Adjusted script file and script file descriptions to match Vanessa branch

R/results/plant_results/tables/disruptive_selection.csv

@@ -1,5 +1,5 @@
 "Trait","Beta","Gamma","P_Linear","P_Quadratic"
 "SLA",0.0476259163621036,-0.100056733921985,0.476890961132518,0.281356584173955
-"deltaC13",-0.011073134823742,-0.046034146815972,0.854663354111405,0.623298218388449
+"deltaC13",-0.011073134823742,-0.046034146815972,0.854663354111406,0.623298218388449
 "FDsnow",-0.13130019385937,-0.0528847820612453,0.0256334813871458,0.558628105292545
 "height",-0.0674159642230305,-0.0859613170953237,0.260467489253676,0.365031981418163

R/results/plant_selection_results/summary_report.txt

@@ -0,0 +1,4 @@
+Analysis Date: 2026-03-21 15:57:58.057275
+Families analyzed: 66
+Traits analyzed: SLA, deltaC13, FDsnow, height
+Significant gradients: 5

R/results/plant_selection_results/tables/multivariate_selection_results.csv

@@ -0,0 +1,15 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"SLA","Linear",-0.160116571717217,0.103086931497492,0.126436518272693,0.0106269154455685
+"deltaC13","Linear",0.0763674095327287,0.0900023314895855,0.400046048707213,0.00810041967356123
+"FDsnow","Linear",-0.280524291405615,0.102272093597715,0.00833401929004965,0.0104595811288598
+"height","Linear",0.0434139539497148,0.0988748702319108,0.662422908307976,0.0097762399633772
+"SLA²","Quadratic",-0.0118567889850557,0.192626616385609,0.951210617410946,0.0371050133401686
+"deltaC13²","Quadratic",-0.326567112789641,0.147886819498291,0.032741280089733,0.0218705113813201
+"FDsnow²","Quadratic",0.288612875715204,0.250518401054682,0.255811009265873,0.0627594692669945
+"height²","Quadratic",-0.386993409386159,0.186210440051138,0.0438334897421209,0.0346743279840384
+"SLA × deltaC13","Correlational",-0.0555686798899385,0.124124028732819,0.656679973212141,0.0154067745088657
+"SLA × FDsnow","Correlational",-0.0217645341405151,0.109941142464156,0.844027264897245,0.012087054806324
+"SLA × height","Correlational",0.362834513206186,0.111580467252191,0.00226453841209758,0.0124502006722173
+"deltaC13 × FDsnow","Correlational",0.269017618255547,0.1035390023982,0.0128691565026352,0.0107203250176144
+"deltaC13 × height","Correlational",0.0477356167768272,0.111939051625481,0.671962976755796,0.0125303512788121
+"FDsnow × height","Correlational",-0.119648489345031,0.159734014382089,0.458002920741552,0.0255149553506175

R/results/plant_selection_results/tables/temporal_selection_results.csv

@@ -0,0 +1,4 @@
+"Year","Sample_Size","Linear","Quadratic"
+2012,57,-0.192876172987036,-0.119381555100731
+2013,57,-0.188603788569611,0.113404451765028
+2014,57,-0.0578596565616787,-0.0359199740868267

R/results/test_all_functions_results/tables/bootstrap_ci.csv

@@ -0,0 +1,6 @@
+"Term","Type","est","lwr","upr"
+"size","Linear",0.0910165365808788,-0.470607067745545,0.494785313002583
+"speed","Linear",-0.144639784052061,-0.425999225091872,0.181200243447631
+"size²","Quadratic",0.402591069131511,-0.164480962378244,1.21279766762013
+"speed²","Quadratic",0.0218970734483146,-0.323895558167426,0.604550913959529
+"size × speed","Correlational",-0.121725317575994,-0.400589904485058,0.148398351925861

