Update nonlinear_auxiliary.R

konfound-project · wwang93 · Feb 19, 2024 · Feb 17, 2024 · Feb 17, 2024 · Feb 17, 2024
commit 5064575b7dec5b1e63af5d3018c5cca2710eb1fa
diff --git a/R/nonlinear_auxiliary.R b/R/nonlinear_auxiliary.R
@@ -692,7 +692,7 @@ result <- list(final_switch = final, User_enter_value = table_start, Transfer_Ta
 return(result)
 }
 
-getswitch_fisher <- function(a, b, c, d, thr_p = 0.05, switch_trm = T){
+getswitch_fisher <- function(a, b, c, d, thr_p = 0.05, switch_trm = TRUE){
   if (a > 0 & b > 0 & c > 0 & d > 0){
     odds_ratio <- fisher_oddsratio(a, b, c, d)
   } else {

diff --git a/R/cop_pse_auxiliary.R b/R/cop_pse_auxiliary.R
                        sample.nobs = n_obs)
        },
        error = function(e){
            flag_cov = FALSE
            return(flag_cov)
        },
        warning = function(w){
            flag_cov = FALSE
            return(flag_cov)
        }
    )
                        sample.nobs = n_obs)
        },
        error = function(e){
            flag_cor = FALSE
            return(flag_cor)
        },
        warning = function(w){
            flag_cor = FALSE
            return(flag_cor)
        }
    )
                        sample.nobs = n_obs)
        },
        error = function(e){
            flag_cov = FALSE
            return(flag_cov)
        },
        warning = function(w){
            flag_cov = FALSE
            return(flag_cov)
        }
    )
                        sample.nobs = n_obs)
        },
        error = function(e){
            flag_cov = FALSE
            return(flag_cov)
        },
        warning = function(w){
            flag_cov = FALSE
            return(flag_cov)
        }
    )
diff --git a/R/konfound.R b/R/konfound.R
  
  # Stop messages
  if (!(class(model_object)[1] %in% c("lm", "glm", "lmerMod"))) {
    stop("konfound() is currently implemented for models estimated with 
         lm(), glm(), and lme4::lmer(); consider using pkonfound() instead")
  }
  
  # Dealing with non-standard evaluation
  
  if (inherits(model_object, "glm") & two_by_two == TRUE) {
    
    if(is.null(n_treat)) stop("Please provide a value for n_treat to use 
                              this functionality with a dichotomous predictor")
      
    output <- konfound_glm_dichotomous(
      model_object = model_object,
diff --git a/R/mkonfound.R b/R/mkonfound.R
  df_vec <- pull(select(d, !!df_enquo))

  if (length(t_vec) <= 1) {
    stop("To carry out sensitivity analysis 
         for a single study, use pkonfound()")
  }
  
  results_df <- purrr::map2_dfr(.x = t_vec, .y = df_vec, 
diff --git a/R/pkonfound.R b/R/pkonfound.R
                      FR2max,
                      FR2max_multiplier = 1.3,
                      to_return = "print") {
  if ("table" %in% to_return) stop("a table can only be 
                                   output when using konfound")
  
   if (index == "COP") {
     
  } else if(!is.null(a)) {
    # error handling
    if (is.null(a) | is.null(b) | is.null(c) | is.null(d)) {
      stop("Please enter values for a, b, c, 
           and d to use the 2 x 2 table functionality")
    }
    
    out <- tkonfound(a = a, 
diff --git a/R/test_cop.R b/R/test_cop.R
  var_z <- sdz <- 1
  
  ## error message if input is inappropriate
  if (!(std_err > 0)) {stop("Did not run! Standard error needs to be 
                            greater than zero.")}
  if (!(sdx > 0)) {stop("Did not run! Standard deviation of x needs to be 
                        greater than zero.")}
  if (!(sdy > 0)) {stop("Did not run! Standard deviation of y needs to be 
                        greater than zero.")}
  if (!(n_obs > n_covariates + 3)) {
    stop("Did not run! There are too few observations relative to the 
         number of observations and covariates. Please specify a less 
         complex model to use KonFound-It.")}
  if (!(R2 < FR2max)) {stop("Did not run! R2 Max needs to be greater than R2.")}
  if (!(FR2max < 1)) {stop("Did not run! R2 Max needs to be less than 1.")}
  if (!(1-((sdy^2/sdx^2)*(1-R2)/((df+1) * std_err^2))>0)) {
    stop("Did not run! Entered values produced Rxz^2 <=0, 
         consider adding more significant digits to your entered values.")}

