R/pointEstimate.R

In MauriceOConnell/causalPAF: Causal Effect for Population Attributable Fractions (PAF)

#' @title Evaluates Point Estimates for Total PAF, Direct PAF, Indirect PAF and Path Specific PAF for a user inputted number of integral simulations. There is no bootstrap applied in this function.
#' @description Evaluates Total PAF, Direct PAF, Indirect PAF and Path Specific PAF for a user inputted number of bootstraps and integral simulations
#' @param dataframe A wide format dataframe containing all the risk factors, confounders, exposures and outcomes within the causal DAG Bayesian network.
#' @param exposure The name of the exposure column variable within dataframe in text format e.g. "phys".
#' @param mediator The name of the mediator column variables within dataframe in text format. There can be more than one mediator of interest. It can be a vector of mediators names within the dataframe e.g. c("subhtn","apob_apoa","whr").
#' @param response The name of the response column variable within dataframe in text format e.g. "case". The cases should be coded as 1 and the controls as 0.
#' @param response_model_mediators A regression model fitted for the response in a causal Bayesian network excluding ``children'' of the mediators in the causal Bayesian network. See example in tutorial.This model can be listed either as (1) an empty list ( response_model_mediators = list() ) or (2) the user can specify their own customised causal regression model(s) to use. When it is listed as an empty list the 'causalPAF' package will fit the response_model_mediators regression model automatically based on the causal DAG supplied by the user in in_outArg. Alternatively, the user can specify the exact model(s) that the user wishes to use, these model(s) must be in list format (list() where length(response_model_mediators) == length(mediator) ), the same length as the parameter, mediator, with the user customised model for each mediator listed in the same order as in the parameter, mediator, and if there is only one model, it must be listed each time within the list() so that length(response_model_mediators) == length(mediator).
#' @param response_model_exposure A regression model fitted for the response in a causal Bayesian network excluding ``children'' of the exposure in the causal Bayesian network. This regression model will not adjust for mediators (exclude mediators) of the exposure in the regression model so that the total effect of the exposure on the response can be modelled. This model can be listed either as (1) an empty list ( response_model_exposure = list() ) or (2) the user can specify their own customised causal regression model to use. If specified as an empty list, list(), then the 'causalPAF' package will define and fit the model automatically based on the causal DAG defined by the in_outArg parameter. Alternatively, the user can specify the exact model that the user wishes to use, this model must be in list format (list() where length(response_model_exposure) == 1 ), of length 1, assuming only one exposure of interest (other exposures can be risk factors) and the model must be defined within a list() since the package assumes a list() format is supplied. See example in tutorial. E.G. If physical exercise ("exer") in the example given in the diagram is the exposure. Then the regression would include all parents of "exer" (i.e. sex, region, educ, age) as well as risk factors at the same level of the causal Bayesian network (i.e. stress, smoke, diet, alcoh).
#' @param in_outArg This defines the causal directed acyclic graph (DAG). A list of length 2. It is defined as a two dimensional list consisting of, firstly, the first list, inlist, i.e. a list of the parents of each variable of interest corresponding to its column name in the data. Splines can be included here if they are to be modelled as splines. Secondly, the second list, outlist, contains a list of a single name of exposure or risk factor or outcome in form of characters i.e. a list of each variable of interest (risk factors, exposures and outcome) corresponding to its column name in the data. Splines should not be input here, only the column names of the variables of interest in the data. The order at which variables are defined must satisfy (i) It is important that variables are defined in the same order in both lists e.g. the first risk factor defined in outlist has its parents listed first in inlist, the second risk factor defined in outlist has its parents listed secondly in inlist and so on. The package assumes this ordering and will not work if this order is violated. (ii) Note it is important also that the order at which the variables are defined is such that all parents of that variable are defined before it. See example in tutorial.
#' @param Splines_outlist A list defined of same size and order of variables as defined in in_outArg[[2]]. If splines are to be used for variables listed in in_outArg[[2]], then the splines should be defined in Splines_outlist in the same order as variables appear in in_outArg[[2]]. It is necessary to list variables in Splines_outlist the same as in in_outArg[[2]] without splines if no spline is to be applied. It should not be input as an empty list, list(), if no splines. A warning will show if input as an empty list requiring the user to populate Splines_outlist either the same as in_outArg[[2]] (if no splines) or in the same order as in_outArg[[2]] with splines (if splines).  See example in tutorial.
#' @param splinesDefinedIn_in_outDAG Logical TRUE or FALSE indicating whether the user has defined splines in the causal DAG, in_out, if TRUE. If FALSE and splines are defined in Splines_outlist_Var, then it is necessary for the package to populate the in_out DAG with splines listed in Splines_outlist_Var.
#' @param model_listArg is a list of models fitted for each of the variables in in_outArg[[2]] (or in_outArg\eqn{\$}outlist ) based on its parents given in in_outArg[[1]] ( or in_out\eqn{\$}inlist ). By default this is set to an empty list. In the default setting, the models are fitted automatically by the 'causalPAF' package based on the order of the variables input in the parameter in_outArg. See the tutorial for more examples. Alternatively, the user can supply their own fitted models here by populating ``model_listArg'' with their own fitted models for each risk factor, mediator, exposure and response variable. But the order of these models must be in the same order of the variables in the second list of in_outArg ( in_outArg[[2]] ) and these models be defined within a list, list(), of the same length as in_outArg[[2]]. See tutorial for further examples.
#' @param weights Column of weights for case control matching listed in the same order as the patients in the data e.g. weights = strokedata$weights.
#' @param NumSimulation This is the number of simulations requested by the user to estimate integrals. The larger the number of simulations the more accurate the results but the longer the code takes to run. Therefore the user may wish to balance speed with accuracy depending on which is of more value in the specific context of interest. The integrals for continuous variables are estimated using simulation methods.
#' @param addCustom Logical TRUE or FALSE indicating whether a customised interaction term is to be added to the each regression. The interaction term can include splines.
#' @param custom text containing the customised interaction term to be added to each regression. The text should be enclosed in inverted commas. Splines can be included within the interaction terms. See tutorial for examples.
#' @export
#' @import splines MASS stats utils
#' @return Estimates point estimates for 5 results that are:(1)Total Population Attributable Fraction (PAF),(2)Direct Effect Population Attributable Fraction (PAF) using  alternative definition, (3)Indirect Effect Population Attributable Fraction (PAF) using  alternative definition, (4)Path Specific Population Attributable Fraction (PAF), (5)Overall Direct Population Attributable Fraction (PAF)
#' @examples
#' \donttest{
#' # Loads some data (fictional Stroke data from the package 'causalPAF')
#' # In this example, we use a small data set called 'strokedata_smallSample' consisting of 5,000
#' # rows of fictional patient data. For more accurate results, a larger data set is available
#' # called 'strokedata'which contains 16,623 rows of fictional patient data. The methodology
