Analysis of Bone Fracture Data
Libraries
## PART 2
library(xgboost)
library(shapper)## Warning: package 'shapper' was built under R version 4.3.3library(ROCR)## Warning: package 'ROCR' was built under R version 4.3.3library(ROSE)## Warning: package 'ROSE' was built under R version 4.3.3library(DMwR2)## Warning: package 'DMwR2' was built under R version 4.3.3library(smotefamily)## Warning: package 'smotefamily' was built under R version 4.3.3library(randomForest)
library(readxl)
library(dplyr)
library(car)
library(caret)## Warning: package 'ggplot2' was built under R version 4.3.3## Warning: package 'lattice' was built under R version 4.3.3library(car)
library(pROC)
library(dplyr)
library(glmnet)## Warning: package 'glmnet' was built under R version 4.3.3library(FactoMineR)## Warning: package 'FactoMineR' was built under R version 4.3.3library(rpart)
library(rpart)
library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 4.3.3library(aplore3)## Warning: package 'aplore3' was built under R version 4.3.3# Load and summarize the dataset
data("glow_bonemed") # Corrected dataset name
summary(glow_bonemed)## sub_id site_id phy_id priorfrac age
## Min. : 1.0 Min. :1.000 Min. : 1.00 No :374 Min. :55.00
## 1st Qu.:125.8 1st Qu.:2.000 1st Qu.: 57.75 Yes:126 1st Qu.:61.00
## Median :250.5 Median :3.000 Median :182.50 Median :67.00
## Mean :250.5 Mean :3.436 Mean :178.55 Mean :68.56
## 3rd Qu.:375.2 3rd Qu.:5.000 3rd Qu.:298.00 3rd Qu.:76.00
## Max. :500.0 Max. :6.000 Max. :325.00 Max. :90.00
## weight height bmi premeno momfrac armassist
## Min. : 39.90 Min. :134.0 Min. :14.88 No :403 No :435 No :312
## 1st Qu.: 59.90 1st Qu.:157.0 1st Qu.:23.27 Yes: 97 Yes: 65 Yes:188
## Median : 68.00 Median :161.5 Median :26.42
## Mean : 71.82 Mean :161.4 Mean :27.55
## 3rd Qu.: 81.30 3rd Qu.:165.0 3rd Qu.:30.79
## Max. :127.00 Max. :199.0 Max. :49.08
## smoke raterisk fracscore fracture bonemed bonemed_fu
## No :465 Less :167 Min. : 0.000 No :375 No :371 No :361
## Yes: 35 Same :186 1st Qu.: 2.000 Yes:125 Yes:129 Yes:139
## Greater:147 Median : 3.000
## Mean : 3.698
## 3rd Qu.: 5.000
## Max. :11.000
## bonetreat
## No :382
## Yes:118
##
##
##
## # Rename Columns and convert factors where needed
glow_bonemed_NEW <- glow_bonemed %>%
rename(
FRACTURE = fracture,
AGE = age,
HEIGHT = height,
WEIGHT = weight,
PREMENO = premeno,
MOMFRAC = momfrac,
RATERISK = raterisk,
PRIORFRAC = priorfrac,
ARMASSIST = armassist,
SMOKE = smoke,
BMI = bmi,
SUB_ID = sub_id,
SITE_ID = site_id,
PHY_ID = phy_id,
BONEMED = bonemed,
FRACSCORE =fracscore,
BONEMED_FU = bonemed_fu,
BONETREAT = bonetreat
) %>%
mutate(
PRIORFRAC = as.numeric(PRIORFRAC == "Yes"),
ARMASSIST = as.numeric(ARMASSIST == "Yes"),
MOMFRAC = as.numeric(MOMFRAC == "Yes"),
SMOKE = as.numeric(SMOKE == "Yes"),
FRACTURE = as.numeric(FRACTURE == "Yes"),
RATERISK_EQ_3 = as.numeric(RATERISK == "Greater"),
RATERISK_num = as.numeric(factor(RATERISK))
)
# INTERACTION AND STANDARDIZATION TERMS
# age
glow_bonemed_NEW <- glow_bonemed_NEW %>%
mutate(AGE_STDZ = scale(AGE, center = TRUE, scale = TRUE))
# Standardize AGE and create interaction terms
glow_bonemed_NEW <- glow_bonemed_NEW %>%
mutate(
AGE_STDZ = scale(AGE, center = TRUE, scale = TRUE), # Standardize AGE
AGExPRIORFRAC = AGE_STDZ * PRIORFRAC, # Interaction term: Standardized AGE * PRIORFRAC
