were effectively used in the analysis</li>
+*the number of observations that were effectively used in the analysis
-<li> the estimator that was used to obtain the parameter values (here: ML)</li>
+*the estimator that was used to obtain the parameter values (here: ML)
-<li> the model test statistic, the degrees of freedom, and a corresponding p-value</li>
+*the model test statistic, the degrees of freedom, and a corresponding p-value
 # Next, is the Model test baseline model and the value for the SRMR
@@ Line 354: / Line 354: @@
 <center>[[Image:SMHS_BigDataBigSci5.png|500px]]</center>
 ==See also==

@@ Line 4: / Line 4: @@
   # Dinov et al., 2016
-<mark>INSERT CHART HERE</mark>
+<center>
   # install.packages("lavaan")

@@ Line 298: / Line 298: @@
   semPaths(fit3)
-[[Image:SMHS_BigDataBigSci4.png|500px]]
+<center>[[Image:SMHS_BigDataBigSci4.png|500px]]</center>
   semPaths(fit3, "std", ask = FALSE, as.expression = "edges", mar = c(3, 1, 5, 1))
-[[Image:SMHS_BigDataBigSci5.png|500px]]
+<center>[[Image:SMHS_BigDataBigSci5.png|500px]]</center>

@@ Line 229: / Line 229: @@
   Information                        &nbsp;&nbsp;      Observed
   Standard Errors                    &nbsp;&nbsp;      Standard
- <blockquote>Estimate  Std.err  Z-value  P(>|z|)</blockquote>
   Latent variables:
   Imaging =~

@@ Line 31: / Line 31: @@
   L_cingulate_gyrus_ComputeArea <- lm(L_cingulate_gyrus_ComputeArea ~  L_cingulate_gyrus_Volume+R_cingulate_gyrus_ComputeArea+R_cingulate_gyrus_Volume+L_caudate_ComputeArea+L_caudate_Volume+R_caudate_ComputeArea+R_caudate_Volume+L_putamen_ComputeArea+L_putamen_Volume+R_putamen_ComputeArea+R_putamen_Volum e+L_hippocampus_ComputeArea+L_hippocampus_Volume+R_hippocampus_ComputeArea+R_hippocampus_Volume+cerebellum_ComputeArea+cerebellum_Volume+L_fusiform_gyrus_ComputeArea+L_fusiform_gyrus_Volume+R_fusiform_gyrus_ComputeArea+R_fusiform_gyru s_Volume, data=myData)$\$$residuals
   Weight <- lm(Weight ~ Sex+ResearchGroup+Age+chr12_rs34637584_GT+chr17_rs11868035_GT+chr17_rs11012_GT+chr17_rs393152_GT+chr17_rs12185268_GT, data=myData)$\$$residuals
-  UPDRS_Part_I_Summary_Score_Baseline  <- lm(UPDRS_Part_I_Summary_Score_Baseline  ~  UPDRS_Part_I_Summary_Score_Month_03+UPDRS_Part_I_Summary_Score_Month_06+UPDRS_Part_I_Summary_Score_Month_09+UPDRS_Part_I_Summary_Score_Month_12+UPDRS_Part_I_Summary_Score_Month_18+UPDRS_Part_I_Summary_Score_Month_24+UPDRS_Part_II_Pati ent_Questionnaire_Summary_Score_Baseline+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_03+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_06+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_09+UPDRS_Part_II_Pa tient_Questionnaire_Summary_Score_Month_12+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_18+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_24+UPDRS_Part_III_Summary_Score_Baseline+UPDRS_Part_III_Summary_Score_Month_ 03+UPDRS_Part_III_Summary_Score_Month_06+UPDRS_Part_III_Summary_Score_Month_09+UPDRS_Part_III_Summary_Score_Month_12+UPDRS_Part_III_Summary_Score_Month_18+UPDRS_Part_III_Summary_Score_Month_24+X_Assessment_Non.Motor_Epworth_Sleepiness _Scale_Summary_Score_Baseline+X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_06+X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_12+X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_ Month_24+X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline+X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_06+X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short _Summary_Score_Month_12+X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_24, data=myData)$\$$residuals
+  UPDRS_Part_I_Summary_Score_Baseline  <- lm(UPDRS_Part_I_Summary_Score_Baseline  ~  UPDRS_Part_I_Summary_Score_Month_03+UPDRS_Part_I_Summary_Score_Month_06+UPDRS_Part_I_Summary_Score_Month_09+UPDRS_Part_I_Summary_Score_Month_12+UPDRS_Part_I_Summary_Score_Month_18+UPDRS_Part_I_Summary_Score_Month_24+UPDRS_Part_II_Pati ent_Questionnaire_Summary_Score_Baseline+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_03+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_06+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_09+UPDRS_Part_II_Pa tient_Questionnaire_Summary_Score_Month_12+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_18+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_24+UPDRS_Part_III_Summary_Score_Baseline+UPDRS_Part_III_Summary_Score_Month_ 03+UPDRS_Part_III_Summary_Score_Month_06+UPDRS_Part_III_Summary_Score_Month_09+UPDRS_Part_III_Summary_Score_Month_12+UPDRS_Part_III_Summary_Score_Month_18+UPDRS_Part_III_Summary_Score_Month_24+X_Assessment_Non.Motor_Epworth_Sleepiness _Scale_Summary_Score_Baseline+X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_06+X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_12+X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_ Month_24+X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline+X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_06+X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short _Summary_Score_Month_12+X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_24, data=myData)$\$$residuals })
 ====Structural Model====

@@ Line 1: / Line 1: @@
 ==[[SMHS_BigDataBigSci_SEM| Structural Equation Modeling (SEM)]] - Hands-on Example 2 (Parkinson’s Disease data) ==
-  # Data: PPMI Integrated (imaging, demographics, genetics, clinical and cognitive (UPDRS) data.
+  # Data: PPMI Integrated imaging, demographics, genetics, clinical and cognitive (UPDRS) data.
-  # Dinov et al., 2015
+  # Dinov et al., 2016
 <mark>INSERT CHART HERE</mark>
@@ Line 303: / Line 303: @@
 [[Image:SMHS_BigDataBigSci5.png|500px]]
 ==See also==
 * [[SMHS_BigDataBigSci_SEM| Back to Structural Equation Modeling (SEM)]]
 * [[SMHS_BigDataBigSci_SEM_Ex1| Back to SEM Example 1: School Kids Mental Abilities]]

@@ Line 167: / Line 167: @@
   Sex ~~ Weight
+  '
   # confirmatory factor analysis (CFA)
   # The baseline is a null model constraining the observed variables to covary with no other variables.
@@ Line 293: / Line 292: @@
 <b>Note:</b> You can get this type of error <b>“…system is computationally singular: reciprocal condition…”,</b> which indicates that the design matrix is not invertible. Thus, it can't be used to develop a regression model. This is due to linearly dependent columns, i.e. strongly correlated variables. Resolve pairwise covariances (or correlations) of your variables to investigate if there are any variables that can potentially be removed. You're looking for covariances (or correlations) >> 0. We can also automate this variable selection by using a forward stepwise regression.
   # Graphical fit model visualization
@@ Line 349: / Line 347: @@
   fit3 <- sem(model3, data=myData2, estimator="MLM")
   summary(fit3)
   # report the standardized coefficients of the model