R/results/test_all_functions_results/tables/selection_binary.csv

@@ -0,0 +1,6 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"size","Linear",0.105036692185697,0.229833011910793,0.64766164551854,0.0528232133639866
+"speed","Linear",0.31389539156892,0.232082567392143,0.176210295823653,0.0538623180873288
+"size²","Quadratic",0.432724154554936,0.3338038142697,0.194857426435059,0.111424986421
+"speed²","Quadratic",0.171981112711459,0.425321637062577,0.685951371316174,0.18089849495359
+"size × speed","Correlational",-0.379609055590874,0.2968068107668,0.200905189547171,0.0880942829175589

R/results/test_all_functions_results/tables/selection_continuous.csv

@@ -0,0 +1,6 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"size","Linear",0.0195027146393764,0.0477041189642697,0.683801736949462,0.0022756829661572
+"speed","Linear",-0.0320661103772255,0.0477041189642697,0.503476401863023,0.00227568296615719
+"size²","Quadratic",0.0837596260850392,0.0796747981055651,0.296556117916126,0.00634807345316255
+"speed²","Quadratic",0.0122552368949205,0.067247096766026,0.855891235202132,0.00452217202345927
+"size × speed","Correlational",-0.0374428040336,0.0525616841787993,0.478482396704399,0.00276273064371184

R/results/test_bumpus_sparrows_results/tables/bootstrap_ci.csv

@@ -0,0 +1,6 @@
+"Term","Type","est","lwr","upr"
+"TL","Linear",-1.19315987959457,-1.93852358439504,-0.675307627511643
+"HL","Linear",1.02193616187728,0.528493560097037,1.74607481201091
+"TL²","Quadratic",-0.803523660313887,-1.90835035323028,0.458089726463099
+"HL²","Quadratic",-0.133911589615358,-1.16514233126158,0.708670236898162
+"TL × HL","Correlational",0.0844408266810334,-0.933892631443336,0.816958727934413

R/results/test_bumpus_sparrows_results/tables/selection_TL.csv

@@ -0,0 +1,2 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"TL","Linear",-0.148600056688157,0.0522929515421683,0.00448759127880404,0.00273455278099157

R/results/test_bumpus_sparrows_results/tables/selection_TL_HL.csv

@@ -0,0 +1,6 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"TL","Linear",-0.320470713731804,0.0747452035886859,1.80688149520203e-05,0.0055868454595141
+"HL","Linear",43.9871206775832,11.2065382336764,8.66828718262568e-05,125.58649918285
+"TL²","Quadratic",-0.060064537823469,0.0438384877177823,0.170645536799746,0.00192181300538215
+"HL²","Quadratic",-506.68734577888,958.715009177128,0.597147669067612,919134.4688215
+"TL × HL","Correlational",1.7510363861204,4.94831457272991,0.723439898989118,24.4858171106912

R/results/test_bumpus_sparrows_results/tables/selection_TL_HL_WT.csv

@@ -0,0 +1,10 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"TL","Linear",-0.2505293381432,0.0793051291248291,0.00158281113461755,0.00628930350550582
+"HL","Linear",57.9134440779548,13.0016414853353,8.41603336064861e-06,169.042681313192
+"WT","Linear",-0.513486985805263,0.185757594440802,0.00570476374837239,0.0345058838924334
+"TL²","Quadratic",-0.0423787301461475,0.0496745231276736,0.393588285551024,0.00246755824796178
+"HL²","Quadratic",-446.213813331271,1228.97142425428,0.716545812020949,1510370.7616336
+"WT²","Quadratic",-0.0979420091408777,0.275518724429552,0.722228359034389,0.0759105675112874
+"TL × HL","Correlational",1.70003321418877,5.49482135915088,0.757026200490369,30.1930617689807
+"TL × WT","Correlational",-0.0575344267982376,0.0950412225538544,0.544937919356804,0.00903283398453128
+"HL × WT","Correlational",8.92035775520364,15.6101687857296,0.567697821970471,243.677369518966

