ANLY505 - Intro to Stan Problem Set #1
Dan Liu
2019-07-27
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?
Load 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)
lm1##
## Call:
## lm(formula = extent_south ~ year, data = seaice)
##
## Coefficients:
## (Intercept) year
## -14.19955 0.01295plot(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
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: 0.02318Write the Stan model
library(rstan)## Warning: package 'rstan' was built under R version 3.5.3## Loading required package: StanHeaders## Warning: package 'StanHeaders' was built under R version 3.5.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.5.3## rstan (Version 2.19.2, 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)## Warning: package 'gdata' was built under R version 3.5.3## 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)## Warning: package 'bayesplot' was built under R version 3.5.3## 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 settingx<-I(seaice$year-1978)
y<-seaice$extent_south
N<-length(seaice$year)
lm2<-lm(y~x)
summary(lm2)##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.23372 -0.18142 0.01587 0.18465 0.88814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.421555 0.125490 91.015 <2e-16 ***
## x 0.012953 0.005468 2.369 0.0232 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3843 on 37 degrees of freedom
## Multiple R-squared: 0.1317, Adjusted R-squared: 0.1082
## F-statistic: 5.611 on 1 and 37 DF, p-value: 0.02318lm_alpha<-summary(lm2)$coeff[1]
lm_beta<-summary(lm2)$coeff[2]
lm_sigma<-sigma(lm2)
stan_data<-list(N=N, x=x, y=y)
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] "model14d44aa66202_stan_model1"
##
## $cppcode
## [1] "// Code generated by Stan version 2.19.1\n\n#include <stan/model/model_header.hpp>\n\nnamespace model14d44aa66202_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\", \"model14d44aa66202_stan_model1\");\n reader.add_event(22, 20, \"end\", \"model14d44aa66202_stan_model1\");\n return reader;\n}\n\nclass model14d44aa66202_stan_model1 : public prob_grad {\nprivate:\n int N;\n vector_d x;\n vector_d y;\npublic:\n model14d44aa66202_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 model14d44aa66202_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__ = \"model14d44aa66202_stan_model1_namespace::model14d44aa66202_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 try {\n // initialize data block variables from context__\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 check_greater_or_equal(function__, \"N\", N, 1);\n\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 x = Eigen::Matrix<double, Eigen::Dynamic, 1>(N);\n vals_r__ = context__.vals_r(\"x\");\n pos__ = 0;\n size_t x_j_1_max__ = N;\n for (size_t j_1__ = 0; j_1__ < x_j_1_max__; ++j_1__) {\n x(j_1__) = vals_r__[pos__++];\n }\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 y = Eigen::Matrix<double, Eigen::Dynamic, 1>(N);\n vals_r__ = context__.vals_r(\"y\");\n pos__ = 0;\n size_t y_j_1_max__ = N;\n for (size_t j_1__ = 0; j_1__ < y_j_1_max__; ++j_1__) {\n y(j_1__) = vals_r__[pos__++];\n }\n\n\n // initialize transformed data variables\n // execute transformed data statements\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__ += 1;\n current_statement_begin__ = 11;\n num_params_r__ += 1;\n current_statement_begin__ = 12;\n num_params_r__ += 1;\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 ~model14d44aa66202_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 typedef double local_scalar_t__;\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 current_statement_begin__ = 10;\n if (!(context__.contains_r(\"alpha\")))\n stan::lang::rethrow_located(std::runtime_error(std::string(\"Variable alpha missing\")), current_statement_begin__, prog_reader__());\n vals_r__ = context__.vals_r(\"alpha\");\n pos__ = 0U;\n context__.validate_dims(\"parameter 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 stan::lang::rethrow_located(std::runtime_error(std::string(\"Error transforming variable alpha: \") + e.what()), current_statement_begin__, prog_reader__());\n }\n\n current_statement_begin__ = 11;\n if (!