  # an indicator of whether the use specified est_eff is negative,
  # 1 is yes negative
    cat("This function calculates delta* and the exact value of delta.")
    cat("\n")
    if (negest == 1) {
      cat("Using the absolute value of the estimated effect, 
          results can be interpreted by symmetry.")
      cat("\n")
    }
    cat(sprintf("delta* is %.3f (assuming no covariates in the baseline model M1), 
                the exact delta is %.3f, with a bias of %.3f%%.", 
                delta_star, delta_exact, delta_pctbias))
    cat("\n")
    cat(sprintf("With delta*, the coefficient in the final model will be %.3f. 
                With the exact delta, the coefficient will be %.3f.",  
                eff_x_M3_oster,eff_x_M3))
    cat("\n")
    cat("Use to_return = raw_ouput to see more specific results 
        and graphic presentation of the result.")
    cat("\n")
    cat("\n")
    cat("This function also calculates conditional RIR that 
        invalidates the statistical inference.")
    cat("\n")
    cat("If the replacement cases have a fixed value, then RIR =", cond_RIR_fixedY, ".")
    cat("\n")
    cat("If the replacement cases follow a null distribution, 
        then RIR =", cond_RIR_null, ".")
    cat("\n")
    cat("If the replacement cases satisfy rxy|Z = 0, then RIR =", cond_RIR_rxyz, ".")
    cat("\n")
diff --git a/R/test_pse.R b/R/test_pse.R
    df = n_obs - n_covariates - 3
    
    ## error message if input is inappropriate
    if (!(std_err > 0)) {stop("Did not run! Standard error needs 
                              to be greater than zero.")}
    if (!(sdx > 0)) {stop("Did not run! Standard deviation of 
                          x needs to be greater than zero.")}
    if (!(sdy > 0)) {stop("Did not run! Standard deviation of 
                          y needs to be greater than zero.")}
    if (!(n_obs > n_covariates + 3)) {
      stop("Did not run! There are too few observations relative 
           to the number of observations and covariates. 
           Please specify a less complex model to use KonFound-It.")}
    if (!(0 < R2)) {stop("Did not run! R2 needs to be greater than zero.")}
    if (!(R2 < 1)) {stop("Did not run! R2 needs to be less than one.")}
    if (!(1-((sdy^2/sdx^2)*(1-R2)/((df+1) * std_err^2))>0)) {
      stop("Did not run! Entered values produced Rxz^2 <=0, 
           consider adding more significant digits to your entered values.")}
    
    ## now standardize 
    beta_thr <- eff_thr * sdx / sdy
    }
    
    if (to_return == "print") {
        cat("This function calculates the conditions that set the 
            estimated effect approximately equal to the threshold 
            while preserving the standard error.")
        cat("\n")
        cat(sprintf("The correlation between X and CV is %.3f, 
                    and the correlation between Y and CV is %.3f.", rxcv, rycv))
        cat("\n")
        cat(sprintf("Conditional on the covariates, the correlation between X 
                    and CV is %.3f, and the correlation between Y and CV 
                    is %.3f.", rxcvGz, rycvGz))
        cat("\n")
        cat(sprintf("Including such CV, the coefficient changes to %.3f, 
                    and standard error is %.3f.", eff_x_M3, se_x_M3))
        cat("\n")
        cat("Use to_return = raw_ouput to see more specific results.")
    }
diff --git a/R/test_sensitivity.R b/R/test_sensitivity.R
                             to_return,
                             model_object,
                             tested_variable) {
    if (nu != 0) warning("You entered a non-zero null hypothesis about an effect; 
                         this is being interpreted in terms of a partial correlation. 
                         Sampling variability is not accounted for.")
    