MOMFRACxARMASSIST = MOMFRAC * ARMASSIST, # Interaction term: MOMFRAC * ARMASSIST
PRIORFRACxAGE_STDZ = PRIORFRAC * AGE_STDZ,
NOPRIORFRACxAGE_STDZ = (1 - PRIORFRAC) * AGE_STDZ
#AGE_STDZxNOPRIOR =(1 - PRIORFRAC) * AGE_STDZ #(same as above but used in code)
)
# Create Interaction Terms
glow_bonemed_NEW <- glow_bonemed_NEW %>%
mutate(
PRIORFRACxAGE_STDZ = PRIORFRAC * AGE_STDZ,
NOPRIORFRACxAGE_STDZ = (1 - PRIORFRAC) * AGE_STDZ
)
# Save the new dataframe to a CSV file
#write.csv(glow_bonemed_NEW, "glow_bonemed_NEW.csv", row.names = FALSE)
# Drop Useless Columns
glow_bonemedNEW <- glow_bonemed_NEW[, !(names(glow_bonemed_NEW) %in% c("SUB_ID", "SITE_ID", "PHY_ID"))]
# Rename Dataset to work with
GLOW_data <- glow_bonemed_NEW
glow <- GLOW_data
glows <- glow
colnames(GLOW_data)## [1] "SUB_ID" "SITE_ID" "PHY_ID"
## [4] "PRIORFRAC" "AGE" "WEIGHT"
## [7] "HEIGHT" "BMI" "PREMENO"
## [10] "MOMFRAC" "ARMASSIST" "SMOKE"
## [13] "RATERISK" "FRACSCORE" "FRACTURE"
## [16] "BONEMED" "BONEMED_FU" "BONETREAT"
## [19] "RATERISK_EQ_3" "RATERISK_num" "AGE_STDZ"
## [22] "AGExPRIORFRAC" "MOMFRACxARMASSIST" "PRIORFRACxAGE_STDZ"
## [25] "NOPRIORFRACxAGE_STDZ"colnames(glow)## [1] "SUB_ID" "SITE_ID" "PHY_ID"
## [4] "PRIORFRAC" "AGE" "WEIGHT"
## [7] "HEIGHT" "BMI" "PREMENO"
## [10] "MOMFRAC" "ARMASSIST" "SMOKE"
## [13] "RATERISK" "FRACSCORE" "FRACTURE"
## [16] "BONEMED" "BONEMED_FU" "BONETREAT"
## [19] "RATERISK_EQ_3" "RATERISK_num" "AGE_STDZ"
## [22] "AGExPRIORFRAC" "MOMFRACxARMASSIST" "PRIORFRACxAGE_STDZ"
## [25] "NOPRIORFRACxAGE_STDZ"colnames(glows)## [1] "SUB_ID" "SITE_ID" "PHY_ID"
## [4] "PRIORFRAC" "AGE" "WEIGHT"
## [7] "HEIGHT" "BMI" "PREMENO"
## [10] "MOMFRAC" "ARMASSIST" "SMOKE"
## [13] "RATERISK" "FRACSCORE" "FRACTURE"
## [16] "BONEMED" "BONEMED_FU" "BONETREAT"
## [19] "RATERISK_EQ_3" "RATERISK_num" "AGE_STDZ"
## [22] "AGExPRIORFRAC" "MOMFRACxARMASSIST" "PRIORFRACxAGE_STDZ"
## [25] "NOPRIORFRACxAGE_STDZ"Model Building
GLM
Prepare the Logistic Regression Model
model1 <- glm(FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC + ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ + NOPRIORFRACxAGE_STDZ, data = GLOW_data, family = binomial())
# Check Model Sumary & Diagnostics
summary(model1)##
## Call:
## glm(formula = FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC +
## ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ + NOPRIORFRACxAGE_STDZ,
## family = binomial(), data = GLOW_data)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.18600 2.90210 1.787 0.073941 .
## AGE_STDZ 0.49416 0.14671 3.368 0.000756 ***
## HEIGHT -0.04329 0.01813 -2.388 0.016951 *
## PRIORFRAC 0.85315 0.25473 3.349 0.000811 ***
## MOMFRAC 0.71225 0.30707 2.319 0.020368 *
## ARMASSIST 0.44757 0.23238 1.926 0.054106 .
## RATERISK_EQ_3 0.46265 0.23961 1.931 0.053495 .
## PRIORFRACxAGE_STDZ -0.51953 0.23153 -2.244 0.024839 *
## NOPRIORFRACxAGE_STDZ NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 562.34 on 499 degrees of freedom
## Residual deviance: 504.78 on 492 degrees of freedom
## AIC: 520.78
##
## Number of Fisher Scoring iterations: 4#car::vif(model)
# Refit the model without the problematic interaction term
model_refit <- glm(FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC + ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ, data = GLOW_data, family = binomial())
