ANLY505 - Intro to Stan Problem Set #1
MENG ZHANG
2019-06-10
Intro to STAN Homework Set #1
After our Intro to Stan lecture I think it would be valuable to have you go through a similar exercise. Let’s test a second research question.
Research question: Is sea ice extent declining in the Southern Hemisphere over time? Is the same pattern happening in the Antarctic as in the Arctic? Fit a Stan model to find out!
Make sure you follow the steps we used in class.
What do your Stan model results indicate so far?
The Stan model results indicates that the sea ice extent is increasing in the Southern Hemisphere over time.
library(rstan)## Loading required package: ggplot2## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang## Loading required package: StanHeaders## rstan (Version 2.18.2, GitRev: 2e1f913d3ca3)## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)## For improved execution time, we recommend calling
## Sys.setenv(LOCAL_CPPFLAGS = '-march=native')
## although this causes Stan to throw an error on a few processors.library(gdata)## gdata: Unable to locate valid perl interpreter
## gdata:
## gdata: read.xls() will be unable to read Excel XLS and XLSX files
## gdata: unless the 'perl=' argument is used to specify the location
## gdata: of a valid perl intrpreter.
## gdata:
## gdata: (To avoid display of this message in the future, please
## gdata: ensure perl is installed and available on the executable
## gdata: search path.)## gdata: Unable to load perl libaries needed by read.xls()
## gdata: to support 'XLX' (Excel 97-2004) files.## ## gdata: Unable to load perl libaries needed by read.xls()
## gdata: to support 'XLSX' (Excel 2007+) files.## ## gdata: Run the function 'installXLSXsupport()'
## gdata: to automatically download and install the perl
## gdata: libaries needed to support Excel XLS and XLSX formats.##
## Attaching package: 'gdata'## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## The following object is masked from 'package:base':
##
## startsWithlibrary(bayesplot)## This is bayesplot version 1.7.0## - Online documentation and vignettes at mc-stan.org/bayesplot## - bayesplot theme set to bayesplot::theme_default()## * Does _not_ affect other ggplot2 plots## * See ?bayesplot_theme_set for details on theme settingLoad and Inspect Data
seaice <- read.csv("seaice.csv", stringsAsFactors = F)
head(seaice)## ï..year extent_north extent_south
## 1 1979 12.328 11.700
## 2 1980 12.337 11.230
## 3 1981 12.127 11.435
## 4 1982 12.447 11.640
## 5 1983 12.332 11.389
## 6 1984 11.910 11.454colnames(seaice) <- c("year", "extent_north", "extent_south")Plot the data
plot(extent_south ~ year, pch = 20, data = seaice)
Run a general linear model using lm()
lm1 <- lm(extent_south ~ year, data = seaice)
summary(lm1)##
## Call:
## lm(formula = extent_south ~ year, data = seaice)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.23372 -0.18142 0.01587 0.18465 0.88814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -14.199551 10.925576 -1.300 0.2018
## year 0.012953 0.005468 2.369 0.0232 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3843 on 37 degrees of freedom
## Multiple R-squared: 0.1317, Adjusted R-squared: 0.1082
## F-statistic: 5.611 on 1 and 37 DF, p-value: 0.02318plot(extent_south ~ year, pch = 20, data = seaice)
abline(lm1, col = 2, lty = 2, lw = 3)
Prepare the data, re-run the lm() and extract summary statistics
x <- I(seaice$year - 1978)
y <- seaice$extent_south
N <- length(seaice$year)
lm1 <- lm(y ~ x)
summary(lm1)##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.23372 -0.18142 0.01587 0.18465 0.88814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.421555 0.125490 91.015 <2e-16 ***