#' # applied in the 'causalPAF' package is more accurate the larger the dataset. To use the larger
#' # 'strokedata' dataset, simply call
#' # stroke_reduced <- strokedata
#' stroke_reduced <- strokedata_smallSample
#'
#' # Just shortening the name of a variable, "apob_apoa", to "apb" so the R code
#' # in document example is not truncated.
#' stroke_reduced$apb  <- stroke_reduced$apob_apoa
#'
#' # The data should contain a column of weights for case control matching.
#' # strokedata$weights
#' # Weigths are not needed for cohort/cross sectional designs.
#'
#' # The data should have reference levels of all risk factors already set.
#' # This can be done as follows  but has already been applied to the data so is not run here:
#' # levels(stroke_reduced$htnadmbp) <- c(0, 1)
#' # stroke_reduced$subhtn <-  factor(stroke_reduced$subhtn,levels=c(1, 2))
#' # levels(stroke_reduced$nevfcur) <- c(1, 2)
#' # stroke_reduced$global_stress2  <- factor(stroke_reduced$global_stress2,levels=c(1,2))
#' # levels(stroke_reduced$whrs2tert) <- c(1, 2, 3)
#' # levels(stroke_reduced$phys) <- c(2, 1)
#' # levels(stroke_reduced$alcohfreqwk) <- c(1, 2, 3)
#' # stroke_reduced$dmhba1c2 <- factor(stroke_reduced$dmhba1c2,levels=c(1,2))
#' # stroke_reduced$cardiacrfcat <- factor(stroke_reduced$cardiacrfcat,levels=c(1,2))
#' # levels(stroke_reduced$ahei3tert) <- c(3,2,1)
#' # levels(stroke_reduced$apob_apoatert) <- c(1,2,3)
#'
#' # The 'causalPAF' package assumes the data is either complete case data or that missing data
#' # analysis has already been performed.
#'
#' # Next, define the causal structure or directed acyclic graph (DAG) of the causal Bayesian
#' # network defined by the data. We list the parents of each exposure or risk factor or outcome
#' # in a vector as follows:
#'
#' # Note it is important that the order at which the variables are defined is such that all
#' # parents of that variable are defined before it. Please refer to the figure of the causal
#' # Bayesian network (with both direct and indirect effects) defined earlier as an example of this
#' # order.
#'
#' in_phys <- c("subeduc","moteduc","fatduc")
#' in_ahei <- c("subeduc","moteduc","fatduc")
#' in_nevfcur <- c("subeduc","moteduc","fatduc")
#' in_alcohfreqwk <- c("subeduc","moteduc","fatduc")
#' in_global_stress2 <- c("subeduc","moteduc","fatduc")
#' in_subhtn <- c("subeduc","moteduc","fatduc","phys","ahei3tert","nevfcur","alcohfreqwk",
#'                "global_stress2")
#' in_apob_apoa <- c("subeduc","moteduc","fatduc","phys","ahei3tert","nevfcur","alcohfreqwk",
#'                   "global_stress2")
#' in_whr <- c("subeduc","moteduc","fatduc","phys","ahei3tert","nevfcur","alcohfreqwk",
#'             "global_stress2")
#'
#' # Note splines can be fitted within the causal structure as shown below especially if splines
#' # are to be used in the fitted models.
#' # It is important that splines of parent variables are "typed" or "spelt" consistently
#' # (including spaces) throughout as 'causalPAF' can fit models automatically provided variables are
#' # spelt consistently. Also if a parent variable is a spline it should be defined in spline
#' # format in all occurences of the parent variable.
#' in_cardiacrfcat <- c("subeduc","moteduc","fatduc","phys","ahei3tert","nevfcur","alcohfreqwk",
#'                      "global_stress2",
#'"ns(apb,knots=quantile(apb,c(.25,.5,.75)),Boundary.knots=quantile(apb,c(.001,.95)))",
#' "ns(whr,df=5)","subhtn")
#' in_dmhba1c2 <- c("subeduc","moteduc","fatduc","phys","ahei3tert","nevfcur","alcohfreqwk",
#'                  "global_stress2",
#'"ns(apb,knots=quantile(apb,c(.25,.5,.75)),Boundary.knots=quantile(apb,c(.001,.95)))",
#' "ns(whr,df=5)","subhtn")
#' in_case <- c("subeduc","moteduc","fatduc","phys","ahei3tert","nevfcur","alcohfreqwk",
#'              "global_stress2",
#'"ns(apb,knots=quantile(apb,c(.25,.5,.75)),Boundary.knots=quantile(apb,c(.001,.95)))",
#' "ns(whr,df=5)","subhtn","cardiacrfcat","dmhba1c2")
#'
#' # Then we define a two dimensional list consisting of
#' # 1. inlist i.e. a list of the parents of each variable of interest corresponding to its column
#' # name in the data. Splines should be included here if they are to be modelled as splines.
#' # 2. outlist i.e. a list of each variable of interest corresponding to its column name in the
#' # data. Splines should not be input here, only the column names of the variables of interest in
#' # the data.
#' # Again the order is such that each variable is defined after all its parents.
#'
#' in_out <- list(inlist=list(in_phys,in_ahei,in_nevfcur,in_alcohfreqwk,in_global_stress2,
#'                in_subhtn,in_apob_apoa,in_whr,in_cardiacrfcat,in_dmhba1c2,in_case),
#'                outlist=c("phys","ahei3tert","nevfcur","alcohfreqwk","global_stress2","subhtn",
#'                          "apb","whr","cardiacrfcat","dmhba1c2","case"))
#'
#' # If splines are to be used for variables listed in in_out$outlist, then the splines should be
#' # defined in the same order as variables appear in in_out$outlist as follows. It is necessary to
#' # list variables in in_out$outlist without splines if no spline is to be applied.
#' # It is important that Splines_outlist is defined in the following format
#' # list(c("splinename1","splinename2","splinename3")) for the package to be applied correctly.
#' # And Splines_outlist should not be an empty list(). If there are no splines it should be
#' # defined the same as in_out[[2]] and in the same order as variables defined in_out[[2]].
#' Splines_outlist = list( c("phys","ahei3tert","nevfcur","alcohfreqwk","global_stress2","subhtn",
#'"ns(apb,knots=quantile(apb,c(.25,.5,.75)),Boundary.knots=quantile(apb,c(.001,.95)))",
#' "ns(whr,df=5)","cardiacrfcat","dmhba1c2","case") )
#'
#' # To fit these models to case control data, one needs to perform weighted maximum likelihood
#' # estimation to imitate estimation using a random sample from the population. We chose weights
#' # of 0.0035 (for each case) and 0.9965 (for each control), reflective of a yearly incidence of
#' # first ischemic stroke of 0.35%, or 3.5 strokes per 1,000 individuals. These weights were
#' # chosen according to average incidences across country, age, group and gender within
#' # INTERSTROKE according to the global burden of disease.
#' w <- rep(1,nrow(stroke_reduced))
#' w[stroke_reduced$case==0] <- 0.9965
#' w[stroke_reduced$case==1] <- 0.0035
#'
#' # It is important to assign stroke_reduced$weights to the updated weights defined in w.
#' # Otherwise if stroke_reduced$weights <- w is not set, the alternative weights supplied in the
#' #  fictional data will be used. In this case, we want to use weigths as defined in w.
#' stroke_reduced$weights <- w
#'
#' #The checkMarkovDAG() function in the 'causalPAF' package should be used before running
#' # causalPAFplot() to ensure:
#' #1. The causal Markov condition holds for the causal structure defined in the variable in_out.
#' #2. The variables in in_out are listed in the order so that no variable is defined before a
#' # parent or direct cause. Note: if this order does not hold, checkMarkovDAG() will automatically
#' # reorder the variables in, in_out, provided it is a Markov DAG.
#'
#' #The causal analysis requires that the causal structure is a Markov DAG. The Causal Markov (CM)
#' # condition states that, conditional on the set of all its direct causes, a node is independent
#' # of all variables which are not direct causes or direct effects of that node. In the event that
#' # the structure of a Bayesian network accurately depicts causality, the two conditions are
#' # equivalent. However, a network may accurately embody the Markov condition without depicting
#' # causality, in which case it should not be assumed to embody the causal Markov condition.
#'
#' # in_out is as defined above and input into this code.
#'
#' if(checkMarkovDAG(in_out)$IsMarkovDAG & !checkMarkovDAG(in_out)$Reordered){
#'   print("Your in_out DAG is a Markov DAG.")
#'   } else if( checkMarkovDAG(in_out)$IsMarkovDAG & checkMarkovDAG(in_out)$Reordered ) {
#'
#'       in_out <- checkMarkovDAG(in_out)[[2]]
#'
#'           print("Your in_out DAG is a Markov DAG.The checkMarkovDAG function has reordered your
#'                in_out list so that all parent variables come before descendants.")
#'           } else{ print("Your ``in_out'' list is not a Bayesian Markov DAG so the methods in the
#'                         'causalPAF' package cannot be applied for non Markov DAGs.")}
#'
#'