# Check the new model summary
summary(model_refit)##
## Call:
## glm(formula = FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC +
## ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ, family = binomial(),
## data = GLOW_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.18600 2.90210 1.787 0.073941 .
## AGE_STDZ 0.49416 0.14671 3.368 0.000756 ***
## HEIGHT -0.04329 0.01813 -2.388 0.016951 *
## PRIORFRAC 0.85315 0.25473 3.349 0.000811 ***
## MOMFRAC 0.71225 0.30707 2.319 0.020368 *
## ARMASSIST 0.44757 0.23238 1.926 0.054106 .
## RATERISK_EQ_3 0.46265 0.23961 1.931 0.053495 .
## PRIORFRACxAGE_STDZ -0.51953 0.23153 -2.244 0.024839 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 562.34 on 499 degrees of freedom
## Residual deviance: 504.78 on 492 degrees of freedom
## AIC: 520.78
##
## Number of Fisher Scoring iterations: 4# Attempt VIF calculation again
vif(model_refit)## AGE_STDZ HEIGHT PRIORFRAC MOMFRAC
## 1.804248 1.069318 1.218999 1.029081
## ARMASSIST RATERISK_EQ_3 PRIORFRACxAGE_STDZ
## 1.106067 1.069982 1.881434# Original Model
# Fit the original logistic regression model
original_model <- glm(FRACTURE ~ AGE + HEIGHT + PRIORFRAC + MOMFRAC + ARMASSIST + RATERISK_EQ_3 + AGExPRIORFRAC,
family = binomial(link = "logit"),
data = GLOW_data)
summary(original_model)##
## Call:
## glm(formula = FRACTURE ~ AGE + HEIGHT + PRIORFRAC + MOMFRAC +
## ARMASSIST + RATERISK_EQ_3 + AGExPRIORFRAC, family = binomial(link = "logit"),
## data = GLOW_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.41714 3.29734 0.430 0.667353
## AGE 0.05497 0.01632 3.368 0.000756 ***
## HEIGHT -0.04329 0.01813 -2.388 0.016951 *
## PRIORFRAC 0.85315 0.25473 3.349 0.000811 ***
## MOMFRAC 0.71225 0.30707 2.319 0.020368 *
## ARMASSIST 0.44757 0.23238 1.926 0.054106 .
## RATERISK_EQ_3 0.46265 0.23961 1.931 0.053495 .
## AGExPRIORFRAC -0.51953 0.23153 -2.244 0.024839 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 562.34 on 499 degrees of freedom
## Residual deviance: 504.78 on 492 degrees of freedom
## AIC: 520.78
##
## Number of Fisher Scoring iterations: 4car::vif(original_model)## AGE HEIGHT PRIORFRAC MOMFRAC ARMASSIST
## 1.804248 1.069318 1.218999 1.029081 1.106067
## RATERISK_EQ_3 AGExPRIORFRAC
## 1.069982 1.881434Logistic Regression Model
model2 <- glm(FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC + ARMASSIST, data = GLOW_data, family = binomial())
summary(model2)##
## Call:
## glm(formula = FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC +
## ARMASSIST, family = binomial(), data = GLOW_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.78083 2.90433 1.990 0.04654 *
## AGE_STDZ 0.26748 0.11464 2.333 0.01964 *
## HEIGHT -0.04635 0.01816 -2.552 0.01070 *
## PRIORFRAC 0.75259 0.23959 3.141 0.00168 **
## MOMFRAC 0.72263 0.30235 2.390 0.01684 *
## ARMASSIST 0.52372 0.22829 2.294 0.02179 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 562.34 on 499 degrees of freedom
## Residual deviance: 513.46 on 494 degrees of freedom
## AIC: 525.46
##
## Number of Fisher Scoring iterations: 4Check Model Summary and Diagnostics
car::vif(model2)## AGE_STDZ HEIGHT PRIORFRAC MOMFRAC ARMASSIST
## 1.140680 1.066260 1.080805 1.012421 1.085556Validation Split Data and Validate Model
set.seed(123)
trainIndex <- createDataPartition(GLOW_data$FRACTURE, p = 0.8, list = FALSE, times = 1)
trainData <- GLOW_data[trainIndex, ]
validationData <- GLOW_data[-trainIndex, ]
fitModel <- glm(FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC, data = trainData, family = binomial())
validationData$predicted_probs <- predict(fitModel, newdata = validationData, type = "response")
validationData$predicted_class <- ifelse(validationData$predicted_probs > 0.5, 1, 0)
conf_matrix <- caret::confusionMatrix(as.factor(validationData$predicted_class), as.factor(validationData$FRACTURE))
print(conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 65 22
## 1 10 3
##
## Accuracy : 0.68
## 95% CI : (0.5792, 0.7698)
## No Information Rate : 0.75
## P-Value [Acc > NIR] : 0.95540
##
## Kappa : -0.0159
##
## Mcnemar's Test P-Value : 0.05183
##
## Sensitivity : 0.8667
## Specificity : 0.1200
## Pos Pred Value : 0.7471
## Neg Pred Value : 0.2308
## Prevalence : 0.7500
## Detection Rate : 0.6500
## Detection Prevalence : 0.8700
## Balanced Accuracy : 0.4933
##
## 'Positive' Class : 0
## ROC Curve & AUC
roc_result <- roc(response = validationData$FRACTURE, predictor = validationData$predicted_probs)## Setting levels: control = 0, case = 1## Setting direction: controls < casesplot(roc_result, main="ROC Curve")
auc(roc_result)## Area under the curve: 0.5464# Improved Model:
# Standardize AGE and create new interaction terms
GLOW_data <- GLOW_data %>%
mutate(
AGE_STDZ = scale(AGE, center = TRUE, scale = TRUE), # Standardize AGE
PRIORFRACxAGE_STDZ = PRIORFRAC * AGE_STDZ, # Interaction term: PRIORFRAC * Standardized AGE
NOPRIORFRACxAGE_STDZ = (1 - PRIORFRAC) * AGE_STDZ # Interaction term: (1 - PRIORFRAC) * Standardized AGE
)
# Fit the improved logistic regression model
improved_model <- glm(FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC + ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ + NOPRIORFRACxAGE_STDZ,
family = binomial(link = "logit"),
data = GLOW_data)
# car::vif(improved_model) # Too Much Multicolinearity
summary(improved_model)##
## Call:
## glm(formula = FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC +
## ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ + NOPRIORFRACxAGE_STDZ,
## family = binomial(link = "logit"), data = GLOW_data)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.18600 2.90210 1.787 0.073941 .