## x 0.012953 0.005468 2.369 0.0232 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3843 on 37 degrees of freedom
## Multiple R-squared: 0.1317, Adjusted R-squared: 0.1082
## F-statistic: 5.611 on 1 and 37 DF, p-value: 0.02318lm_alpha <- summary(lm1)$coeff[1]
lm_beta <- summary(lm1)$coeff[2]
lm_sigma <- sigma(lm1)
stan_data <- list(N = N, x = x, y = y)Write the Stan model
write("// Stan model for simple linear regression
data {
int < lower = 1 > N; // Sample size
vector[N] x; // Predictor
vector[N] y; // Outcome
}
parameters {
real alpha; // Intercept
real beta; // Slope (regression coefficients)
real < lower = 0 > sigma; // Error SD
}
model {
y ~ normal(alpha + x * beta , sigma);
}
generated quantities {
} // The posterior predictive distribution",
"stan_model1.stan")
stanc("stan_model1.stan")## $status
## [1] TRUE
##
## $model_cppname
## [1] "model52cc5d507ad7_stan_model1"
##
## $cppcode
## [1] "// Code generated by Stan version 2.18.1\n\n#include <stan/model/model_header.hpp>\n\nnamespace model52cc5d507ad7_stan_model1_namespace {\n\nusing std::istream;\nusing std::string;\nusing std::stringstream;\nusing std::vector;\nusing stan::io::dump;\nusing stan::math::lgamma;\nusing stan::model::prob_grad;\nusing namespace stan::math;\n\nstatic int current_statement_begin__;\n\nstan::io::program_reader prog_reader__() {\n stan::io::program_reader reader;\n reader.add_event(0, 0, \"start\", \"model52cc5d507ad7_stan_model1\");\n reader.add_event(22, 20, \"end\", \"model52cc5d507ad7_stan_model1\");\n return reader;\n}\n\nclass model52cc5d507ad7_stan_model1 : public prob_grad {\nprivate:\n int N;\n vector_d x;\n vector_d y;\npublic:\n model52cc5d507ad7_stan_model1(stan::io::var_context& context__,\n std::ostream* pstream__ = 0)\n : prob_grad(0) {\n ctor_body(context__, 0, pstream__);\n }\n\n model52cc5d507ad7_stan_model1(stan::io::var_context& context__,\n unsigned int random_seed__,\n std::ostream* pstream__ = 0)\n : prob_grad(0) {\n ctor_body(context__, random_seed__, pstream__);\n }\n\n void ctor_body(stan::io::var_context& context__,\n unsigned int random_seed__,\n std::ostream* pstream__) {\n typedef double local_scalar_t__;\n\n boost::ecuyer1988 base_rng__ =\n stan::services::util::create_rng(random_seed__, 0);\n (void) base_rng__; // suppress unused var warning\n\n current_statement_begin__ = -1;\n\n static const char* function__ = \"model52cc5d507ad7_stan_model1_namespace::model52cc5d507ad7_stan_model1\";\n (void) function__; // dummy to suppress unused var warning\n size_t pos__;\n (void) pos__; // dummy to suppress unused var warning\n std::vector<int> vals_i__;\n std::vector<double> vals_r__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n (void) DUMMY_VAR__; // suppress unused var warning\n\n // initialize member variables\n try {\n current_statement_begin__ = 4;\n context__.validate_dims(\"data initialization\", \"N\", \"int\", context__.to_vec());\n N = int(0);\n vals_i__ = context__.vals_i(\"N\");\n pos__ = 0;\n N = vals_i__[pos__++];\n current_statement_begin__ = 5;\n validate_non_negative_index(\"x\", \"N\", N);\n context__.validate_dims(\"data initialization\", \"x\", \"vector_d\", context__.to_vec(N));\n validate_non_negative_index(\"x\", \"N\", N);\n x = vector_d(static_cast<Eigen::VectorXd::Index>(N));\n vals_r__ = context__.vals_r(\"x\");\n pos__ = 0;\n size_t x_i_vec_lim__ = N;\n for (size_t i_vec__ = 0; i_vec__ < x_i_vec_lim__; ++i_vec__) {\n x[i_vec__] = vals_r__[pos__++];\n }\n current_statement_begin__ = 6;\n validate_non_negative_index(\"y\", \"N\", N);\n context__.validate_dims(\"data