R/results/test_bumpus_sparrows_results/tables/selection_TL_HL_WT_KL.csv

@@ -0,0 +1,15 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"TL","Linear",-0.320670993277407,0.0867071450740701,0.000217028959691374,0.00751812900689584
+"HL","Linear",50.6908326622677,13.3806064644342,0.000151632311771967,179.040629356059
+"WT","Linear",-0.693530446559714,0.20806922272575,0.00085861934246498,0.0432928014456976
+"KL","Linear",21.9212334713976,7.43179129088394,0.00318127133161747,55.2315217912584
+"TL²","Quadratic",-0.00797783367440701,0.0580764732264461,0.89074013175626,0.00337287674242211
+"HL²","Quadratic",-624.863576259735,1250.02646782503,0.617159381022702,1562566.17026312
+"WT²","Quadratic",-0.244594360801222,0.317609280557919,0.441234125899812,0.100875655096519
+"KL²","Quadratic",370.460776281646,469.816611916324,0.430391383259098,220727.648832534
+"TL × HL","Correlational",4.45461045018703,5.94429764120929,0.453620834279414,35.3346744472864
+"TL × WT","Correlational",-0.011566779173003,0.106037263868682,0.913137284387746,0.0112439013287565
+"TL × KL","Correlational",-7.21346835386392,4.23506161420698,0.0885168914226284,17.9357468761295
+"HL × WT","Correlational",10.1534888398331,16.7627568835616,0.544702612937581,280.990018337392
+"HL × KL","Correlational",-79.26982848486,472.990065542256,0.866903471130716,223719.602101667
+"WT × KL","Correlational",6.0068916037349,8.96777928786145,0.502965882378995,80.4210653557968

R/results/test_bumpus_sparrows_results/tables/selection_differentials.csv

@@ -0,0 +1,5 @@
+"trait","differential"
+"TL",158.708333333333
+"HL",0.735944444444444
+"WT",25.2263888888889
+"KL",0.845027777777778

R/results/test_extended_tests_results/tables/bootstrap_ci.csv

@@ -0,0 +1,6 @@
+"Term","Type","est","lwr","upr"
+"size","Linear",0.0760558821848199,-0.28684282516807,0.422172485252698
+"speed","Linear",-0.024681347148765,-0.779342389158814,0.376818126567057
+"size²","Quadratic",-0.182389837319528,-1.28268185489664,0.678660140549856
+"speed²","Quadratic",-0.518814502983225,-1.35468930322043,0.653153946517994
+"size × speed","Correlational",0.00340236207759671,-0.489466130934407,0.685313790601494

R/results/test_extended_tests_results/tables/selection_binary.csv

@@ -0,0 +1,6 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"size","Linear",0.102235520119481,0.327332684561984,0.754790432960762,0.107146686382556
+"speed","Linear",1.29260811355349,0.429605966576952,0.00262262259617551,0.184561286518517
+"size²","Quadratic",0.452890996569856,0.543639470596815,0.404803941834131,0.295543873990785
+"speed²","Quadratic",0.13983935898269,0.900723508521005,0.876622451362294,0.811302838802389
+"size × speed","Correlational",0.467107818931824,0.535712665585419,0.383242620642065,0.286988060068635

R/results/test_extended_tests_results/tables/selection_continuous.csv

@@ -0,0 +1,6 @@
+"Term","Type","Beta_Coefficient","Standard_Error","P_Value","Variance"
+"size","Linear",0.0128739186150879,0.0585959979082062,0.827051239013278,0.00343349097085851
+"speed","Linear",-0.00986602522278092,0.0585959979082062,0.86701178293771,0.00343349097085851
+"size²","Quadratic",-0.0610022454480119,0.107781197437072,0.574281244807887,0.0116167865209691
+"speed²","Quadratic",-0.12966954826548,0.0914870314363421,0.163422019425159,0.00836987692103425
+"size × speed","Correlational",0.00694577455413535,0.0717055298753092,0.923273281776424,0.00514168301469886

R/scripts/0.0_initialize.R

@@ -1,6 +1,8 @@
 # ======================================================
-# 0.0_initialize.R
-# Project initialization script
+# initialize.R
+#
+# Purpose:
+#   1. Installs and loads all required packages
 # ======================================================
 
 cat("\n========================================\n")