## AGE_STDZ 0.49416 0.14671 3.368 0.000756 ***
## HEIGHT -0.04329 0.01813 -2.388 0.016951 *
## PRIORFRAC 0.85315 0.25473 3.349 0.000811 ***
## MOMFRAC 0.71225 0.30707 2.319 0.020368 *
## ARMASSIST 0.44757 0.23238 1.926 0.054106 .
## RATERISK_EQ_3 0.46265 0.23961 1.931 0.053495 .
## PRIORFRACxAGE_STDZ -0.51953 0.23153 -2.244 0.024839 *
## NOPRIORFRACxAGE_STDZ NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 562.34 on 499 degrees of freedom
## Residual deviance: 504.78 on 492 degrees of freedom
## AIC: 520.78
##
## Number of Fisher Scoring iterations: 4# Fit the improved logistic regression model without the problematic term
improved_model2 <- glm(FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC + ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ,
family = binomial(link = "logit"),
data = GLOW_data)
summary(improved_model2)##
## Call:
## glm(formula = FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC +
## ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ, family = binomial(link = "logit"),
## data = GLOW_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.18600 2.90210 1.787 0.073941 .
## AGE_STDZ 0.49416 0.14671 3.368 0.000756 ***
## HEIGHT -0.04329 0.01813 -2.388 0.016951 *
## PRIORFRAC 0.85315 0.25473 3.349 0.000811 ***
## MOMFRAC 0.71225 0.30707 2.319 0.020368 *
## ARMASSIST 0.44757 0.23238 1.926 0.054106 .
## RATERISK_EQ_3 0.46265 0.23961 1.931 0.053495 .
## PRIORFRACxAGE_STDZ -0.51953 0.23153 -2.244 0.024839 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 562.34 on 499 degrees of freedom
## Residual deviance: 504.78 on 492 degrees of freedom
## AIC: 520.78
##
## Number of Fisher Scoring iterations: 4# check the VIF for the improved model again
car::vif(improved_model2)## AGE_STDZ HEIGHT PRIORFRAC MOMFRAC
## 1.804248 1.069318 1.218999 1.029081
## ARMASSIST RATERISK_EQ_3 PRIORFRACxAGE_STDZ
## 1.106067 1.069982 1.881434# Test Model
# Split into training and validation
set.seed(123) # for reproducibility
trainIndex <- createDataPartition(GLOW_data$FRACTURE, p = 0.8,
list = FALSE,
times = 1)
trainData <- GLOW_data[trainIndex, ]
validationData <- GLOW_data[-trainIndex, ]
# Fit Model on Training Data
improved_model <- glm(FRACTURE ~ AGE_STDZ + HEIGHT + PRIORFRAC + MOMFRAC + ARMASSIST + RATERISK_EQ_3 + PRIORFRACxAGE_STDZ,
family = binomial(link = "logit"),
data = trainData)
# Make Predictions on Validation Data
# Predicting probabilities
validationData$predicted_probs <- predict(improved_model, newdata = validationData, type = "response")
# Convert probabilities to a binary outcome (0 or 1) based on a threshold of 0.5
validationData$predicted_class <- ifelse(validationData$predicted_probs > 0.5, 1, 0)
# Evaluate Model Performance
# Creating a confusion matrix to compare actual and predicted classifications
conf_matrix <- confusionMatrix(as.factor(validationData$predicted_class), as.factor(validationData$FRACTURE))
print(conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 67 21
## 1 8 4
##
## Accuracy : 0.71
## 95% CI : (0.6107, 0.7964)
## No Information Rate : 0.75
## P-Value [Acc > NIR] : 0.85046
##
## Kappa : 0.0645
##
## Mcnemar's Test P-Value : 0.02586
##
## Sensitivity : 0.8933
## Specificity : 0.1600
## Pos Pred Value : 0.7614
## Neg Pred Value : 0.3333
## Prevalence : 0.7500
## Detection Rate : 0.6700
## Detection Prevalence : 0.8800
## Balanced Accuracy : 0.5267
##
## 'Positive' Class : 0
## # ROC Curve & AUC
# ROC curve
roc_result <- roc(response = validationData$FRACTURE, predictor = validationData$predicted_probs)## Setting levels: control = 0, case = 1## Setting direction: controls < casesplot(roc_result, main="ROC Curve")
auc(roc_result)## Area under the curve: 0.6149# REFINING FURTHER
# Pairwise
pairwise_interactions <- GLOW_data %>%
mutate(
AGExWEIGHT = AGE * WEIGHT,
AGExHEIGHT = AGE * HEIGHT,
WEIGHTxHEIGHT = WEIGHT * HEIGHT
)
# Total Pairwise
selected_vars <- c("AGE", "WEIGHT", "HEIGHT", "PRIORFRAC", "AGExPRIORFRAC", "AGE_STDZ", "AGE_STDZxPRIOR", "AGE_STDZxNOPRIOR", "BMI", "PREMENO", "MOMFRAC", "ARMASSIST", "MOMFRACxARMASSIST", "SMOKE", "RATERISK", "RATERISK_EQ_1", "RATERISK_EQ_2", "RATERISK_EQ_3", "FRACSCORE", "PRIORFRACxAGE_STDZ", "NOPRIORFRACxAGE_STDZ") # List the variables to combine
# Ensure to use the correct variable names as they exist in your dataframe
combinations <- combn(selected_vars, 2, simplify = FALSE) # Get all combinations of these variables
# Iterate over the combinations and create interaction terms
for(comb in combinations) {
if(all(comb %in% names(GLOW_data))) {
var_name <- paste(comb, collapse = "TOTAL_PAIRWISE") # Create a name for the new variable
pairwise_interactions[[var_name]] <- GLOW_data[[comb[1]]] * GLOW_data[[comb[2]]]
} else {
warning("Variable combination does not exist in the dataset: ", paste(comb, collapse = " and "))
}
}## Warning: Variable combination does not exist in the dataset: AGE and
