Difference between revisions of "SMHS BigDataBigSci GCM"
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Revision as of 14:53, 18 May 2016
Model-based Analytics - Growth Curve Models
Latent growth curve models may be used to analyze longitudinal or temporal data where the outcome measure is assessed on multiple occasions, and we examine its change over time, e.g., the trajectory over time can be modeled as a linear or quadratic function. Random effects are used to capture individual differences by conveniently representing (continuous) latent variables, aka growth factors. To fit a linear growth model we may specify a model with two latent variables: a random intercept, and a random slope:
#load data 05_PPMI_top_UPDRS_Integrated_LongFormat.csv ( dim(myData) 661 71), wide
# setwd("/dir/")
myData <- read.csv("https://umich.instructure.com/files/330395/download?download_frd=1&verifier=v6jBvV4x94ka3EYcGKuXXg5BZNaOLBVp0xkJih0H",header=TRUE)
attach(myData)
# dichotomize the "ResearchGroup" variable
table(myData$\$$ResearchGroup)
myData$\$$ResearchGroup <- ifelse(myData$\$$ResearchGroup == "Control", 1, 0)
# linear growth model with 4 timepoints
# intercept (i) and slope (s) with fixed coefficients
# i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4 (intercept/constant)
# s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4 (slope/linear term)
# ??? =~ 0*t1 + 1*t2 + 2*t3 + 3*t4 (quadratic term)
In this model, we have fixed all the coefficients of the linear growth functions:
model4 <-
'
i =~ 1*UPDRS_Part_I_Summary_Score_Baseline + 1*UPDRS_Part_I_Summary_Score_Month_03 +
1*UPDRS_Part_I_Summary_Score_Month_06 + 1*UPDRS_Part_I_Summary_Score_Month_09 +
1*UPDRS_Part_I_Summary_Score_Month_12 + 1*UPDRS_Part_I_Summary_Score_Month_18 +
1*UPDRS_Part_I_Summary_Score_Month_24 +
1*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline +
1*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_03 +
1*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_06 +
1*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_09 +
1*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_12 +
1*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_18 +
1*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_24 +
1*UPDRS_Part_III_Summary_Score_Baseline + 1*UPDRS_Part_III_Summary_Score_Month_03 +
1*UPDRS_Part_III_Summary_Score_Month_06 + 1*UPDRS_Part_III_Summary_Score_Month_09 +
1*UPDRS_Part_III_Summary_Score_Month_12 + 1*UPDRS_Part_III_Summary_Score_Month_18 +
1*UPDRS_Part_III_Summary_Score_Month_24 +
1*X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Baseline +
1*X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_06 +
1*X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_12 +
1*X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_24 +
1*X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline +
1*X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_06 +
1*X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_12 +
1*X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_24
s =~ 0*UPDRS_Part_I_Summary_Score_Baseline + 1*UPDRS_Part_I_Summary_Score_Month_03 +
2*UPDRS_Part_I_Summary_Score_Month_06 + 3*UPDRS_Part_I_Summary_Score_Month_09 +
4*UPDRS_Part_I_Summary_Score_Month_12 + 5*UPDRS_Part_I_Summary_Score_Month_18 +
6*UPDRS_Part_I_Summary_Score_Month_24 +
0*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline +
1*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_03 +
2*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_06 +
3*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_09 +
4*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_12 +
5*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_18 +
6*UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_24 +
0*UPDRS_Part_III_Summary_Score_Baseline + 1*UPDRS_Part_III_Summary_Score_Month_03 +
2*UPDRS_Part_III_Summary_Score_Month_06 + 3*UPDRS_Part_III_Summary_Score_Month_09 +
4*UPDRS_Part_III_Summary_Score_Month_12 + 5*UPDRS_Part_III_Summary_Score_Month_18 +
6*UPDRS_Part_III_Summary_Score_Month_24 +
0*X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Baseline +
2*X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_06 +
4*X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_12 +
6*X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_24 +
0*X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline +
2*X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_06 +
4*X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_12 +
6*X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_24
'
fit4 <- growth(model4, data=myData)
summary(fit4)
parameterEstimates(fit4) # extracts the values of the estimated parameters, the standard errors,
# the z-values, the standardized parameter values, and returns a data frame
fitted(fit4) # return the model-implied (fitted) covariance matrix (and mean vector) of a fitted model
# resid() function return (unstandardized) residuals of a fitted model including the difference between
# the observed and implied covariance matrix and mean vector
resid(fit4)
=='"`UNIQ--h-1--QINU`"'Measures of model quality (Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA))==
# report the fit measures as a signature vector: Comparative Fit Index (CFI), Root Mean Square Error of
# Approximation (RMSEA)
fitMeasures(fit4, c("cfi", "rmsea", "srmr"))
===='"`UNIQ--h-2--QINU`"'Comparative Fit Index====
(CFI) is an incremental measure directly based on the non-centrality measure. If d = χ2(df) where df are the degrees of freedom of the model, the Comparative Fit Index is:
\begin{equation}
\frac{(Null Model)-d(Proposed Model)}{d(Null Model)}.