#' # The pointEstimate() function evaluates Point Estimates for Total PAF, Direct PAF, Indirect PAF
#' # and Path Specific PAF for a user inputted number of integral simulations. There is no bootstrap
#' # applied in this function.
#' # Since bootstraps are not applied, the pointEstimate() function will run quicker than the
#' # alternative causalPAFplot() function which calculates bootstrap estimates which can take
#' # longer to run.
#'
#'
#'          pointEstimate(dataframe = stroke_reduced,
#'                        exposure="phys",
#'                        mediator=c("subhtn","apb","whr"),
#'                        response="case",
#'                        response_model_mediators = list(),
#'                        response_model_exposure = list(),
#'                        in_outArg = in_out,
#'                        Splines_outlist = Splines_outlist,
#'                        splinesDefinedIn_in_outDAG = TRUE,
#'                        model_listArg = list(),
#'                        weights = w,
#'                        NumSimulation = 3,
#'                        addCustom = TRUE,
#'                        custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)")
#'
#'
#' # The pointEstimate() function below has response_model_mediators, response_model_exposure and
#' # model_listArg pre fit. This allows the user to apply customised regressions instead of the
#' # default setting above, where the 'causalPAF' R package fitted these regressions automatically
#' # based on the causalDAG defined in in_outArg.
#'
#' # Libraries must be loaded if fitting models outside of the 'causalPAF' R package.
#'
#' library(MASS)
#' library(splines)
#'
#'
#' # Next we fit the, response_model_mediators and response_model_exposure, models outside of the
#' # 'causalPAF' package as an input into the package.
#'
#' # It is important that response_vs_mediator is a list and it must be the same length as the
#' # parameter, mediator, i.e. length( response_vs_mediator ) == length( mediator). In this
#' # example, mediator=c("subhtn","apb","whr") so length( mediator) is 3, so we create a list
#' # with three models for "subhtn","apb" and "whr" respectively in that order. Note in this
#' # example, the model is the same for each mediator, but it must still be input 3 times within
#' # the list as follows:
#'
#' response_vs_mediator <-  list(
#' glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) +subeduc+moteduc+ fatduc+ phys+
#' ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2+ subhtn +
#' ns(apb, knots = quantile(apb,c(.25,0.5,0.75)),
#' Boundary.knots = quantile(apb,c(.001,0.95)))+
#' ns(whr,df=5)",data = stroke_reduced,family='binomial',w = stroke_reduced$weights ),
#' # "subhtn" mediator model
#' glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) +  subeduc+ moteduc+ fatduc+ phys+
#' ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2+ subhtn +
#' ns(apb, knots = quantile(apb,c(.25,0.5,0.75)),
#' Boundary.knots = quantile(apb,c(.001,0.95)))+
#' ns(whr,df=5)",data = stroke_reduced,family='binomial',w = stroke_reduced$weights ),
#' # "apb" mediator model
#' glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) +  subeduc+ moteduc+ fatduc+ phys+
#' ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2+ subhtn +
#' ns(apb, knots = quantile(apb,c(.25,0.5,0.75)),
#' Boundary.knots = quantile(apb,c(.001,0.95)))+
#' ns(whr,df=5)",data = stroke_reduced,family='binomial',w = stroke_reduced$weights ) )
#'  # "whr" mediator model
#'
#'
#' # Next we fit a customised response_model_exposure model rather than allowing the package fit it
#' # automatically as shown previously. This must be a list of length 1.
#' response_vs_phys <- list(glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)+subeduc+moteduc+
#' fatduc+ phys+ ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2",data = stroke_reduced,
#' family='binomial',w= stroke_reduced$weights) )
#'
#' # model_listArg is a list of models fitted for each of the variables in in_out$outlist based on
#' # its parents given in in_out$inlist. By default this is set to an empty list. Alternatively the
#' # user can supply their custom fitted, model_listpop, which should be consistent with the causal
#' # structure. model_listArg is defined earlier in this tutorial.
#' # Note it is important that model_listArg is defined as a list and in the same order and length
#' # as the variables defined in in_outArg[[2]].
#'
#' model_listArgFit <- list(glm(formula = phys ~ subeduc + regionnn7 * ns(eage, df = 5) +
#' esex * ns(eage, df = 5) + moteduc + fatduc, family = "binomial", data = stroke_reduced,
#'  weights = weights), # model 1 phys
#' polr(formula = ahei3tert ~ subeduc + regionnn7 * ns(eage, df = 5) + esex * ns(eage, df = 5) +
#' moteduc + fatduc, data = stroke_reduced, weights = weights), # model 2 ahei3tert
#' glm(formula = nevfcur ~ subeduc + regionnn7 * ns(eage, df = 5) + esex * ns(eage, df = 5) +
#' moteduc + fatduc, family = "binomial",data = stroke_reduced, weights = weights),
#' # model 3 nevfcur
#' polr(formula = alcohfreqwk ~ subeduc + regionnn7 * ns(eage, df = 5) +esex * ns(eage, df = 5) +
#' moteduc + fatduc, data = stroke_reduced,weights = weights), # model 4 alcohfreqwk
#' glm(formula = global_stress2 ~ subeduc + regionnn7 * ns(eage,df = 5) + esex * ns(eage, df = 5) +
#' moteduc + fatduc, family = "binomial",data = stroke_reduced, weights = weights),
#' # model 5 global_stress2
#' glm(formula = subhtn ~ subeduc + regionnn7 * ns(eage, df = 5) +esex * ns(eage, df = 5) +
#' moteduc + fatduc + phys + ahei3tert +nevfcur + alcohfreqwk + global_stress2,family = "binomial",
#' data = stroke_reduced, weights = weights), # model 6 subhtn
#' lm(formula = apb ~ subeduc + regionnn7 * ns(eage, df = 5) +esex * ns(eage, df = 5) +
#' moteduc + fatduc + phys + ahei3tert +nevfcur + alcohfreqwk + global_stress2,
#' data = stroke_reduced,weights = weights), # model 7 apob_apoa coded as apb
#' lm(formula = whr ~ subeduc + regionnn7 * ns(eage, df = 5) + esex *ns(eage, df = 5) + moteduc +
#' fatduc + phys + ahei3tert +nevfcur + alcohfreqwk + global_stress2, data = stroke_reduced,
#' weights = weights), # model 8 whr
#' glm(formula = cardiacrfcat ~ subeduc + regionnn7 * ns(eage, df = 5) +esex * ns(eage, df = 5) +
#' moteduc + fatduc + phys + ahei3tert +nevfcur + alcohfreqwk + global_stress2 +
#' ns(apb, knots = quantile(apb,c(0.25, 0.5, 0.75)),
#' Boundary.knots = quantile(apb,c(0.001, 0.95))) + ns(whr, df = 5) + subhtn,
#' family = "binomial",data = stroke_reduced, weights = weights), # model 9 cardiacrfcat
#' glm(formula = dmhba1c2 ~ subeduc + regionnn7 * ns(eage, df = 5) +esex * ns(eage, df = 5) +
#' moteduc + fatduc + phys + ahei3tert +nevfcur + alcohfreqwk + global_stress2 +
#' ns(apb, knots = quantile(apb,c(0.25, 0.5, 0.75)),
#' Boundary.knots = quantile(apb,c(0.001, 0.95))) + ns(whr, df = 5) + subhtn,
#' family = "binomial",data = stroke_reduced, weights = weights), # model 10 dmhba1c2
#' glm(formula = case ~ subeduc + regionnn7 * ns(eage, df = 5) +esex * ns(eage, df = 5) + moteduc +
#' fatduc + phys + ahei3tert +nevfcur + alcohfreqwk + global_stress2 +
#' ns(apb, knots = quantile(apb,c(0.25, 0.5, 0.75)),
#' Boundary.knots = quantile(apb,c(0.001, 0.95))) + ns(whr, df = 5) + subhtn +
#' cardiacrfcat +dmhba1c2, family = "binomial", data = stroke_reduced, weights = weights)
#' # model 11 case
#' )
#'
#'
#'          pointEstimate(dataframe = stroke_reduced,
#'                        exposure="phys",
#'                        mediator=c("subhtn","apb","whr"),
#'                        response="case",
#'                        response_model_mediators = response_vs_mediator,
#'                        response_model_exposure = response_vs_phys,
#'                        in_outArg = in_out,
#'                        Splines_outlist = Splines_outlist,
#'                        splinesDefinedIn_in_outDAG = TRUE,
#'                        model_listArg = model_listArgFit,
#'                        weights = w,
#'                        NumSimulation = 3,
#'                        addCustom = TRUE,
#'                        custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)")
#'
#'
#'
#' # See examples from the function causalPAFplot() to see how bootstraps and confidence intervals
#' # can be calculated in addition to the pont estimates calculated from the pointEstimate()
#' # function. The causalPAFplot() function also shows examples of plotting these point estimate
#' # results with confidence intervals.
#' }