initialization\", \"y\", \"vector_d\", context__.to_vec(N));\n validate_non_negative_index(\"y\", \"N\", N);\n y = vector_d(static_cast<Eigen::VectorXd::Index>(N));\n vals_r__ = context__.vals_r(\"y\");\n pos__ = 0;\n size_t y_i_vec_lim__ = N;\n for (size_t i_vec__ = 0; i_vec__ < y_i_vec_lim__; ++i_vec__) {\n y[i_vec__] = vals_r__[pos__++];\n }\n\n // validate, data variables\n current_statement_begin__ = 4;\n check_greater_or_equal(function__,\"N\",N,1);\n current_statement_begin__ = 5;\n current_statement_begin__ = 6;\n // initialize data variables\n\n\n // validate transformed data\n\n // validate, set parameter ranges\n num_params_r__ = 0U;\n param_ranges_i__.clear();\n current_statement_begin__ = 10;\n ++num_params_r__;\n current_statement_begin__ = 11;\n ++num_params_r__;\n current_statement_begin__ = 12;\n ++num_params_r__;\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());\n // Next line prevents compiler griping about no return\n throw std::runtime_error(\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\");\n }\n }\n\n ~model52cc5d507ad7_stan_model1() { }\n\n\n void transform_inits(const stan::io::var_context& context__,\n std::vector<int>& params_i__,\n std::vector<double>& params_r__,\n std::ostream* pstream__) const {\n stan::io::writer<double> writer__(params_r__,params_i__);\n size_t pos__;\n (void) pos__; // dummy call to supress warning\n std::vector<double> vals_r__;\n std::vector<int> vals_i__;\n\n if (!(context__.contains_r(\"alpha\")))\n throw std::runtime_error(\"variable alpha missing\");\n vals_r__ = context__.vals_r(\"alpha\");\n pos__ = 0U;\n context__.validate_dims(\"initialization\", \"alpha\", \"double\", context__.to_vec());\n double alpha(0);\n alpha = vals_r__[pos__++];\n try {\n writer__.scalar_unconstrain(alpha);\n } catch (const std::exception& e) { \n throw std::runtime_error(std::string(\"Error transforming variable alpha: \") + e.what());\n }\n\n if (!(context__.contains_r(\"beta\")))\n throw std::runtime_error(\"variable beta missing\");\n vals_r__ = context__.vals_r(\"beta\");\n pos__ = 0U;\n context__.validate_dims(\"initialization\", \"beta\", \"double\", context__.to_vec());\n double beta(0);\n beta = vals_r__[pos__++];\n try {\n writer__.scalar_unconstrain(beta);\n } catch (const std::exception& e) { \n throw std::runtime_error(std::string(\"Error transforming variable beta: \") + e.what());\n }\n\n if (!(context__.contains_r(\"sigma\")))\n throw std::runtime_error(\"variable sigma missing\");\n vals_r__ = context__.vals_r(\"sigma\");\n pos__ = 0U;\n context__.validate_dims(\"initialization\", \"sigma\", \"double\", context__.to_vec());\n double sigma(0);\n sigma = vals_r__[pos__++];\n try {\n writer__.scalar_lb_unconstrain(0,sigma);\n } catch (const std::exception& e) { \n throw std::runtime_error(std::string(\"Error transforming variable sigma: \") + e.what());\n }\n\n params_r__ = writer__.data_r();\n params_i__ = writer__.data_i();\n }\n\n void transform_inits(const stan::io::var_context& context,\n Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,\n std::ostream* pstream__) const {\n std::vector<double> params_r_vec;\n std::vector<int> params_i_vec;\n transform_inits(context, params_i_vec, params_r_vec, pstream__);\n params_r.resize(params_r_vec.size());\n for (int i = 0; i < params_r.size(); ++i)\n params_r(i) = params_r_vec[i];\n }\n\n\n template <bool propto__, bool jacobian__, typename T__>\n T__ log_prob(vector<T__>& params_r__,\n vector<int>& params_i__,\n std::ostream* pstream__ = 0) const {\n\n typedef T__ local_scalar_t__;\n\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n (void) DUMMY_VAR__; // suppress unused