\end{equation}
$0≤CFI≤1$ (by definition). It is interpreted as:
<li>$CFI<0.9$ - model fitting is poor. </li>
<li>$0.9≤CFI≤0.95$ is considered marginal, </li>
<li>$CFI>0.95$ is good. </li>
CFI is a relative index of model fit – it compare the fit of your model to the fit of (the worst) fitting null model.
===='"`UNIQ--h-3--QINU`"'Root Mean Square Error of Approximation====
(RMSEA) - “Ramsey”
An absolute measure of fit based on the non-centrality parameter:
$\sqrt{\frac{X^2-df}{df×(N - 1)}}$,
where N the sample size and df the degrees of freedom of the model. If χ<sup>2</sup> < df, then the RMSEA∶=0. It has a penalty for complexity via the chi square to df ratio. The RMSEA is a popular measure of model fit.
<li>RMSEA < 0.01, excellent, </li>
<li>RMSEA < 0.05, good </li>
<li>RMSEA > 0.10 cutoff for poor fitting models</li>
===='"`UNIQ--h-4--QINU`"'Standardized Root Mean Square Residual====
(SRMR) is an absolute measure of fit defined as the standardized difference between the observed correlation and the predicted correlation. A value of zero indicates perfect fit. The SRMR has no penalty for model complexity. SRMR <0.08 is considered a good fit.
# inspect the model results (report parameter table)
inspect(fit4)
#install.packages("semTools")
# library("semTools")
<b><u>A Simpler Model (fit5)</u></b>
model5 <- '
# intercept and slope with fixed coefficients
i =~ UPDRS_Part_I_Summary_Score_Baseline + UPDRS_Part_I_Summary_Score_Month_03 + UPDRS_Part_I_Summary_Score_Month_24
s =~ 0*UPDRS_Part_I_Summary_Score_Baseline + 1*UPDRS_Part_I_Summary_Score_Month_03 + 6*UPDRS_Part_I_Summary_Score_Month_24
# regressions
i ~ R_fusiform_gyrus_Volume + Weight + ResearchGroup + Age + chr12_rs34637584_GT
s ~ R_fusiform_gyrus_Volume + Weight + ResearchGroup + Age + chr12_rs34637584_GT
# time-varying covariates
UPDRS_Part_I_Summary_Score_Baseline ~ Weight
UPDRS_Part_I_Summary_Score_Month_03 ~ ResearchGroup
UPDRS_Part_I_Summary_Score_Month_24 ~ Age
'
fit5 <- growth(model5, data=myData)
summary(fit5); fitMeasures(fit5, c("cfi", "rmsea", "srmr"))
parameterEstimates(fit5) # extracts the values of the estimated parameters, the standard errors,
# the z-values, the standardized parameter values, and returns a data frame
lavaan (0.5-18) converged normally after 99 iterations
Number of observations 661
Estimator ML
Minimum Function Test Statistic 3.703
Degrees of freedom 1
P-value (Chi-square) 0.054
Parameter estimates:
Information Expected
Standard Errors Standard
Estimate Std.err Z-value P(>|z|)
Latent variables:
i =~
UPDRS_P_I_S_S 1.000
UPDRS_P_I_S_S 1.074
UPDRS_P_I_S_S 1.172
s =~
UPDRS_P_I_S_S 0.000
UPDRS_P_I_S_S 1.000
UPDRS_P_I_S_S 6.000
Regressions:
i ~
R_fsfrm_gyr_V 0.000
Weight 0.003
ResearchGroup -0.880
Age -0.009
c12_34637584_ -0.907
s ~
R_fsfrm_gyr_V -0.000
Weight -0.000
ResearchGroup -0.084
Age 0.002
c12_34637584_ -0.047
UPDRS_Part_I_Summary_Score_Baseline ~
Weight -0.000
UPDRS_Part_I_Summary_Score_Month_03 ~
ResearchGroup 0.693
UPDRS_Part_I_Summary_Score_Month_24 ~
Age -0.002
Covariances:
i ~~
s 0.074
Intercepts:
UPDRS_P_I_S_S 0.000
UPDRS_P_I_S_S 0.000
UPDRS_P_I_S_S 0.000
i 1.633
s -0.023
Variances:
UPDRS_P_I_S_S 1.017
UPDRS_P_I_S_S 1.093
UPDRS_P_I_S_S 2.993
i 1.019
s -0.025
<b>cfi rmsea srmr</b>
<b>0.996 0.064 0.008</b>
fitted(fit5) # return the model-implied (fitted) covariance matrix (and mean vector) of a fitted model
# write.table(fitted(fit5), file="C:\\Users\\Dinov\\Desktop\\test1.txt")
# resid() function return (unstandardized) residuals of a fitted model including the difference between
# the observed and implied covariance matrix and mean vector
resid(fit5)
# report the fit measures as a signature vector
fitMeasures(fit5, c("cfi", "rmsea", "srmr")) # comparative fit index (CFI)
# inspect the model results (report parameter table)
inspect(fit5)
<b>Note:</b> See discussion of SEM modeling pros/cons <sup>2</sup>.