pointEstimate <- function(dataframe,
                          exposure="phys",
                          mediator=c("subhtn","apob_apoa","whr"),
                          response="case",
                          response_model_mediators = list(),
                          response_model_exposure = list(),
                          in_outArg,
                          Splines_outlist,
                          splinesDefinedIn_in_outDAG,
                          model_listArg,
                          weights = 1,  ## At moment, weigths are populated in the dataframe before calling function causalPAFplot.r. The default is weights = 1 which is not a case-control data format but rather a cohort weighting by default.
                          NumSimulation,
                          addCustom = FALSE,
                          custom = ""
                          ){

  # ggproto <- ggplot2::ggproto

  ##################################
  ## Test run entries
  ##################################
                          # dataframe = stroke_reduced
                          # exposure="phys"
                          # mediator=c("subhtn","apob_apoa","whr")
                          # response="case"
                          # response_model_mediators = list()
                          # response_model_exposure = list()
                          # in_outArg = in_out
                          # Splines_outlist = Splines_outlist  # needs to be input as list() if no splines. Assumes if appears as spline once needs to appear as a spline in all occurences
                          # splinesDefinedIn_in_outDAG = TRUE
                          # model_listArg = list()
                          # weights = stroke_reduced$weights
                          # NumSimulation = 200
                          # addCustom = TRUE
                          # custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)" # need to update make_formula() so that it runs without needing ~ first
                          # # custom = "+ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) "



                          ## If USER WANTS TO FIT OWN MODELS
                          # response_model_mediators = response_vs_mediator
                          # response_model_exposure = response_vs_phys
                          # model_listArg = model_listArgFit


# TestpointEstimate  <- pointEstimate(dataframe = stroke_reduced,
#                                     exposure="phys",
#                                     mediator=c("subhtn","apob_apoa","whr"),
#                                     response="case",
#                                     response_model_mediators = list(),
#                                     response_model_exposure = list(),
#                                     in_outArg = in_out,
#                                     Splines_outlist = Splines_outlist,  # needs to be input as list() if no splines. Assumes if appears as spline once needs to appear as a spline in all occurences
#                                     splinesDefinedIn_in_outDAG = TRUE,
#                                     model_listArg = list(),
#                                     weights = stroke_reduced$weights,
#                                     NumSimulation = 1000,
#                                     addCustom = TRUE,
#                                     custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)" # need to update make_formula() so that it runs without needing ~ first
#                                     # custom = "+ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) "
#                                     )


  # TestpointEstimate$results_mediatorPointEstimate
  # TestpointEstimate$mediators
  # TestpointEstimate$response_vs_physPointEstimate
  # TestpointEstimate$response_vs_mediatorPointEstimate

# #####################
# #####################
#  NumSimulation = 1000
# #####################
# #####################

####################
####################
## ADD IN POINT ESTIMATE WITHOUT BOOTSTRAP
##
####################
####################
# library(splines)


# results_subhtn <- matrix(nrow= 1, ncol=5)
# # colnames(results_subhtn) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
# colnames(results_subhtn) <- c("Total PAF","PAF_{Direct,M^j}","PAF_{Indirect,M^j}","PS-PAF_{A->M^j=>Y}","Direct PAF_{A->Y}")
#
# results_whr <- matrix(nrow= 1, ncol=5)
# # colnames(results_whr) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
# colnames(results_whr) <- c("Total PAF","PAF_{Direct,M^j}","PAF_{Indirect,M^j}","PS-PAF_{A->M^j=>Y}","Direct PAF_{A->Y}")
#
#
# results_apob_apoa <- matrix(nrow= 1, ncol=5 )
# # colnames(results_apob_apoa) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
# colnames(results_apob_apoa) <- c("Total PAF","PAF_{Direct,M^j}","PAF_{Indirect,M^j}","PS-PAF_{A->M^j=>Y}","Direct PAF_{A->Y}")


#####################
#######################
#######################
# ADDED IN FROM causalPAFplot
results_mediatorPointEstimate <- list()
for(i in 1:length(mediator)){
  # results_mediatorPointEstimate[[i]] <- matrix(nrow= dataframe,ncol=5)
  results_mediatorPointEstimate[[i]] <- matrix(nrow = 1,ncol=5)
  # colnames(results_mediatorPointEstimate[[i]]) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
  colnames(results_mediatorPointEstimate[[i]]) <- c("Total PAF","PAF_{Direct,M^j}","PAF_{Indirect,M^j}","PS-PAF_{A->M^j=>Y}","Direct PAF_{A->Y}")
}


model_list_usePointEstimate <- vector(mode = "list", length = length(in_outArg[[2]]) )
model_list_evalPointEstimate <- vector(mode = "list", length = length(in_outArg[[2]]) )

##########################################################################
##########################################################################
# Perform check and dagitty check
##########################################################################
##########################################################################
## ADD IN make_DAG() here to perform check and dagitty on in_outArg
# MOVE make_DAG() below up here
# This works since in_outArg is used to define the DAG based on the original input of the user, but is then updated based on the check in the following if statement.
make_DAG_output <- make_DAG(in_outDAG = in_outArg ,
                            exposure = exposure,
                            response = response,
                            mediator = mediator,
                            Splines_outlist_Var = Splines_outlist,
                            # splinesDefinedIn_in_outDAG = TRUE,
                            splinesDefinedIn_in_outDAG = splinesDefinedIn_in_outDAG,
                            addCustomExposureAdjustmentSet = addCustom,
                            customExposureAdjustmentSet = custom,
                            addCustomMediatorAdjustmentSet = addCustom,
                            customMediatorAdjustmentSet = custom )   # custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)"

## If is NULL, it means there was no update applied and no change is required.
## So if not Null, then in_outArg needs to be updated based on (1) dagitty check and (2) check required based on derivation.
if( !is.null(make_DAG_output$my_list_causal$in_outDAG_updatedAfterCheck) ){

        ##in_outArg is a parameter of the function but what to update it based on make_DAG (1) check requested by John Ferguson and (2) Dagitty check
        # MAYBE WE DO NOT WANT TO CHANGE THE INPUT PARAMETER in_outArg and rather we want to change
        # eval_make_formula(...., in_out = make_DAG_output$my_list_causal$in_outDAG_updatedAfterCheck, ....)
        ## check whether changing the parameter input in_outArg upsets anything else within the function?
         in_outArg <- make_DAG_output$my_list_causal$in_outDAG_updatedAfterCheck
}

###########################################################################
###########################################################################
###########################################################################

# If user has not fitted their own model_listArg then fit as follows
if( length(model_listArg) == 0 ){
      model_list_inputPointEstimate <- list()

      # model_list_usePointEstimate <- eval_make_formula(data = dataframe, in_out=in_outArg,model_list=model_list_inputPointEstimate, w=dataframe$weights, addCustom, custom)
      model_list_usePointEstimate <- eval_make_formula(data = dataframe, in_out = in_outArg, model_list=model_list_inputPointEstimate, w = weights, addCustom, custom)
      #   # TRY TO GET IN FUNCTION ENVIRONMENT E.G. model$terms <- eval(model$call$formula)
    #   # Return these from function and evaluate in this evironment!! Hopefully solve it.