var warning\n\n T__ lp__(0.0);\n stan::math::accumulator<T__> lp_accum__;\n\n try {\n // model parameters\n stan::io::reader<local_scalar_t__> in__(params_r__,params_i__);\n\n local_scalar_t__ alpha;\n (void) alpha; // dummy to suppress unused var warning\n if (jacobian__)\n alpha = in__.scalar_constrain(lp__);\n else\n alpha = in__.scalar_constrain();\n\n local_scalar_t__ beta;\n (void) beta; // dummy to suppress unused var warning\n if (jacobian__)\n beta = in__.scalar_constrain(lp__);\n else\n beta = in__.scalar_constrain();\n\n local_scalar_t__ sigma;\n (void) sigma; // dummy to suppress unused var warning\n if (jacobian__)\n sigma = in__.scalar_lb_constrain(0,lp__);\n else\n sigma = in__.scalar_lb_constrain(0);\n\n\n // transformed parameters\n\n\n\n // validate transformed parameters\n\n const char* function__ = \"validate transformed params\";\n (void) function__; // dummy to suppress unused var warning\n\n // model body\n\n current_statement_begin__ = 16;\n lp_accum__.add(normal_log<propto__>(y, add(alpha,multiply(x,beta)), sigma));\n\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());\n // Next line prevents compiler griping about no return\n throw std::runtime_error(\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\");\n }\n\n lp_accum__.add(lp__);\n return lp_accum__.sum();\n\n } // log_prob()\n\n template <bool propto, bool jacobian, typename T_>\n T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,\n std::ostream* pstream = 0) const {\n std::vector<T_> vec_params_r;\n vec_params_r.reserve(params_r.size());\n for (int i = 0; i < params_r.size(); ++i)\n vec_params_r.push_back(params_r(i));\n std::vector<int> vec_params_i;\n return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);\n }\n\n\n void get_param_names(std::vector<std::string>& names__) const {\n names__.resize(0);\n names__.push_back(\"alpha\");\n names__.push_back(\"beta\");\n names__.push_back(\"sigma\");\n }\n\n\n void get_dims(std::vector<std::vector<size_t> >& dimss__) const {\n dimss__.resize(0);\n std::vector<size_t> dims__;\n dims__.resize(0);\n dimss__.push_back(dims__);\n dims__.resize(0);\n dimss__.push_back(dims__);\n dims__.resize(0);\n dimss__.push_back(dims__);\n }\n\n template <typename RNG>\n void write_array(RNG& base_rng__,\n std::vector<double>& params_r__,\n std::vector<int>& params_i__,\n std::vector<double>& vars__,\n bool include_tparams__ = true,\n bool include_gqs__ = true,\n std::ostream* pstream__ = 0) const {\n typedef double local_scalar_t__;\n\n vars__.resize(0);\n stan::io::reader<local_scalar_t__> in__(params_r__,params_i__);\n static const char* function__ = \"model52cc5d507ad7_stan_model1_namespace::write_array\";\n (void) function__; // dummy to suppress unused var warning\n // read-transform, write parameters\n double alpha = in__.scalar_constrain();\n double beta = in__.scalar_constrain();\n double sigma = in__.scalar_lb_constrain(0);\n vars__.push_back(alpha);\n vars__.push_back(beta);\n vars__.push_back(sigma);\n\n // declare and define transformed parameters\n double lp__ = 0.0;\n (void) lp__; // dummy to suppress unused var warning\n stan::math::accumulator<double> lp_accum__;\n\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n (void) DUMMY_VAR__; // suppress unused var warning\n\n try {\n\n\n\n // validate transformed parameters\n\n // write transformed parameters\n if (include_tparams__) {\n }\n if (!include_gqs__) return;\n // declare and define generated quantities\n\n\n\n // validate generated quantities\n\n // write generated quantities\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());\n // Next line prevents compiler griping