    ## error message if input is inappropriate
    if (!(std_err > 0)) {
        stop("Did not run! Standard error needs to be greater than zero.")}
    if (!(n_obs > n_covariates + 3)) {
        stop("Did not run! There are too few observations relative to the number 
             of observations and covariates. Please specify a less complex model 
             to use KonFound-It.")}
    
    # calculating statistics used in every case
    if (est_eff < 0) {
    } else if (to_return == "table") {
        return(output_table(model_object, tested_variable))
    } else {
        stop("to_return must be set to 'raw_output', 'print', 'table', 
             'thresh_plot', or 'corr_plot' or some combination thereof")
    }
 }
diff --git a/R/test_sensitivity_ln.R b/R/test_sensitivity_ln.R
    
    ## error message if input is inappropriate
    if (!(std_err > 0)) {
      stop("Did not run! Standard error needs to be greater than zero.")}
    if (!(n_obs > n_covariates + 3)) {
      stop("Did not run! There are too few observations relative to 
           the number of observations and covariates. Please specify a 
           less complex model to use KonFound-It.")}
    
    thr_t <- cal_thr_t(est_eff, alpha, tails, n_obs, n_covariates)
    
    # stop message
    if (n_obs <= 0 || n_treat <= 0) {
        stop("Please enter positive integers for sample size 
             and number of treatment group cases.")
    }
    if (n_obs <= n_treat) {
        stop("The total sample size should be larger than 
             the number of treatment group cases.")
    }
    
    odds_ratio <- exp(est_eff)
            change, sprintf("you would need to replace %d", RIR), RIRway, "cases")
        
        if (replace == "control") {
            conclusion1a <- sprintf("with cases for which the probability of 
                                    failure in the control group applies (RIR = %d).", RIR)
        } else {
            conclusion1a <- sprintf("with cases for which the probability of failure 
                                    in the entire sample applies (RIR = %d).", RIR)
        }
        
        conclusion1b <- paste(
        conclusion1 <- paste(
            change, c("only transferring cases from"), transferway, "is not enough.")
        
        conclusion1b <- paste(sprintf("We also need to transfer %d cases from", 
                                      final_solution$final_extra),
                              transferway_extra, c("as shown, from the User-entered 
                                                   Table to the Transfer Table. "))
        
        conclusion1c <- paste(sprintf("This means we need to replace %d of", RIR), RIRway, 
                              sprintf("with null hypothesis cases; and replace %d", 
                                      RIR_extra), RIRway_extra, 
                              c("with null hypothesis cases to change the inference.")
        )
    }
diff --git a/R/tkonfound.R b/R/tkonfound.R
                        the control group applies (RIR = %d). ", RIR))
        } else {
            conclusion1b <- paste0(
                sprintf(" cases for which the probability of failure in 
                        the entire group applies (RIR = %d). ", RIR))    
        }
        
        conclusion1c <- paste0(
        
        if (replace == "control") {
            conclusion1b <- paste0(
                sprintf(" cases for which the probability of failure in 
                        the control group applies (RIR = %d). ", RIR))
        } else {
            conclusion1b <- paste0(
                sprintf(" cases for which the probability of failure in 
                        the entire group applies (RIR = %d). ", RIR))    
        }
        
        conclusion1c <- paste0(
    
    if (test == "chisq"){
        conclusion2 <- sprintf(
            "For the User-entered Table, we have a Pearson's chi square of %.3f, 
          with p-value of %.3f:", chisq_ob, p_ob)
        conclusion3 <- sprintf(
            "For the Transfer Table, we have a Pearson's chi square of %.3f, 
          with p-value of %.3f:", chisq_final, p_final)
    }
    
    if (test == "fisher"){
diff --git a/R/tkonfound_fig.R b/R/tkonfound_fig.R
 }

 if (neg_thr_pdif <= max(zoom$pdif) && neg_thr_pdif >= min(zoom$pdif)) {
  fig2 <- fig2 + ggplot2::geom_hline(yintercept = neg_thr_pdif, 
                                     linetype = "dashed", color="red", size = 1)
 }