## AGE_STDZxPRIOR## Warning: Variable combination does not exist in the dataset: AGE and
## AGE_STDZxNOPRIOR## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: AGE and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: AGE and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: WEIGHT and
## AGE_STDZxPRIOR## Warning: Variable combination does not exist in the dataset: WEIGHT and
## AGE_STDZxNOPRIOR## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: WEIGHT and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: WEIGHT and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: HEIGHT and
## AGE_STDZxPRIOR## Warning: Variable combination does not exist in the dataset: HEIGHT and
## AGE_STDZxNOPRIOR## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: HEIGHT and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: HEIGHT and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: PRIORFRAC and
## AGE_STDZxPRIOR## Warning: Variable combination does not exist in the dataset: PRIORFRAC and
## AGE_STDZxNOPRIOR## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: PRIORFRAC and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: PRIORFRAC and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: AGExPRIORFRAC and
## AGE_STDZxPRIOR## Warning: Variable combination does not exist in the dataset: AGExPRIORFRAC and
## AGE_STDZxNOPRIOR## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: AGExPRIORFRAC and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: AGExPRIORFRAC and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: AGE_STDZ and
## AGE_STDZxPRIOR## Warning: Variable combination does not exist in the dataset: AGE_STDZ and
## AGE_STDZxNOPRIOR## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: AGE_STDZ and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: AGE_STDZ and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## AGE_STDZxNOPRIOR## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## BMI## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## PREMENO## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## MOMFRAC## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## ARMASSIST## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## MOMFRACxARMASSIST## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## SMOKE## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## RATERISK## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## RATERISK_EQ_3## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## FRACSCORE## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## PRIORFRACxAGE_STDZ## Warning: Variable combination does not exist in the dataset: AGE_STDZxPRIOR and
## NOPRIORFRACxAGE_STDZ## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and BMI## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and PREMENO## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and MOMFRAC## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and ARMASSIST## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and MOMFRACxARMASSIST## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and SMOKE## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and RATERISK## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and RATERISK_EQ_3## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and FRACSCORE## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and PRIORFRACxAGE_STDZ## Warning: Variable combination does not exist in the dataset: AGE_STDZxNOPRIOR
## and NOPRIORFRACxAGE_STDZ## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: BMI and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: BMI and
## RATERISK_EQ_2## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: PREMENO and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: PREMENO and
## RATERISK_EQ_2## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: MOMFRAC and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: MOMFRAC and
## RATERISK_EQ_2## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: ARMASSIST and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: ARMASSIST and
## RATERISK_EQ_2## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: MOMFRACxARMASSIST
## and RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: MOMFRACxARMASSIST
## and RATERISK_EQ_2## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: SMOKE and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: SMOKE and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: RATERISK and
## RATERISK_EQ_1## Warning: Variable combination does not exist in the dataset: RATERISK and
## RATERISK_EQ_2## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors
## Warning in Ops.factor(GLOW_data[[comb[1]]], GLOW_data[[comb[2]]]): '*' not
## meaningful for factors## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_1 and
## RATERISK_EQ_2## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_1 and
## RATERISK_EQ_3## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_1 and
## FRACSCORE## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_1 and
## PRIORFRACxAGE_STDZ## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_1 and
## NOPRIORFRACxAGE_STDZ## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_2 and
## RATERISK_EQ_3## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_2 and
## FRACSCORE## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_2 and
## PRIORFRACxAGE_STDZ## Warning: Variable combination does not exist in the dataset: RATERISK_EQ_2 and
## NOPRIORFRACxAGE_STDZ## MORE ADVANCED MODELING
# Refining Further
# Pairwise
pairwise_interactions <- GLOW_data %>%
mutate(
AGExWEIGHT = AGE * WEIGHT,
AGExHEIGHT = AGE * HEIGHT,
WEIGHTxHEIGHT = WEIGHT * HEIGHT
)
# Total Pairwise
selected_vars <- c("AGE", "WEIGHT", "HEIGHT")
combinations <- combn(selected_vars, 2, simplify = FALSE) # Get all combinations of these variables
# Check the structure of the new dataframe with interaction terms
str(pairwise_interactions)## 'data.frame': 500 obs. of 28 variables:
## $ SUB_ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ SITE_ID : int 1 4 6 6 1 5 5 1 1 4 ...
## $ PHY_ID : int 14 284 305 309 37 299 302 36 8 282 ...
## $ PRIORFRAC : num 0 0 1 0 0 1 0 1 1 0 ...
## $ AGE : int 62 65 88 82 61 67 84 82 86 58 ...
## $ WEIGHT : num 70.3 87.1 50.8 62.1 68 68 50.8 40.8 62.6 63.5 ...
## $ HEIGHT : int 158 160 157 160 152 161 150 153 156 166 ...
## $ BMI : num 28.2 34 20.6 24.3 29.4 ...
## $ PREMENO : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ MOMFRAC : num 0 0 1 0 0 0 0 0 0 0 ...
## $ ARMASSIST : num 0 0 1 0 0 0 0 0 0 0 ...
## $ SMOKE : num 0 0 0 0 0 1 0 0 0 0 ...
## $ RATERISK : Factor w/ 3 levels "Less","Same",..: 2 2 1 1 2 2 1 2 2 1 ...
## $ FRACSCORE : int 1 2 11 5 1 4 6 7 7 0 ...
## $ FRACTURE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BONEMED : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ BONEMED_FU : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ BONETREAT : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ RATERISK_EQ_3 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ RATERISK_num : num 2 2 1 1 2 2 1 2 2 1 ...
## $ AGE_STDZ : num [1:500, 1] -0.73 -0.396 2.162 1.495 -0.841 ...
## ..- attr(*, "scaled:center")= num 68.6
## ..- attr(*, "scaled:scale")= num 8.99
## $ AGExPRIORFRAC : num [1:500, 1] 0 0 2.16 0 0 ...
## ..- attr(*, "scaled:center")= num 68.6
## ..- attr(*, "scaled:scale")= num 8.99
## $ MOMFRACxARMASSIST : num 0 0 1 0 0 0 0 0 0 0 ...
## $ PRIORFRACxAGE_STDZ : num [1:500, 1] 0 0 2.16 0 0 ...
## ..- attr(*, "scaled:center")= num 68.6
## ..- attr(*, "scaled:scale")= num 8.99
## $ NOPRIORFRACxAGE_STDZ: num [1:500, 1] -0.73 -0.396 0 1.495 -0.841 ...
## ..- attr(*, "scaled:center")= num 68.6
## ..- attr(*, "scaled:scale")= num 8.99
## $ AGExWEIGHT : num 4359 5662 4470 5092 4148 ...
## $ AGExHEIGHT : int 9796 10400 13816 13120 9272 10787 12600 12546 13416 9628 ...
## $ WEIGHTxHEIGHT : num 11107 13936 7976 9936 10336 ...# View the first few rows to confirm the new columns were added
head(pairwise_interactions)## SUB_ID SITE_ID PHY_ID PRIORFRAC AGE WEIGHT HEIGHT BMI PREMENO MOMFRAC
## 1 1 1 14 0 62 70.3 158 28.16055 No 0
## 2 2 4 284 0 65 87.1 160 34.02344 No 0
## 3 3 6 305 1 88 50.8 157 20.60936 No 1
## 4 4 6 309 0 82 62.1 160 24.25781 No 0
## 5 5 1 37 0 61 68.0 152 29.43213 No 0
## 6 6 5 299 1 67 68.0 161 26.23356 No 0
## ARMASSIST SMOKE RATERISK FRACSCORE FRACTURE BONEMED BONEMED_FU BONETREAT
## 1 0 0 Same 1 0 No No No
## 2 0 0 Same 2 0 No No No
## 3 1 0 Less 11 0 No No No
## 4 0 0 Less 5 0 No No No
## 5 0 0 Same 1 0 No No No
## 6 0 1 Same 4 0 No No No
## RATERISK_EQ_3 RATERISK_num AGE_STDZ AGExPRIORFRAC MOMFRACxARMASSIST
## 1 0 2 -0.7299597 0.0000000 0
## 2 0 2 -0.3962384 0.0000000 0
## 3 0 1 2.1622915 2.1622915 1
## 4 0 1 1.4948489 0.0000000 0
## 5 0 2 -0.8412001 0.0000000 0
## 6 0 2 -0.1737576 -0.1737576 0
## PRIORFRACxAGE_STDZ NOPRIORFRACxAGE_STDZ AGExWEIGHT AGExHEIGHT WEIGHTxHEIGHT
## 1 0.0000000 -0.7299597 4358.6 9796 11107.4
## 2 0.0000000 -0.3962384 5661.5 10400 13936.0
## 3 2.1622915 0.0000000 4470.4 13816 7975.6
## 4 0.0000000 1.4948489 5092.2 13120 9936.0
## 5 0.0000000 -0.8412001 4148.0 9272 10336.0
## 6 -0.1737576 0.0000000 4556.0 10787 10948.0# Find Target Column "FRACTURE"
# Print all column names in the dataset
print(names(GLOW_data))## [1] "SUB_ID" "SITE_ID" "PHY_ID"