about no return\n throw std::runtime_error(\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\");\n }\n }\n\n template <typename RNG>\n void write_array(RNG& base_rng,\n Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,\n Eigen::Matrix<double,Eigen::Dynamic,1>& vars,\n bool include_tparams = true,\n bool include_gqs = true,\n std::ostream* pstream = 0) const {\n std::vector<double> params_r_vec(params_r.size());\n for (int i = 0; i < params_r.size(); ++i)\n params_r_vec[i] = params_r(i);\n std::vector<double> vars_vec;\n std::vector<int> params_i_vec;\n write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);\n vars.resize(vars_vec.size());\n for (int i = 0; i < vars.size(); ++i)\n vars(i) = vars_vec[i];\n }\n\n static std::string model_name() {\n return \"model52cc5d507ad7_stan_model1\";\n }\n\n\n void constrained_param_names(std::vector<std::string>& param_names__,\n bool include_tparams__ = true,\n bool include_gqs__ = true) const {\n std::stringstream param_name_stream__;\n param_name_stream__.str(std::string());\n param_name_stream__ << \"alpha\";\n param_names__.push_back(param_name_stream__.str());\n param_name_stream__.str(std::string());\n param_name_stream__ << \"beta\";\n param_names__.push_back(param_name_stream__.str());\n param_name_stream__.str(std::string());\n param_name_stream__ << \"sigma\";\n param_names__.push_back(param_name_stream__.str());\n\n if (!include_gqs__ && !include_tparams__) return;\n\n if (include_tparams__) {\n }\n\n\n if (!include_gqs__) return;\n }\n\n\n void unconstrained_param_names(std::vector<std::string>& param_names__,\n bool include_tparams__ = true,\n bool include_gqs__ = true) const {\n std::stringstream param_name_stream__;\n param_name_stream__.str(std::string());\n param_name_stream__ << \"alpha\";\n param_names__.push_back(param_name_stream__.str());\n param_name_stream__.str(std::string());\n param_name_stream__ << \"beta\";\n param_names__.push_back(param_name_stream__.str());\n param_name_stream__.str(std::string());\n param_name_stream__ << \"sigma\";\n param_names__.push_back(param_name_stream__.str());\n\n if (!include_gqs__ && !include_tparams__) return;\n\n if (include_tparams__) {\n }\n\n\n if (!include_gqs__) return;\n }\n\n}; // model\n\n}\n\ntypedef model52cc5d507ad7_stan_model1_namespace::model52cc5d507ad7_stan_model1 stan_model;\n\n"
##
## $model_name
## [1] "stan_model1"
##
## $model_code
## [1] "// Stan model for simple linear regression\n\ndata {\n int < lower = 1 > N; // Sample size\n vector[N] x; // Predictor\n vector[N] y; // Outcome\n}\n\nparameters {\n real alpha; // Intercept\n real beta; // Slope (regression coefficients)\n real < lower = 0 > sigma; // Error SD\n}\n\nmodel {\n y ~ normal(alpha + x * beta , sigma);\n}\n\ngenerated quantities {\n} // The posterior predictive distribution"
## attr(,"model_name2")
## [1] "stan_model1"stan_model1 <- "stan_model1.stan"
fit <- stan(file = stan_model1, data = stan_data, warmup = 500, iter = 1000, chains = 4, cores = 2, thin = 1)
fit## Inference for Stan model: stan_model1.
## 4 chains, each with iter=1000; warmup=500; thin=1;
## post-warmup draws per chain=500, total post-warmup draws=2000.
##
## mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
## alpha 11.42 0.00 0.13 11.18 11.34 11.42 11.51 11.69 760 1
## beta 0.01 0.00 0.01 0.00 0.01 0.01 0.02 0.02 812 1
## sigma 0.40 0.00 0.05 0.32 0.36 0.40 0.43 0.51 901 1
## lp__ 16.26 0.05 1.29 12.99 15.66 16.57 17.19 17.73 683 1
##
## Samples were drawn using NUTS(diag_e) at Mon Jun 10 21:09:31 2019.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).Run the Stan model
posterior <- extract(fit)