(context__.contains_r(\"beta\")))\n stan::lang::rethrow_located(std::runtime_error(std::string(\"Variable beta missing\")), current_statement_begin__, prog_reader__());\n vals_r__ = context__.vals_r(\"beta\");\n pos__ = 0U;\n context__.validate_dims(\"parameter 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 stan::lang::rethrow_located(std::runtime_error(std::string(\"Error transforming variable beta: \") + e.what()), current_statement_begin__, prog_reader__());\n }\n\n current_statement_begin__ = 12;\n if (!(context__.contains_r(\"sigma\")))\n stan::lang::rethrow_located(std::runtime_error(std::string(\"Variable sigma missing\")), current_statement_begin__, prog_reader__());\n vals_r__ = context__.vals_r(\"sigma\");\n pos__ = 0U;\n context__.validate_dims(\"parameter 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 stan::lang::rethrow_located(std::runtime_error(std::string(\"Error transforming variable sigma: \") + e.what()), current_statement_begin__, prog_reader__());\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(std::vector<T__>& params_r__,\n std::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__; // dummy to suppress unused var warning\n\n T__ lp__(0.0);\n stan::math::accumulator<T__> lp_accum__;\n try {\n stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);\n\n // model parameters\n current_statement_begin__ = 10;\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 current_statement_begin__ = 11;\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 current_statement_begin__ = 12;\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 // 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__ = \"model14d44aa66202_stan_model1_namespace::write_array\";\n (void) function__; // dummy to suppress unused var warning\n\n // read-transform, write parameters\n double alpha = in__.scalar_constrain();\n vars__.push_back(alpha);\n\n double beta = in__.scalar_constrain();\n vars__.push_back(beta);\n\n double sigma = in__.scalar_lb_constrain(0);\n vars__.push_back(sigma);\n\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 if (!include_tparams__ && !include_gqs__) return;\n\n try {\n if (!include_gqs__ && !include_tparams__) return;\n if (!include_gqs__) return;\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 \"model14d44aa66202_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 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 if (!include_gqs__) return;\n }\n\n}; // model\n\n} // namespace\n\ntypedef model14d44aa66202_stan_model1_namespace::model14d44aa66202_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"Run the Stan model
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 11.43 0.00 0.13 11.18 11.34 11.42 11.51 11.68 914 1
## beta 0.01 0.00 0.01 0.00 0.01 0.01 0.02 0.02 960 1
## sigma 0.40 0.00 0.05 0.32 0.37 0.39 0.42 0.50 961 1
## lp__ 16.31 0.05 1.32 12.92 15.77 16.68 17.23 17.73 716 1
##
## Samples were drawn using NUTS(diag_e) at Sat Jul 27 23:32:34 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).posterior<-extract(fit)
str(posterior)## List of 4
## $ alpha: num [1:2000(1d)] 11.3 11.5 11.4 11.3 11.4 ...
## ..- attr(*, "dimnames")=List of 1
## .. ..$ iterations: NULL
## $ beta : num [1:2000(1d)] 0.0152 0.0116 0.0126 0.0235 0.0169 ...
## ..- attr(*, "dimnames")=List of 1
## .. ..$ iterations: NULL
## $ sigma: num [1:2000(1d)] 0.361 0.412 0.342 0.414 0.331 ...
## ..- attr(*, "dimnames")=List of 1
## .. ..$ iterations: NULL
## $ lp__ : num [1:2000(1d)] 17.3 17.6 17.2 15.5 16.6 ...
## ..- attr(*, "dimnames")=List of 1
## .. ..$ iterations: NULLCompare your results to our results to “lm”
plot(y~x, pch=20)
abline(lm2,col=2,lty=2,lw=3)
abline(mean(posterior$alpha),mean(posterior$beta),col=3, lw=2)
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=3, lw=2)
plot(posterior$alpha,type="l")
plot(posterior$beta,type="l")
plot(posterior$sigma,type="l")
Answer: No, the sea ice extent increased in the southern hemisphere over time. It is not the same pattern happening in the antarctic as arctic. And trough the modeling, we found the stan model results matches with linear model to indicate that the sea ice stent increses in the south.