==='"`UNIQ--h-5--QINU`"'Generalized Estimating Equation (GEE) Modeling===
Generalized Estimating Equations (GEE) modeling is used for analyzing data with the following characteristics:
(1) the observations within a group may be correlated, (2) observations in separate clusters are independent, (3) a monotone transformation of the expectation is linearly related to the explanatory variables, and (4) the variance is a function of the expectation. The expectation (#3) and the variance (# 4) are conditional given group-level or individual-level covariates.
GEE is applied to handle correlated discrete and continuous outcome variables. For the outcome variables, it only requires specification of the first 2 moments and correlation among them. The goal is to estimate fixed parameters without specifying their joint distribution. The correlation is specified by one of these 4 alternatives (which is specified in the R call: geeglm(outcome ~ center + treat + sex + baseline + age, data = respiratory, family = "binomial", id = id, <b>corstr = " exchangeable"</b>, scale.fix = TRUE):
<center>[[Image:SMHS_BigDataBigSci8.png|300px]]</center>
==='"`UNIQ--h-6--QINU`"'Respiratory Illness GEE R example===
This example is based on a data set on respiratory illness <sup>4</sup> and the <b>geepack</b> package. The data is from a clinical study of the treatment effects on patients with respiratory illness. N=111 patients from 2 clinical centers randomized to receive either placebo or active treatments. 4 temporal examinations assessed the <b>respiratory state</b> of patients as good (=1) or poor (=0). Explanatory variables characterizing a patient were: <b>center</b> (1,2), treatment (A=active, P=placebo), <b>sex</b> (M=male, F=female), <b>age</b> (in years) at baseline. The values of the covariates were constant for the repeated elementary observations on each patient.
<b>Table 1</b> shows the number of patients for the response patterns across the 4 visits split by baseline-status and treatment. Baseline respiratory status = 0 appear to have either low or high number of positive responses. Baseline respiratory status = 1 tend to respond positively. <b>Table 2</b> describes the distribution of the number of positive responses per patient for sex and center.
# library("geepack")
<b>Table 1</b>: Distribution of patients for <b>different response patterns</b> classified by <b>baseline-respiratory</b> response and <b>treatment</b>. The patterns are ordered according to increasing numbers of positive responses.
<mark>!!!!!Insert tables here</mark>
<b>Table 2</b>: Distribution of patients for the number of positive responses across the 4 visits for <b>Sex</b> and <b>Center</b>.
<b>Figure 1</b> shows a plot of age against the proportion of positive responses for each patient. It indicates a quadratic relationship between the proportions and the age. Fitting a logistic model to the data (which would be appropriate if there were <i>no time effects</i> and <i>no spread in the response probabilities</i> for patients with the same covariate values).