 ###plot figure 3 RIS% as sample size gets larger, using t statistic as the criterion
	sample.nobs = n_obs)
	},
	error = function(e){
	flag_cov = FALSE
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	return(flag_cov)
	},
	warning = function(w){
	flag_cov = FALSE
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	return(flag_cov)
	}
	)
	sample.nobs = n_obs)
	},
	error = function(e){
	flag_cor = FALSE
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	return(flag_cor)
	},
	warning = function(w){
	flag_cor = FALSE
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	return(flag_cor)
	}
	)
	sample.nobs = n_obs)
	},
	error = function(e){
	flag_cov = FALSE
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	return(flag_cov)
	},
	warning = function(w){
	flag_cov = FALSE
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	return(flag_cov)
	}
	)
	sample.nobs = n_obs)
	},
	error = function(e){
	flag_cov = FALSE
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	return(flag_cov)
	},
	warning = function(w){
	flag_cov = FALSE
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	return(flag_cov)
	}
	)

	# Stop messages
	if (!(class(model_object)[1] %in% c("lm", "glm", "lmerMod"))) {
	stop("konfound() is currently implemented for models estimated with
	lm(), glm(), and lme4::lmer(); consider using pkonfound() instead")
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	}

	# Dealing with non-standard evaluation

	if (inherits(model_object, "glm") & two_by_two == TRUE) {

	if(is.null(n_treat)) stop("Please provide a value for n_treat to use
	this functionality with a dichotomous predictor")
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	output <- konfound_glm_dichotomous(
	model_object = model_object,
	df_vec <- pull(select(d, !!df_enquo))

	if (length(t_vec) <= 1) {
	stop("To carry out sensitivity analysis
	for a single study, use pkonfound()")
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	}

	results_df <- purrr::map2_dfr(.x = t_vec, .y = df_vec,
	FR2max,
	FR2max_multiplier = 1.3,
	to_return = "print") {
	if ("table" %in% to_return) stop("a table can only be
	output when using konfound")
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	if (index == "COP") {

	} else if(!is.null(a)) {
	# error handling
	if (is.null(a) \| is.null(b) \| is.null(c) \| is.null(d)) {
	stop("Please enter values for a, b, c,
	and d to use the 2 x 2 table functionality")
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	}

	out <- tkonfound(a = a,
	var_z <- sdz <- 1

	## error message if input is inappropriate
	if (!(std_err > 0)) {stop("Did not run! Standard error needs to be
	greater than zero.")}
	if (!(sdx > 0)) {stop("Did not run! Standard deviation of x needs to be
	greater than zero.")}
	if (!(sdy > 0)) {stop("Did not run! Standard deviation of y needs to be
	greater than zero.")}
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	if (!(n_obs > n_covariates + 3)) {
	stop("Did not run! There are too few observations relative to the
	number of observations and covariates. Please specify a less
	complex model to use KonFound-It.")}
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	if (!(R2 < FR2max)) {stop("Did not run! R2 Max needs to be greater than R2.")}
	if (!(FR2max < 1)) {stop("Did not run! R2 Max needs to be less than 1.")}
	if (!(1-((sdy^2/sdx^2)(1-R2)/((df+1) std_err^2))>0)) {
	stop("Did not run! Entered values produced Rxz^2 <=0,
	consider adding more significant digits to your entered values.")}
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	# an indicator of whether the use specified est_eff is negative,
	# 1 is yes negative
	cat("This function calculates delta* and the exact value of delta.")
	cat("\n")
	if (negest == 1) {
	cat("Using the absolute value of the estimated effect,
	results can be interpreted by symmetry.")
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	cat("\n")
	}
	cat(sprintf("delta* is %.3f (assuming no covariates in the baseline model M1),
	the exact delta is %.3f, with a bias of %.3f%%.",
	delta_star, delta_exact, delta_pctbias))
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	cat("\n")
	cat(sprintf("With delta*, the coefficient in the final model will be %.3f.
	With the exact delta, the coefficient will be %.3f.",
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	eff_x_M3_oster,eff_x_M3))
	cat("\n")
	cat("Use to_return = raw_ouput to see more specific results
	and graphic presentation of the result.")
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	cat("\n")
	cat("\n")
	cat("This function also calculates conditional RIR that
	invalidates the statistical inference.")
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	cat("\n")
	cat("If the replacement cases have a fixed value, then RIR =", cond_RIR_fixedY, ".")
	cat("\n")
	cat("If the replacement cases follow a null distribution,
	then RIR =", cond_RIR_null, ".")
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	cat("\n")
	cat("If the replacement cases satisfy rxy\|Z = 0, then RIR =", cond_RIR_rxyz, ".")
	cat("\n")
	df = n_obs - n_covariates - 3

