cpg2 <- data.frame(matrix(array(0), nrow = nrow(cpg1)*4, ncol = 4))
names(cpg2) <- c("ID", "Prod", "Rating", "Cond")
for(i in 1:nrow(cpg1)) {
indx <- ((i-1)*4 + 1):(i*4)
cpg2$ID[indx] <- cpg1$ResponseId[i]
cpg2$Prod[indx[1]] <- 1
cpg2$Rating[indx[1]] <- ifelse(!is.na(cpg1$Q1[i]), cpg1$Q1[i], cpg1$Q26[i])
cpg2$Cond[indx[1]] <- ifelse(!is.na(cpg1$Q1[i]), 0, 1)
cpg2$Prod[indx[2]] <- 2
cpg2$Rating[indx[2]] <- ifelse(!is.na(cpg1$Q27[i]), cpg1$Q27[i], cpg1$Q28[i])
cpg2$Cond[indx[2]] <- ifelse(!is.na(cpg1$Q27[i]), 0, 1)
cpg2$Prod[indx[3]] <- 3
cpg2$Rating[indx[3]] <- ifelse(!is.na(cpg1$Q29[i]), cpg1$Q29[i], cpg1$Q30[i])
cpg2$Cond[indx[3]] <- ifelse(!is.na(cpg1$Q29[i]), 0, 1)
cpg2$Prod[indx[4]] <- 4
cpg2$Rating[indx[4]] <- ifelse(!is.na(cpg1$Q31[i]), cpg1$Q31[i], cpg1$Q32[i])
cpg2$Cond[indx[4]] <- ifelse(!is.na(cpg1$Q31[i]), 0, 1)
}
cpg2$Prod <- factor(cpg2$Prod)
cpg2$Cond <- factor(cpg2$Cond)Analyze data
library(brms)## Loading required package: Rcpp## Loading 'brms' package (version 2.22.0). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').##
## Attaching package: 'brms'## The following object is masked from 'package:stats':
##
## arcpgmod1 <- brm(Rating ~ (1|ID) + Prod + Cond, data = cpg2)## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## using C compiler: ‘Apple clang version 17.0.0 (clang-1700.4.4.1)’
## using SDK: ‘MacOSX26.1.sdk’
## clang -arch arm64 -std=gnu2x -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DUSE_STANC3 -DSTRICT_R_HEADERS -DBOOST_PHOENIX_NO_VARIADIC_EXPRESSION -D_HAS_AUTO_PTR_ETC=0 -include '/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp:22:
## In file included from /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/RcppEigen/include/Eigen/Core:19:
## /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:679:10: fatal error: 'cmath' file not found
## 679 | #include <cmath>
## | ^~~~~~~
## 1 error generated.
## make: *** [foo.o] Error 1## Start sampling##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
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## Chain 4:summary(cpgmod1)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: Rating ~ (1 | ID) + Prod + Cond
## Data: cpg2 (Number of observations: 492)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 123)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.72 0.10 0.54 0.92 1.00 1408 2348
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 4.82 0.16 4.51 5.13 1.00 4748 3106
## Prod2 -0.73 0.18 -1.07 -0.37 1.00 5950 3270
## Prod3 -0.08 0.18 -0.44 0.26 1.00 5970 3410
## Prod4 0.03 0.18 -0.30 0.38 1.00 5964 2807
## Cond1 -0.26 0.14 -0.52 0.02 1.00 6599 2847
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 1.41 0.05 1.32 1.52 1.00 3216 3335
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).