## [4] "PRIORFRAC" "AGE" "WEIGHT"
## [7] "HEIGHT" "BMI" "PREMENO"
## [10] "MOMFRAC" "ARMASSIST" "SMOKE"
## [13] "RATERISK" "FRACSCORE" "FRACTURE"
## [16] "BONEMED" "BONEMED_FU" "BONETREAT"
## [19] "RATERISK_EQ_3" "RATERISK_num" "AGE_STDZ"
## [22] "AGExPRIORFRAC" "MOMFRACxARMASSIST" "PRIORFRACxAGE_STDZ"
## [25] "NOPRIORFRACxAGE_STDZ"# Or use the which function to find the index of the 'FRACTURE' column
fracture_column_index <- which(names(GLOW_data) == "FRACTURE")
print(paste("The 'FRACTURE' column is at index:", fracture_column_index))## [1] "The 'FRACTURE' column is at index: 15"# *********************************************
# ADD FRACTURE COLLMN BACK IN
# GLOW_data <- GLOW_data %>%
# mutate(
# FRACTURE = as.numeric(FRACTURE == "Yes")
# )
# **********************************************# Ensure y is just the FRACTURE column as a factor if it's categorical
y <- as.factor(GLOW_data$FRACTURE)
# Ensure x excludes the FRACTURE column
x <- GLOW_data[, -which(names(GLOW_data) == "FRACTURE")]
# Setup RFE control
control <- rfeControl(functions=rfFuncs, method="repeatedcv", number=10, repeats=3)
# Run RFE
results <- rfe(x, y, sizes=c(1:5), rfeControl=control)
# Print results
print(results)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 1 0.9987 0.9964 0.005074 0.013524
## 2 0.9993 0.9982 0.003651 0.009732 *
## 3 0.9987 0.9964 0.005074 0.013524
## 4 0.9987 0.9964 0.005074 0.013524
## 5 0.9987 0.9964 0.005074 0.013524
## 24 0.9987 0.9964 0.005074 0.013524
##
## The top 2 variables (out of 2):
## SUB_ID, FRACSCORE# Print the results
print(results)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 1 0.9987 0.9964 0.005074 0.013524
## 2 0.9993 0.9982 0.003651 0.009732 *
## 3 0.9987 0.9964 0.005074 0.013524
## 4 0.9987 0.9964 0.005074 0.013524
## 5 0.9987 0.9964 0.005074 0.013524
## 24 0.9987 0.9964 0.005074 0.013524
##
## The top 2 variables (out of 2):
## SUB_ID, FRACSCORE# Summary RFE
summary(results)## Length Class Mode
## pred 0 -none- NULL
## variables 6 data.frame list
## results 5 data.frame list
## bestSubset 1 -none- numeric
## fit 18 randomForest list
## optVariables 2 -none- character
## optsize 1 -none- numeric
## call 5 -none- call
## control 14 -none- list
## resample 8 data.frame list
## metric 1 -none- character
## maximize 1 -none- logical
## perfNames 2 -none- character
## times 3 -none- list
## resampledCM 0 -none- NULL
## obsLevels 2 -none- character
## dots 0 -none- list# Plotting RFE
plot(results, type = c("g", "c"))
# Review Selected Features
print(results$optsize) # Prints the optimal size of features## [1] 2print(results$variables) # Prints the names of the selected variables at the optimal size## 0 1 Overall var Variables
## 1 57.560077217 57.560077217 57.560077217 SUB_ID 24
## 2 4.876131282 4.876131282 4.876131282 FRACSCORE 24
## 3 4.428949742 4.428949742 4.428949742 NOPRIORFRACxAGE_STDZ 24
## 4 3.959130250 3.959130250 3.959130250 BONEMED_FU 24
## 5 3.196624973 3.196624973 3.196624973 HEIGHT 24
## 6 2.847696121 2.847696121 2.847696121 BMI 24
## 7 1.905056279 1.905056279 1.905056279 BONETREAT 24
## 8 1.753818085 1.753818085 1.753818085 AGE 24
## 9 1.657072125 1.657072125 1.657072125 BONEMED 24
## 10 1.472733126 1.472733126 1.472733126 WEIGHT 24
## 11 1.202020666 1.202020666 1.202020666 AGE_STDZ 24
## 12 1.002721061 1.002721061 1.002721061 MOMFRACxARMASSIST 24
## 13 0.671048244 0.671048244 0.671048244 PRIORFRAC 24
## 14 0.618954678 0.618954678 0.618954678 RATERISK 24
## 15 0.238286534 0.238286534 0.238286534 ARMASSIST 24
## 16 0.128223582 0.128223582 0.128223582 