      ## N.B. FOR LOOP HAS TO BE i AS MODELS DEFINED IN [[I]]
      for( i in 1:length(model_list_usePointEstimate)){
        eval(parse(text=model_list_usePointEstimate[[i]]))
         model_list_evalPointEstimate[[i]] <- model_list_inputPointEstimate[[i]]
      }
      # model_list_usePointEstimate <- eval(parse(text=model_list_usePointEstimate))
      # to_execute <- paste(model_list_text,"[[i]] <-", theform,sep='')
      # eval(parse(text=to_execute))
} else{
       model_list_evalPointEstimate <- model_listArg
}

# Select models for mediators input by user from model_listArg
  mediator_orderPointEstimate <- c()
  for(i in 1:length(model_list_evalPointEstimate)){
    mediator_orderPointEstimate[i] <-  as.character(formula(model_list_evalPointEstimate[[i]]))[2]
  }
  model_list_mediator_orderPointEstimate <- (1:length( mediator_orderPointEstimate ))[ mediator_orderPointEstimate %in% mediator]

  mediator_modelPointEstimate <- list()
  for(i in 1:length(model_list_mediator_orderPointEstimate)){
    mediator_modelPointEstimate[[i]] = model_list_evalPointEstimate[[ model_list_mediator_orderPointEstimate[i] ]]
  }

### NO NEED TO UPDATE AS SHOULD BE RUN ON ORIGINAL DATA AS IN causalPAFplot
# response_vs_mediator <- update(response_model_mediators, data = Bootstrap, weights = Bootstrap$weights )
#
# response_vs_phys <- update(response_model_exposure, data = Bootstrap, weights = Bootstrap$weights )


#######################
#######################
#######################

# model_list <- list()
# w <- rep(1,nrow(stroke_reduced))
# w[stroke_reduced$case==0] <- 0.9965
# w[stroke_reduced$case==1] <- 0.0035

#######################
#######################
# weights is input into function instead as some users may prefer a more detailed weighting calculation. So instead this is prepopulated as input into the functions.
#######################
# weightControl = 0.9965 # POPULATE THESE FROM FUNCTION
# weightCase = 0.0035  # POPULATE THESE FROM FUNCTION
# w <- rep(1,nrow(dataframe))
#
# column_response <- (1:length(colnames(dataframe)))[colnames(dataframe) == response ]
# w[ dataframe[,column_response] == 0 ] = weightControl
# w[ dataframe[,column_response] == 1 ] = weightCase
# dataframe <- mutate(dataframe, weigths_use = w)
#######################
#######################
#######################

# # response
# column <- (1:length(colnames(data)))[colnames(data) %in% response ]
#           y <- data[,column]
# deparse(substitute(response))
# w <- rep(1,nrow(dataframe))
# w[dataframe$case==0] <- 0.9965
# w[dataframe$case==1] <- 0.0035


# ##################################
# ##################################
# # If user has not fitted their own model_listArg then fit as follows
# if( length(model_listArg) == 0 ){
#       model_list_input <- list()
#       # model_list_use <- eval_make_formula(data = Bootstrap, in_out=in_outArg,model_list=model_listArg, w=Bootstrap$weights)
#       model_list_use <- eval_make_formula(data = dataframe, in_out=in_outArg,model_list=model_list_input, w=dataframe$weights, addCustom, custom)
#       #   # TRY TO GET IN FUNCTION ENVIRONMENT E.G. model$terms <- eval(model$call$formula)
#     #   # model$terms <- eval(model$call$formula)
#     #   model_list_use[[1]]$terms <- eval(model_list_use[[1]]$call$formula)
#     #   eval(model_list_use[[1]]$call$data )
#     #   eval(model_list_use[[1]]$call$family )
#     #   eval(model_list_use[[1]]$call$weights )
#     # #   glm(formula = phys ~ regionnn7 * ns(eage, df = 5) + esex * ns(eage,
#     # # df = 5) + subeduc + moteduc + fatduc, family = "binomial",
#     # # data = Bootstrap, weights = Bootstrap$weights)
#     #   glm(formula = model_list_use[[1]]$call$formula, family = model_list_use[[1]]$call$family,
#     # data = model_list_use[[1]]$call$data, weights = model_list_use[[1]]$call$weights )
#     #   # Return these from function and evaluate in this evironment!! Hopefully solve it.
#       # eval(parse(text=to_execute))
#                           ## model_listReturn[[i]] <- eval(parse(text=to_execute))
#                           #model_listReturn[[i]] <- eval( parse(text= paste(model_list_text,"[[i]]",sep='') ) )
#
#       ## N.B. FOR LOOP HAS TO BE i AS MODELS DEFINED IN [[I]]
#       for( i in 1:length(model_list_use)){
#         eval(parse(text=model_list_use[[i]]))
#          model_list_eval[[i]] <- model_list_input[[i]]
#       }
#       # model_list_use <- eval(parse(text=model_list_use))
#       # to_execute <- paste(model_list_text,"[[i]] <-", theform,sep='')
#       # eval(parse(text=to_execute))
# } else{
#        model_list_eval <- model_listArg
# }
#
# # Select models for mediators input by user from model_listArg
#   mediator_order <- c()
#   for(i in 1:length(model_list_eval)){
#     mediator_order[i] <-  as.character(formula(model_list_eval[[i]]))[2]
#   }
#   model_list_mediator_order <- (1:length( mediator_order ))[ mediator_order %in% mediator]
#
#   mediator_model <- list()
#   for(i in 1:length(model_list_mediator_order)){
#     mediator_model[[i]] = model_list_eval[[ model_list_mediator_order[i] ]]
#   }
#
# ###################################
# ###################################


################
## Maybe fit from make_DAG.R both below based on similar code to for loop below
##  resultExposure
##  resultMediator
################
################
### NEED TO RUN HERE WITH custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)" RATHER THAN custom = "~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) + "
# NEED TO UPDATE CUSTOM AS CURRENTLY make_formula() requires ~ but should change so runs without ~ needed
# custom = "~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) + "
# custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)"
# ############
# ## Moved make_DAG_output <- make_DAG(...) call up as needs to be used earlier
# ############
# make_DAG_output <- make_DAG(in_outDAG = in_outArg ,
#                             exposure = exposure,
#                             response = response,
#                             mediator = mediator,
#                             Splines_outlist_Var = Splines_outlist,
#                             splinesDefinedIn_in_outDAG = TRUE,
#                             addCustomExposureAdjustmentSet = addCustom,
#                             customExposureAdjustmentSet = custom,
#                             addCustomMediatorAdjustmentSet = addCustom,
#                             customMediatorAdjustmentSet = custom )   # custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)"