# install.packages("geepack")
library("geepack")
# data include a clinical trial of 111 patients with respiratory illness from two different clinics were randomized to receive either
# placebo (P) or an active (A) treatment. Patients were examined at baseline and at four visits during treatment.
# At each examination, respiratory status (categorized as 1 = good, 0 = poor)
data("respiratory")
head(respiratory)
myData <- respiratory
<center>head(myData)
{| class="wikitable" style="text-align:center; " border="1"
|-
|||Center||ID||Treat||Sex||Age||Baseline||Visit||Outcome
|-
|1 ||1||1||P||M||46||0||1||0
|-
|2 ||1||1||P||M||46||0||2||0
|-
|3 ||1||1||P||M||46||0||3||0
|-
|4 ||1||1||P||M||46||0||4||0
|-
|5||1||2||P||M||28||0||1||0
|-
|6||1||2||P||M||28||0||2||0
|}
</center>
# Get proportions of positive responses
responses <- factor(myData$\$$outcome, labels = c("OutcomePositive", "OutcomeNegative"))
data.frame <- data.frame(responses, myData$\$$age)
head(data.frame)
tab <- prop.table(table(data.frame), 1); tab # compute proportions
sum(tab[1,]) # check proportions (sums to 1.0)?
prop <- tab[1,] # save the proportions of positive responses for each patient
plot(as.numeric(dimnames(tab)$\$$myData.age), tab[1,], xlab = "Age", ylab = "Proportion of Positive Outcomes")
# dimnames(tab) # to see/inspect positive/negative outcomes
x <- as.numeric(dimnames(tab)$\$$myData.age) poly <- loess( prop ~ x) # fit a Local Polynomial Regression Fitting plot(x, prop) lines(predict(poly), col='red', lwd=2)
smoothingSpline <- smooth.spline(x, prop, spar=0.6)
plot(x, prop)
lines(smoothingSpline, col='red', lwd=1.5)
smoothPolySpline <- smooth.spline(x, predict(poly), spar=0.6)
lines(smoothPolySpline, col='blue', lwd=2)
legend("topright", inset=.05, title="Polynomial regression models", c("Raw Poly","Smooth Poly"), fill=c('red', 'blue'), horiz=TRUE)
model.glm <- glm(outcome ~ baseline + center + sex + treat + age + I(age^2), data = respiratory, family = binomial)
summary(model.glm)
| Min | 1Q | Median | 3Q | Max |
| -2.5951 | -0.9108 | 0.4034 | 0.8336 | 2.0951 |
| Estimate | Std. Error | z value | z|)$ | |
| (Intercept) | 3.3579727 | 1.0285292 | 3.265 | 0.0011 ** |
| baseline | 1.8850421 | 0.2482959 | 7.592 | 3.15e-14 *** |
| center | 0.5099244 | 0.2453982 | 2.078 | 0.0377 * |
| sexM | -0.4510595 | 0.3166570 | -1.424 | 0.1543 |
| Treatp | -1.3231587 | 0.2431603 | -5.442 | 5.28e-08 *** |
| age | -0.2072815 | 0.0472538 | -4.387 | 1.15e-05 *** |
| I(age^2) | 0.0025650 | 0.0006324 | 4.056 | 4.99e-05 *** |
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 609.41 on 443 degrees of freedom
Residual deviance: 468.62 on 437 degrees of freedom
AIC: 482.62
The correlation matrix of the of the outcome measures across visits is shown in Table 3.
attach(myData) mat1 <- matrix(c(outcome[visit==1], outcome [visit==2], outcome [visit==3],
outcome[visit==4]), ncol = 4)
cor(mat1)
Table 3: Correlation matrix for the outcome measurements at different visits.
| [,1] | [,2] | [,3] | [,4] | |
| [,1] | 1.0000000 | 0.5087944 | 0.4431438 | 0.5139016 |
| [,2] | 0.5087944 | 1.0000000 | 0.5821877 | 0.5301611 |
| [,3] | 0.4431438 | 0.5821877 | 1.0000000 | 0.5871276 |
| [,4] | 0.5139016 | 0.5301611 | 0.5871276 | 1.0000000 |
# We can also examine for multicollinearity problem, using the correlation matrix for X cor(model.matrix(model.glm)[,-1])
# GEE modeling: R function arguments/options
gee.model1 <- geeglm(outcome ~ center + treat + sex + baseline + age, data = respiratory, family = "binomial", id = id, corstr = "exchangeable", scale.fix = TRUE)
# The column labeled Wald in the summary table is the square of the z-statistic. The reported p-values are the # upper tailed probabilities from a chisq1 distribution and test whether the true parameter value ≠0. summary(gee.model1)
# To test the effect of treatment using anova() gee.model1 <- geeglm(outcome ~ center + treat + sex + baseline + age, data = respiratory, family=binomial(link="logit"), id = id, corstr = "exchangeable", std.err="san.se") gee.model2 <- geeglm(outcome ~ center + sex + baseline + age, data = respiratory, family=binomial(link="logit"), id=id, corstr = "exchangeable", std.err="san.se") anova(gee.model1, gee.model2)
# To test whether a categorical predictor with more than two levels should be retained in a GEE model we need # to test the entire set of dummy variables simultaneously as a single construct. # The geepack package provides a method for the anova function for a multivariate Wald test # When the anova function is applied to a single geeglm object it returns sequential Wald tests for # individual predictors with the tests carried out in the order the predictors are listed in the model formula. anova(gee.model1)
3http://www.jstatsoft.org/v15/i02/
4https://books.google.com/books?id=mdEqBgAAQBAJ
PD GEE example
This example used the PPMI/PD data to show GEE analysis.