	## error message if input is inappropriate
	if (!(std_err > 0)) {stop("Did not run! Standard error needs
	to be greater than zero.")}
	if (!(sdx > 0)) {stop("Did not run! Standard deviation of
	x needs to be greater than zero.")}
	if (!(sdy > 0)) {stop("Did not run! Standard deviation of
	y needs to be greater than zero.")}
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	if (!(n_obs > n_covariates + 3)) {
	stop("Did not run! There are too few observations relative
	to the number of observations and covariates.
	Please specify a less complex model to use KonFound-It.")}
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	if (!(0 < R2)) {stop("Did not run! R2 needs to be greater than zero.")}
	if (!(R2 < 1)) {stop("Did not run! R2 needs to be less than one.")}
	if (!(1-((sdy^2/sdx^2)(1-R2)/((df+1) std_err^2))>0)) {
	stop("Did not run! Entered values produced Rxz^2 <=0,
	consider adding more significant digits to your entered values.")}
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	## now standardize
	beta_thr <- eff_thr * sdx / sdy
	}

	if (to_return == "print") {
	cat("This function calculates the conditions that set the
	estimated effect approximately equal to the threshold
	while preserving the standard error.")
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	cat("\n")
	cat(sprintf("The correlation between X and CV is %.3f,
	and the correlation between Y and CV is %.3f.", rxcv, rycv))
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	cat("\n")
	cat(sprintf("Conditional on the covariates, the correlation between X
	and CV is %.3f, and the correlation between Y and CV
	is %.3f.", rxcvGz, rycvGz))
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	cat("\n")
	cat(sprintf("Including such CV, the coefficient changes to %.3f,
	and standard error is %.3f.", eff_x_M3, se_x_M3))
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	cat("\n")
	cat("Use to_return = raw_ouput to see more specific results.")
	}
	to_return,
	model_object,
	tested_variable) {
	if (nu != 0) warning("You entered a non-zero null hypothesis about an effect;
	this is being interpreted in terms of a partial correlation.
	Sampling variability is not accounted for.")
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	## error message if input is inappropriate
	if (!(std_err > 0)) {
	stop("Did not run! Standard error needs to be greater than zero.")}
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	if (!(n_obs > n_covariates + 3)) {
	stop("Did not run! There are too few observations relative to the number
	of observations and covariates. Please specify a less complex model
	to use KonFound-It.")}
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	# calculating statistics used in every case
	if (est_eff < 0) {
	} else if (to_return == "table") {
	return(output_table(model_object, tested_variable))
	} else {
	stop("to_return must be set to 'raw_output', 'print', 'table',
	'thresh_plot', or 'corr_plot' or some combination thereof")
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	}
	}
	the control group applies (RIR = %d). ", RIR))
	} else {
	conclusion1b <- paste0(
	sprintf(" cases for which the probability of failure in
	the entire group applies (RIR = %d). ", RIR))
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	}

	conclusion1c <- paste0(

	if (replace == "control") {
	conclusion1b <- paste0(
	sprintf(" cases for which the probability of failure in
	the control group applies (RIR = %d). ", RIR))
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	} else {
	conclusion1b <- paste0(
	sprintf(" cases for which the probability of failure in
	the entire group applies (RIR = %d). ", RIR))
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	}

	conclusion1c <- paste0(

	if (test == "chisq"){
	conclusion2 <- sprintf(
	"For the User-entered Table, we have a Pearson's chi square of %.3f,
	with p-value of %.3f:", chisq_ob, p_ob)
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	conclusion3 <- sprintf(
	"For the Transfer Table, we have a Pearson's chi square of %.3f,
	with p-value of %.3f:", chisq_final, p_final)
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	}

	if (test == "fisher"){
	}

	if (neg_thr_pdif <= max(zoom$pdif) && neg_thr_pdif >= min(zoom$pdif)) {
	fig2 <- fig2 + ggplot2::geom_hline(yintercept = neg_thr_pdif,
	linetype = "dashed", color="red", size = 1)
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	}

	###plot figure 3 RIS% as sample size gets larger, using t statistic as the criterion