# make_DAG_output_resultExposure <- make_DAG(in_outDAG = in_outArg ,
#                                            exposure = exposure,
#                                            response = response,
#                                            mediator = mediator,
#                                            Splines_outlist_Var = Splines_outlist,
#                                            splinesDefinedIn_in_outDAG = TRUE,
#                                            addCustomExposureAdjustmentSet = addCustom,
#                                            customExposureAdjustmentSet = custom,
#                                            addCustomMediatorAdjustmentSet = addCustom,
#                                            customMediatorAdjustmentSet = custom )$resultExposure   # custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)"
#
# make_DAG_output_resultMediator <- make_DAG(in_outDAG = in_outArg ,
#                                            exposure = exposure,
#                                            response = response,
#                                            mediator = mediator,
#                                            Splines_outlist_Var = Splines_outlist,
#                                            splinesDefinedIn_in_outDAG = TRUE,
#                                            addCustomExposureAdjustmentSet = addCustom,
#                                            customExposureAdjustmentSet = custom,
#                                            addCustomMediatorAdjustmentSet = addCustom,
#                                            customMediatorAdjustmentSet = custom )$resultMediator   # custom = "regionnn7*ns(eage,df=5)+esex*ns(eage,df=5)"


# test$resultExposure
#
# test$resultMediator

eval_make_DAG_output <- eval_make_DAG(data = dataframe,
                                      regressionExposure = make_DAG_output$resultExposure,
                                      regressionMediator = make_DAG_output$resultMediator,
                                      response = response,
                                      response_model_mediators = response_model_mediators,
                                      response_model_exposure = response_model_exposure,
                                      w = weights )

# eval_make_DAG_output_regressionExposure_listReturn <- eval_make_DAG(data = dataframe,
#                                                                     regressionExposure = make_DAG_output_resultExposure,
#                                                                     regressionMediator = make_DAG_output_resultMediator,
#                                                                     response = response,
#                                                                     response_model_mediators = response_model_mediators,
#                                                                     response_model_exposure = response_model_exposure,
#                                                                     w = weights )$regressionExposure_listReturn
#
# eval_make_DAG_output_regressionMediator_listReturn <- eval_make_DAG(data = dataframe,
#                                                                     regressionExposure = make_DAG_output_resultExposure,
#                                                                     regressionMediator = make_DAG_output_resultMediator,
#                                                                     response = response,
#                                                                     response_model_mediators = response_model_mediators,
#                                                                     response_model_exposure = response_model_exposure,
#                                                                     w = weights )$regressionMediator_listReturn


#######
### NEED TO EVALUATE THEM NEXT
#######

#################################
#################################
#################################
# eval_make_DAG_output
#
# $regressionExposure_listReturn
# $regressionExposure_listReturn[[1]]
# [1] "make_DAG_output$resultExposure[[i]] <-glm(case ~  phys  +  ahei3tert + alcohfreqwk + fatduc + global_stress2 + moteduc + nevfcur + subeduc + regionnn7*ns(eage,df=5)+esex*ns(eage,df=5),data=dataframe,family='binomial',w=weights)"
#
#
# $regressionMediator_listReturn
# $regressionMediator_listReturn[[1]]
# [1] "make_DAG_output$resultMediator[[i]] <-glm(case ~  subhtn  +  ahei3tert + alcohfreqwk + ns(apob_apoa, knots = quantile(apob_apoa,c(.25,0.5,0.75)), Boundary.knots = quantile(apob_apoa,c(.001,0.95))) + fatduc + global_stress2 + moteduc + nevfcur + phys + subeduc + ns(whr,df=5) + regionnn7*ns(eage,df=5)+esex*ns(eage,df=5),data=dataframe,family='binomial',w=weights)"
#
# $regressionMediator_listReturn[[2]]
# [1] "make_DAG_output$resultMediator[[i]] <-glm(case ~  ns(apob_apoa, knots = quantile(apob_apoa,c(.25,0.5,0.75)), Boundary.knots = quantile(apob_apoa,c(.001,0.95)))  +  ahei3tert + alcohfreqwk + fatduc + global_stress2 + moteduc + nevfcur + phys + subeduc + subhtn + ns(whr,df=5) + regionnn7*ns(eage,df=5)+esex*ns(eage,df=5),data=dataframe,family='binomial',w=weights)"
#
# $regressionMediator_listReturn[[3]]
# [1] "make_DAG_output$resultMediator[[i]] <-glm(case ~  ns(whr,df=5)  +  ahei3tert + alcohfreqwk + ns(apob_apoa, knots = quantile(apob_apoa,c(.25,0.5,0.75)), Boundary.knots = quantile(apob_apoa,c(.001,0.95))) + fatduc + global_stress2 + moteduc + nevfcur + phys + subeduc + subhtn + regionnn7*ns(eage,df=5)+esex*ns(eage,df=5),data=dataframe,family='binomial',w=weights)"
#################################
#################################
#################################

## N.B. FOR LOOP HAS TO BE i AS MODELS DEFINED IN [[I]]
      # for( i in 1:length(model_list_usePointEstimate)){
      #   eval(parse(text=model_list_usePointEstimate[[i]]))
      #    model_list_evalPointEstimate[[i]] <- model_list_inputPointEstimate[[i]]
      # }

# Eval_regressionExposure_listReturn <- eval_make_DAG_output_regressionExposure_listReturn


  # response_model_exposure
  # response_model_mediators

###############################################
# if( length(response_model_exposure) == 0 ){
#
#         for(i in 1:length(eval_make_DAG_output$regressionExposure_listReturn) ){
#
#                   # eval(parse(text=model_list_usePointEstimate[[i]]))
#                   eval(parse(text = eval_make_DAG_output$regressionExposure_listReturn[[i]]))
#                   # eval(parse(text = eval_make_DAG_output_regressionExposure_listReturn[[i]]))
#                   # model_list_evalPointEstimate[[i]] <- model_list_inputPointEstimate[[i]]
#                   # THIS IS HOW THE REGRESSION IS CALLED WITHIN eval_make_DAG.R
#                   make_DAG_output$resultExposure[[i]] <- make_DAG_output$resultExposure[[i]]
#
#             }
#
# }else if( length(response_model_exposure) != 0 ){
#
#                     make_DAG_output$resultExposure <- list( eval_make_DAG_output$regressionExposure_listReturn )
#
#
# }else{
#   stop("Stopped since error in pointEstimate() as length(response_model_mediators) is neither != 0 or == 0.")
# }

  for(i in 1:length(eval_make_DAG_output$regressionExposure_listReturn) ){

                  if( length(response_model_exposure) == 0){

                          # eval(parse(text=model_list_usePointEstimate[[i]]))
                          eval(parse(text = eval_make_DAG_output$regressionExposure_listReturn[[i]]))
                          # eval(parse(text = eval_make_DAG_output_regressionExposure_listReturn[[i]]))
                          # model_list_evalPointEstimate[[i]] <- model_list_inputPointEstimate[[i]]
                          # THIS IS HOW THE REGRESSION IS CALLED WITHIN eval_make_DAG.R
                          make_DAG_output$resultExposure[[i]] <- make_DAG_output$resultExposure[[i]]

                  }else if( length(response_model_exposure) != 0 ){

                          make_DAG_output$resultExposure[[i]] <- eval_make_DAG_output$regressionExposure_listReturn[[i]]

                  }else{
                    stop("Error in pointEstimate with length(response_model_exposure).")
                  }