str(posterior)## List of 4
## $ alpha: num [1:2000(1d)] 11.4 11.5 11.2 11.3 11.7 ...
## ..- attr(*, "dimnames")=List of 1
## .. ..$ iterations: NULL
## $ beta : num [1:2000(1d)] 0.0121 0.0102 0.0193 0.0193 0.0065 ...
## ..- attr(*, "dimnames")=List of 1
## .. ..$ iterations: NULL
## $ sigma: num [1:2000(1d)] 0.357 0.328 0.459 0.35 0.468 ...
## ..- attr(*, "dimnames")=List of 1
## .. ..$ iterations: NULL
## $ lp__ : num [1:2000(1d)] 17.4 16.5 15.5 16.6 14.8 ...
## ..- attr(*, "dimnames")=List of 1
## .. ..$ iterations: NULLCompare your results to our results to “lm”
plot(y ~ x, pch = 20)
abline(lm1, col = 2, lty = 2, lw = 3)
abline( mean(posterior$alpha), mean(posterior$beta), col = 6, lw = 2)
for (i in 1:500) {
abline(posterior$alpha[i], posterior$beta[i], col = "gray", lty = 1)
}
plot(y ~ x, pch = 20)
for (i in 1:500) {
abline(posterior$alpha[i], posterior$beta[i], col = "gray", lty = 1)
}
abline(mean(posterior$alpha), mean(posterior$beta), col = 6, lw = 2)
plot(posterior$alpha, type = "l")
plot(posterior$beta, type = "l")
plot(posterior$sigma, type = "l")
## Parameter summaries
par(mfrow = c(1,3))
plot(density(posterior$alpha), main = "Alpha")
abline(v = lm_alpha, col = 4, lty = 2)
plot(density(posterior$beta), main = "Beta")
abline(v = lm_beta, col = 4, lty = 2)
plot(density(posterior$sigma), main = "Sigma")
abline(v = lm_sigma, col = 4, lty = 2)
sum(posterior$beta>0)/length(posterior$beta)## [1] 0.9835sum(posterior$beta>0.2)/length(posterior$beta)## [1] 0traceplot(fit)
stan_dens(fit)
stan_hist(fit)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot(fit, show_density = FALSE, ci_level = 0.5, outer_level = 0.95, fill_color = "salmon")## ci_level: 0.5 (50% intervals)## outer_level: 0.95 (95% intervals)
Posterior predictive checks
write("// Stan model for simple linear regression
data {
int < lower = 1 > N; // Sample size
vector[N] x; // Predictor
vector[N] y; // Outcome
}
parameters {
real alpha; // Intercept
real beta; // Slope (regression coefficients)
real < lower = 0 > sigma; // Error SD
}
model {
y ~ normal(x * beta + alpha, sigma);
}
generated quantities {
real y_rep[N];
for (n in 1:N) {
y_rep[n] = normal_rng(x[n] * beta + alpha, sigma);
}
}",
"stan_model2_GQ.stan")
stan_model2_GQ <- "stan_model2_GQ.stan"
fit3 <- stan(stan_model2_GQ, data = stan_data, iter = 1000, chains = 4, cores = 2, thin = 1)
y_rep <- as.matrix(fit3, pars = "y_rep")
dim(y_rep)## [1] 2000 39ppc_dens_overlay(y, y_rep[1:200, ])
ppc_stat(y = y, yrep = y_rep, stat = "mean")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ppc_scatter_avg(y = y, yrep = y_rep)
available_ppc()## bayesplot PPC module:
## ppc_bars
## ppc_bars_grouped
## ppc_boxplot
## ppc_data
## ppc_dens
## ppc_dens_overlay
## ppc_ecdf_overlay
## ppc_error_binned
## ppc_error_hist
## ppc_error_hist_grouped
## ppc_error_scatter
## ppc_error_scatter_avg
## ppc_error_scatter_avg_vs_x
## ppc_freqpoly
## ppc_freqpoly_grouped
## ppc_hist
## ppc_intervals
## ppc_intervals_data
## ppc_intervals_grouped
## ppc_loo_intervals
## ppc_loo_pit
## ppc_loo_pit_overlay
## ppc_loo_pit_qq
## ppc_loo_ribbon
## ppc_ribbon
## ppc_ribbon_data
## ppc_ribbon_grouped
## ppc_rootogram
## ppc_scatter
## ppc_scatter_avg
## ppc_scatter_avg_grouped
## ppc_stat
## ppc_stat_2d
## ppc_stat_freqpoly_grouped
## ppc_stat_grouped
## ppc_violin_groupedcolor_scheme_view(c("blue", "gray", "green", "pink", "purple",
"red","teal","yellow"))
color_scheme_view("mix-blue-red")
color_scheme_set("blue")