# 05_PPMI_top_UPDRS_Integrated_LongFormat1.csv
longData <- read.csv("https://umich.instructure.com/files/330397/download?download_frd=1",header=TRUE)
# library("geepack")
# Data Elements: FID_IID L_insular_cortex_ComputeArea L_insular_cortex_Volume R_insular_cortex_ComputeArea R_insular_cortex_Volume 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_Volume Sex Weight ResearchGroup Age chr12_rs34637584_GT chr17_rs11868035_GT chr17_rs11012_GT chr17_rs393152_GT chr17_rs12185268_GT chr17_rs199533_GT UPDRS_part_I UPDRS_part_II UPDRS_part_III time_visit
dim(longData)
data1 = na.omit(longData) attach(data1) ControlGroup <- ifelse(ResearchGroup == "Control", 1, 0)
# these calculations take a long time!!! # if you get “Error in geese.fit(xx, yy, id, offset, soffset, w, waves = waves, zsca, : # nrow(zsca) and length(y) not match” – this indicates some of the variables are of different lengths # if you get “glm.fit: algorithm did not converge” – see this discussion: http://goo.gl/lrjBjB
gee.model0 <- geeglm(ControlGroup ~ L_insular_cortex_ComputeArea+L_insular_cortex_Volume+ Sex + Weight + Age + chr17_rs11012_GT + chr17_rs199533_GT + UPDRS_part_I + UPDRS_part_II + time_visit, data = data1, family=binomial(link="logit"), id = FID_IID, corstr = "unstructured", std.err="san.se")
gee.model1 <- geeglm(ControlGroup ~ L_insular_cortex_ComputeArea+L_insular_cortex_Volume+ R_putamen_ComputeArea + R_putamen_Volume + Sex + Weight + Age + chr17_rs11012_GT + chr17_rs199533_GT + UPDRS_part_I + UPDRS_part_II + time_visit, data = data1, family=binomial(link="logit"), id = FID_IID, corstr = "unstructured", std.err="san.se")
# compare 2 gee models # anova(gee.model0,gee.model1)
# you can try the “family = poisson(link = "log")” model for the ResearchGroup response, as well
gee.model2 <- geeglm(ControlGroup ~ L_insular_cortex_ComputeArea+L_insular_cortex_Volume+R_insular_cortex_ComputeArea+ R_insular_cortex_Volume +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_Volume + Sex + Weight + Age + chr12_rs34637584_GT + chr17_rs11868035_GT + chr17_rs11012_GT + chr17_rs393152_GT + chr17_rs12185268_GT + chr17_rs199533_GT + UPDRS_part_I + UPDRS_part_II + time_visit, data = data1, family=binomial(link="logit"), id = FID_IID, corstr = "unstructured", std.err="san.se")
Remember that we do not interpret GEE coefficients as relating to individuals – GEE models are marginal models and the conclusions drawn are interpreted as population-based. Also, the time element in the model (time_visit) is just another controlling factor. The effect-sizes (betas) associated with each variable/predictor represent the slopes associated with the corresponding covariate, while holding time constant. If we need to examine interactions (e.g., Weight change over Time), we need to include an interaction term in model: (i.e. + Weight*time_visit).
summary (gee.model2)
# Individual Wald test and confidence intervals for each covariate predictors2 <- coef(summary(gee.model2)) CI2 <- with(as.data.frame(predictors2), cbind(lwr=Estimate-1.96*Std.err, est=Estimate, upr=Estimate+1.96*Std.err)) rownames(CI2) <- rownames(predictors2) CI2
Appendix
SEM References
GEE References
See also
- Back to Model-based Analytics
- Structural Equation Modeling (SEM)
- Next Section: Generalized Estimating Equation (GEE) Modeling
- SOCR Home page: http://www.socr.umich.edu
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