            }

##################################################
# if( length(response_model_mediators ) == 0 ){
#
#         for(i in 1:length(eval_make_DAG_output$regressionMediator_listReturn) ){
#
#                   # eval(parse(text=model_list_usePointEstimate[[i]]))
#                   eval(parse(text = eval_make_DAG_output$regressionMediator_listReturn[[i]]))
#                   # eval(parse(text = eval_make_DAG_output_regressionMediator_listReturn[[i]]))
#                   # model_list_evalPointEstimate[[i]] <- model_list_inputPointEstimate[[i]]
#                   make_DAG_output$resultMediator[[i]] <- make_DAG_output$resultMediator[[i]]
#
#             }
#
# }else if( length(response_model_mediators ) != 0 ){
#
#               make_DAG_output$resultMediator <- list( eval_make_DAG_output$regressionMediator_listReturn )
#
# }else{
#   stop("Stopped since error in pointEstimate() as length(response_model_mediators) is neither != 0 or == 0.")
# }


# Eval_regressionMediator_listReturn <- eval_make_DAG_output_regressionMediator_listReturn

for(i in 1:length(eval_make_DAG_output$regressionMediator_listReturn) ){


                  if( length( response_model_mediators ) == 0){

                          # eval(parse(text=model_list_usePointEstimate[[i]]))
                          eval(parse(text = eval_make_DAG_output$regressionMediator_listReturn[[i]]))
                          # eval(parse(text = eval_make_DAG_output_regressionMediator_listReturn[[i]]))
                          # model_list_evalPointEstimate[[i]] <- model_list_inputPointEstimate[[i]]
                          make_DAG_output$resultMediator[[i]] <- make_DAG_output$resultMediator[[i]]

                  }else if( length( response_model_mediators ) != 0 ){

                          make_DAG_output$resultMediator[[i]] <- eval_make_DAG_output$regressionMediator_listReturn[[i]]

                  }else{
                    stop("Error in pointEstimate with length( response_model_mediators ).")
                  }

            }


#####################################

###############
################
################

# #####
# ## MOC NOTE: index in for loop here may need to be "i" sunce make-formula function uses "i" in it and passes it into this below as "i".
# #####
# for(i in 1:length(in_out[[2]])){
#
#
#         column <- (1:length(colnames(stroke_reduced)))[colnames(stroke_reduced) %in% in_out[[2]][i]]
#         formula_text <- make_formula(in_out[[1]][[i]],in_out[[2]][i])
#         y <- stroke_reduced[,column]
#         if(length(table(y))==2){
#                 theform <- paste("glm(",formula_text,",data=stroke_reduced,family='binomial',w=w)",sep='')
#         }
#         if(length(table(y))>2 & is.factor(y)){
#                 theform <- paste("polr(",formula_text,",data=stroke_reduced,w=w)",sep='')
#         }
#         if(length(table(y))>2 & is.numeric(y)){
#                 theform <- paste("lm(",formula_text,",data=stroke_reduced,w=w)",sep='')
#         }
#         to_execute <- paste("model_list[[i]] <-", theform,sep='')
#         eval(parse(text=to_execute))
# }



#### MOC: Added in all 3 mediators in one model. Separate models are listed underneath as used previously in earlier versions of model
# response_vs_mediator <-  glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) +  subeduc+ moteduc+ fatduc+ phys+ ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2+ subhtn + ns(apob_apoa, knots = quantile(apob_apoa,c(.25,0.5,0.75)), Boundary.knots = quantile(apob_apoa,c(.001,0.95)))+ns(whr,df=5)",data = stroke_reduced,family='binomial',w=w)
# could be more than 1 dimensional
# response_vs_mediator <- eval_make_DAG_output$regressionMediator_listReturn
##### for loop above evaluates make_DAG_output$resultMediator
response_vs_mediatorPointEstimate <- make_DAG_output$resultMediator


# ###  Model that estimates causal effect of high blood pressure on stroke
#
# # MOC: USED IN PREVIOUS VERSIONS OF MODEL, BUT UPDATED TO INCLUDE ALL 3 MEDIATORS IN "Response_vs_mediator"
#  response_vs_HBP <- glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) +  subeduc+ moteduc+ fatduc+ phys+ ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2+ subhtn",data = stroke_reduced,family='binomial',w=w)
#
# # MOC: USED IN PREVIOUS VERSIONS OF MODEL, BUT UPDATED TO INCLUDE ALL 3 MEDIATORS IN "Response_vs_mediator"
#   response_vs_apob_apoa <- glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) +  subeduc+ moteduc+ fatduc+ phys+ ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2+ ns(apob_apoa, knots = quantile(stroke_reduced$apob_apoa,c(.25,0.5,0.75)), Boundary.knots = quantile(stroke_reduced$apob_apoa,c(.001,0.95)))", data = stroke_reduced,family = 'binomial', w = w )
#
#
# # MOC: USED IN PREVIOUS VERSIONS OF MODEL, BUT UPDATED TO INCLUDE ALL 3 MEDIATORS IN "Response_vs_mediator"
#   response_vs_whr <- glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) +  subeduc+ moteduc+ fatduc+ phys+ ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2+ ns(whr , df = 5)",data=stroke_reduced,family='binomial',w=w)

#######
#######

# MOC: leaving this without mediators
# response_vs_phys <- glm("case ~ regionnn7*ns(eage,df=5)+esex*ns(eage,df=5) +  subeduc+ moteduc+ fatduc+ phys+ ahei3tert+ nevfcur+ alcohfreqwk+ global_stress2",data=stroke_reduced,family='binomial',w=w)
# could be more than 1 dimensional
# response_vs_phys <- eval_make_DAG_output$regressionExposure_listReturn
# for loop above evaluates make_DAG_output$resultExposure
response_vs_physPointEstimate <- make_DAG_output$resultExposure

#####################################
#####################################
# add in text "PointEstimate"
#####################################

  results_mediator_simulationStorePointEstimate <- list()
  for(i in 1:length(mediator)){
    results_mediator_simulationStorePointEstimate[[i]] <- matrix(nrow = NumSimulation,ncol=5)
    # colnames(results_mediator_simulationStorePointEstimate[[i]]) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
    colnames(results_mediator_simulationStorePointEstimate[[i]]) <- c("Total PAF","PAF_{Direct,M^j}","PAF_{Indirect,M^j}","PS-PAF_{A->M^j=>Y}","Direct PAF_{A->Y}")
  }

  for(med in 1:length(results_mediatorPointEstimate) ){

          for(i in 1:NumSimulation){

# EXPOSURE COULD BE MORE THAN 1 DIMENSIONAL BUT ONLY CODED HERE FRO 1 DEMSIONAL RESULT I.E. MIGHT NEED FOR LOOP FOR EACH EXPOSURE

              results_mediator_simulationStorePointEstimate[[med]][i,1:3] <- indirect_PAF_Sjolander_onesimulation(
                data_frame = dataframe,
                exposure=exposure, # is it an issue that exposure = exposure?
                mediator=mediator[med],
                response = response, # is it an issue that response = response?
                # mediator_model = mediator_modelPointEstimate[[med]], # CHECK IF THIS CHANGE WORKS i.e. ADDING ON [[med]]. # Is it an issue that mediator_model = mediator_model? AND THAT THIS NOW CAN BE MORE THAN 1 DIMENSIONAL
                mediator_model = mediator_modelPointEstimate,
                # response_model=response_vs_mediator,
                response_model = response_vs_mediatorPointEstimate[[med]],  # CHECK IF THIS CHANGE WORKS i.e. ADDING ON [[med]]
                # response_model_2=response_vs_phys,
                response_model_2=response_vs_physPointEstimate[[1]], # EXPOSURE COULD BE MORE THAN 1 DIMENSIONAL BUT ONLY CODED HERE FRO 1 DEMSIONAL RESULT I.E. MIGHT NEED FOR LOOP FOR EACH EXPOSURE
                weights= weights)  # SHOULD BE = weights