R/scripts/2_linear_selection_analysis.R

@@ -1,7 +1,8 @@
 # ============================================================================
 # analyze_linear_selection
 #
-# Purpose: Estimate linear selection gradients (β) using Lande & Arnold (1983)
+# Purpose: 
+#   1. Estimate linear selection gradients (β) using Lande & Arnold (1983)
 #
 # IMPORTANT NOTE:
 #   - ALWAYS use OLS (ordinary least squares) to estimate selection gradients
@@ -27,6 +28,12 @@
 #     4. Type III ANOVA on GLM for overall term significance
 #     5. Check VIF using OLS (GLM VIF not directly comparable)
 #
+# Parameters:
+#   data           : data frame containing fitness and trait measurements
+#   fitness_col    : name of the fitness column (character)
+#   trait_cols     : vector of trait column names
+#   fitness_type   : "binary" or "continuous"
+#
 # Returns:
 #   For continuous: list with model (lm), summary, anova, vif, fitness_type
 #   For binary: list with model (list of ols and glm), summary (list of ols and glm),
@@ -44,7 +51,10 @@
 #'
 #' @return A list containing the fitted models, summaries, ANOVA tables, and VIFs.
 #' @export
-analyze_linear_selection <- function(data, fitness_col, trait_cols, fitness_type) {
+analyze_linear_selection <- function(data,
+                                     fitness_col, 
+                                     trait_cols, 
+                                     fitness_type) {
   # Check sample size
   if (nrow(data) < 10) {
     warning("Small sample size (n < 10) — results may be unreliable")

R/scripts/3_nonlinear_selection_analysis.R

@@ -1,7 +1,16 @@
 # ============================================================================
 # analyze_nonlinear_selection
 #
-# Purpose: Estimate quadratic (γ) and correlational (γ_ij) selection gradients
+# Purpose: 
+#   1. Estimate quadratic (γ) and correlational (γ_ij) selection gradients
+#
+# Model:
+#   w = α + β₁z₁ + β₂z₂ + ½γ₁₁z₁² + ½γ₂₂z₂² + γ₁₂z₁z₂ + ε
+#
+#   Where:
+#     β = linear selection gradients
+#     γ_ii = quadratic selection gradients (stabilizing/disruptive)
+#     γ_ij = correlational selection gradients (interactions)
 #
 # IMPORTANT NOTE:
 #   - ALWAYS use OLS to estimate selection gradients (β, γ, γ_ij)
@@ -11,21 +20,19 @@
 #   - For continuous fitness: OLS provides both gradient estimates AND valid
 #     p-values (via t-tests and F-tests).
 #
-# Model:
-#   w = α + β₁z₁ + β₂z₂ + ½γ₁₁z₁² + ½γ₂₂z₂² + γ₁₂z₁z₂ + ε
-#
-# Where:
-#   - β = linear selection gradients
-#   - γ_ii = quadratic selection gradients (stabilizing/disruptive)
-#   - γ_ij = correlational selection gradients (interactions)
-#
 # Workflow:
 #   1. Build formula with linear, quadratic, and interaction terms
 #   2. Fit OLS to get all gradient estimates (β, γ, γ_ij)
 #   3. For binary fitness: fit logistic GLM for valid p-values
 #   4. Type III ANOVA for significance tests
 #   5. VIF check for multicollinearity
 #
+# Parameters:
+#   data           : data frame containing fitness and trait measurements
+#   fitness_col    : name of the fitness column (character)
+#   trait_cols     : vector of trait column names
+#   fitness_type   : "binary" or "continuous"
+#
 # Returns:
 #   For continuous: list with model_ols, summary_ols, anova, vif, fitness_type
 #   For binary: list with model_ols, model_glm, summary_ols, summary_glm,
@@ -43,7 +50,10 @@
 #'
 #' @return A list containing the fitted nonlinear models, summaries, ANOVA tables, and VIFs.
 #' @export
-analyze_nonlinear_selection <- function(data, fitness_col, trait_cols, fitness_type) {
+analyze_nonlinear_selection <- function(data, 
+                                        fitness_col, 
+                                        trait_cols, 
+                                        fitness_type) {
   if (length(trait_cols) < 2) {
     stop("Nonlinear selection requires at least 2 traits")
   }