              results_mediator_simulationStorePointEstimate[[med]][i,4] <- path_specific_onesimulation(
                data_frame = dataframe,
                exposure = exposure, # is it an issue that exposure = exposure?
                mediator = mediator[med],
                response = response, # is it an issue that response = response?
                # mediator_model = mediator_modelPointEstimate[[med]], # CHECK IF THIS CHANGE WORKS i.e. ADDING ON [[med]]. # is it an issue that mediator_model = mediator_model?
                mediator_model = mediator_modelPointEstimate,
                response_model=response_vs_mediatorPointEstimate[[med]],  # CHECK IF THIS CHANGE WORKS i.e. ADDING ON [[med]]
                response_model_2=response_vs_physPointEstimate[[1]], # EXPOSURE COULD BE MORE THAN 1 DIMENSIONAL BUT ONLY CODED HERE FRO 1 DEMSIONAL RESULT I.E. MIGHT NEED FOR LOOP FOR EACH EXPOSURE
                weights= weights)  # SHOULD BE = weights

              results_mediator_simulationStorePointEstimate[[med]][i,5] <- overall_direct(
                data_frame = dataframe,
                exposure=exposure, # is it an issue that exposure = exposure?
                mediator = mediator[med],
                response = response, # is it an issue that response = response?
                # mediator_model = mediator_modelPointEstimate[[med]], # CHECK IF THIS CHANGE WORKS i.e. ADDING ON [[med]]. # is it an issue that mediator_model = mediator_model?
                mediator_model = mediator_modelPointEstimate,
                response_model=response_vs_mediatorPointEstimate[[med]],  # CHECK IF THIS CHANGE WORKS i.e. ADDING ON [[med]]
                response_model_2=response_vs_physPointEstimate[[1]], # EXPOSURE COULD BE MORE THAN 1 DIMENSIONAL BUT ONLY CODED HERE FRO 1 DEMSIONAL RESULT I.E. MIGHT NEED FOR LOOP FOR EACH EXPOSURE
                weights= weights)  # SHOULD BE = weights

              flush.console()
              print(i)
          }

      # v IS ONLY NEEDED FOR BOOTSTRAP
      # results_mediatorPointEstimate[[med]][v,] = apply(results_mediator_simulationStorePointEstimate[[med]],2,mean)
        results_mediatorPointEstimate[[med]] = apply(results_mediator_simulationStorePointEstimate[[med]],2,mean)

  }

###########
###########
##### ONLY FOR BOOTSTRAP
###########
###########
# results_mediator_tablePointEstimate <- list()
# for(i in 1:length(mediator)){
#     results_mediator_tablePointEstimate[[i]] <- matrix(nrow = 3,ncol=5)
#     results_mediator_tablePointEstimate[[i]][1,] <- apply(results_mediatorPointEstimate[[i]],2,mean)
#     results_mediator_tablePointEstimate[[i]][2,] <- apply(results_mediatorPointEstimate[[i]],2,mean) - 1.96*apply(results_mediatorPointEstimate[[i]],2,sd)
#     results_mediator_tablePointEstimate[[i]][3,] <- apply(results_mediatorPointEstimate[[i]],2,mean) + 1.96*apply(results_mediatorPointEstimate[[i]],2,sd)
#     colnames(results_mediator_tablePointEstimate[[i]]) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
#     rownames(results_mediator_tablePointEstimate[[i]]) <- c("Mean", "Lower 95% C.I.","Upper 95% C.I.")
#     results_mediator_tablePointEstimate[[i]]
# }



# my_listPointEstimate <- list("plot" = plotList, "mediators" = mediator, "table" = results_mediator_table )
# return("results_mediator_tablePointEstimate" = results_mediator_tablePointEstimate)
my_listPointEstimate <- list("results_mediatorPointEstimate" = results_mediatorPointEstimate, "mediators" = mediator, "response_vs_physPointEstimate" = response_vs_physPointEstimate , "response_vs_mediatorPointEstimate" = response_vs_mediatorPointEstimate )


return( my_listPointEstimate )

# make_DAG_output$resultExposure
# make_DAG_output$resultMediator

#####################################
#####################################
#####################################

#   results_subhtn_simulationStore = matrix(nrow = NumSimulation,ncol=5)
#   colnames(results_subhtn_simulationStore) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
#
# for(i in 1:NumSimulation){
#   results_subhtn_simulationStore[i,1:3] <- indirect_PAF_Sjolander_onesimulation(data_frame = stroke_reduced, mediator="subhtn")
#   # data_frame, exposure, mediator, response, mediator_model, response_model, response_model_2, weights
#   results_subhtn_simulationStore[i,4] <- path_specific_onesimulation(data_frame = stroke_reduced, mediator="subhtn")
#   results_subhtn_simulationStore[i,5] <- overall_direct(data_frame = stroke_reduced, mediator="subhtn")
#   flush.console()
#   print(i)
# }
# results_subhtn[1,] = apply(results_subhtn_simulationStore,2,mean)
#
#
#   results_whr_simulationStore = matrix(nrow = NumSimulation,ncol=5)
#   colnames(results_whr_simulationStore) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
#
# for(i in 1:NumSimulation){
#   results_whr_simulationStore[i,1:3] <- indirect_PAF_Sjolander_onesimulation(data_frame = stroke_reduced, mediator="whr")
#   results_whr_simulationStore[i,4] <- path_specific_onesimulation(data_frame = stroke_reduced, mediator="whr")
#   results_whr_simulationStore[i,5] <- overall_direct(data_frame = stroke_reduced, mediator="whr")
#   flush.console()
#   print(i)
#  }
# results_whr[1,] = apply(results_whr_simulationStore,2,mean)
#
#   results_apob_apoa_simulationStore = matrix(nrow = NumSimulation,ncol=5)
#   colnames(results_apob_apoa_simulationStore) <- c("overall","direct Sjolander","indirect Sjolander","path specific","overall Direct")
#
#   for(i in 1:NumSimulation){
#   results_apob_apoa_simulationStore[i,1:3] <- indirect_PAF_Sjolander_onesimulation(data_frame = stroke_reduced, mediator="apob_apoa")
#   results_apob_apoa_simulationStore[i,4] <- path_specific_onesimulation(data_frame = stroke_reduced, mediator="apob_apoa")
#   results_apob_apoa_simulationStore[i,5] <- overall_direct(data_frame = stroke_reduced, mediator="apob_apoa")
#   flush.console()
#   print(i)
#   }
#
#   results_apob_apoa[1,] = apply(results_apob_apoa_simulationStore,2,mean)
#
#   results_subhtn
#   results_whr
#   results_apob_apoa
#
#  #############################
#   #############################
#   #############################
#
# ####################
# ####################
# ####################
#
#
#
# # ##########################################
# # ##########################################
# # ##########################################
# # ## MOC CHECK
# # ##########################################
# # ##########################################
# # ##########################################
# # indirect_PAF_Sjolander_onesimulation(data_frame = stroke_reduced, mediator="subhtn")
# # path_specific_onesimulation(data_frame = stroke_reduced, mediator="subhtn")
# # overall_direct(data_frame = stroke_reduced, mediator="subhtn")
# #
# # indirect_PAF_Sjolander_onesimulation(data_frame = stroke_reduced, mediator="whr")
# # path_specific_onesimulation(data_frame = stroke_reduced, mediator="whr")
# # overall_direct(data_frame = stroke_reduced,mediator="whr")
# #
# # indirect_PAF_Sjolander_onesimulation(data_frame = stroke_reduced, mediator="apob_apoa")
# # path_specific_onesimulation(data_frame = stroke_reduced, mediator="apob_apoa")
# # overall_direct(data_frame = stroke_reduced,mediator="apob_apoa")
#


}