PRIORFRACxAGE_STDZ 24
## 17 -0.128383570 -0.128383570 -0.128383570 MOMFRAC 24
## 18 -0.162165219 -0.162165219 -0.162165219 RATERISK_num 24
## 19 -0.175339388 -0.175339388 -0.175339388 RATERISK_EQ_3 24
## 20 -0.274453245 -0.274453245 -0.274453245 SMOKE 24
## 21 -0.283265976 -0.283265976 -0.283265976 AGExPRIORFRAC 24
## 22 -0.470113074 -0.470113074 -0.470113074 SITE_ID 24
## 23 -0.513873770 -0.513873770 -0.513873770 PHY_ID 24
## 24 -0.555358191 -0.555358191 -0.555358191 PREMENO 24
## 25 57.560077217 57.560077217 57.560077217 SUB_ID 5
## 26 4.876131282 4.876131282 4.876131282 FRACSCORE 5
## 27 4.428949742 4.428949742 4.428949742 NOPRIORFRACxAGE_STDZ 5
## 28 3.959130250 3.959130250 3.959130250 BONEMED_FU 5
## 29 3.196624973 3.196624973 3.196624973 HEIGHT 5
## 30 57.560077217 57.560077217 57.560077217 SUB_ID 4
## 31 4.876131282 4.876131282 4.876131282 FRACSCORE 4
## 32 4.428949742 4.428949742 4.428949742 NOPRIORFRACxAGE_STDZ 4
## 33 3.959130250 3.959130250 3.959130250 BONEMED_FU 4
## 34 57.560077217 57.560077217 57.560077217 SUB_ID 3
## 35 4.876131282 4.876131282 4.876131282 FRACSCORE 3
## 36 4.428949742 4.428949742 4.428949742 NOPRIORFRACxAGE_STDZ 3
## 37 57.560077217 57.560077217 57.560077217 SUB_ID 2
## 38 4.876131282 4.876131282 4.876131282 FRACSCORE 2
## 39 57.560077217 57.560077217 57.560077217 SUB_ID 1
## 40 60.106351341 60.106351341 60.106351341 SUB_ID 24
## 41 4.394348434 4.394348434 4.394348434 FRACSCORE 24
## 42 4.333670621 4.333670621 4.333670621 BMI 24
## 43 3.295910892 3.295910892 3.295910892 AGE_STDZ 24
## 44 3.224382679 3.224382679 3.224382679 BONEMED 24
## 45 2.861585754 2.861585754 2.861585754 NOPRIORFRACxAGE_STDZ 24
## 46 2.569018933 2.569018933 2.569018933 BONEMED_FU 24
## 47 2.141929242 2.141929242 2.141929242 WEIGHT 24
## 48 2.121453690 2.121453690 2.121453690 RATERISK_EQ_3 24
## 49 2.110967585 2.110967585 2.110967585 AGE 24
## 50 1.916234714 1.916234714 1.916234714 PRIORFRACxAGE_STDZ 24
## 51 1.817747514 1.817747514 1.817747514 BONETREAT 24
## 52 1.702698691 1.702698691 1.702698691 AGExPRIORFRAC 24
## 53 1.566460698 1.566460698 1.566460698 RATERISK 24
## 54 1.283613735 1.283613735 1.283613735 PRIORFRAC 24
## 55 1.276637664 1.276637664 1.276637664 SITE_ID 24
## 56 0.974081302 0.974081302 0.974081302 PHY_ID 24
## 57 0.812423130 0.812423130 0.812423130 ARMASSIST 24
## 58 0.688750195 0.688750195 0.688750195 PREMENO 24
## 59 0.381986178 0.381986178 0.381986178 MOMFRAC 24
## 60 0.252838045 0.252838045 0.252838045 HEIGHT 24
## 61 -0.010617450 -0.010617450 -0.010617450 RATERISK_num 24
## 62 -0.491919399 -0.491919399 -0.491919399 SMOKE 24
## 63 -0.824362228 -0.824362228 -0.824362228 MOMFRACxARMASSIST 24
## 64 60.106351341 60.106351341 60.106351341 SUB_ID 5
## 65 4.394348434 4.394348434 4.394348434 FRACSCORE 5
## 66 4.333670621 4.333670621 4.333670621 BMI 5
## 67 3.295910892 3.295910892 3.295910892 AGE_STDZ 5
## 68 3.224382679 3.224382679 3.224382679 BONEMED 5
## 69 60.106351341 60.106351341 60.106351341 SUB_ID 4
## 70 4.394348434 4.394348434 4.394348434 FRACSCORE 4
## 71 4.333670621 4.333670621 4.333670621 BMI 4
## 72 3.295910892 3.295910892 3.295910892 AGE_STDZ 4
## 73 60.106351341 60.106351341 60.106351341 SUB_ID 3
## 74 4.394348434 4.394348434 4.394348434 FRACSCORE 3
## 75 4.333670621 4.333670621 4.333670621 BMI 3
## 76 60.106351341 60.106351341 60.106351341 SUB_ID 2
## 77 4.394348434 4.394348434 4.394348434 FRACSCORE 2
## 78 60.106351341 60.106351341 60.106351341 SUB_ID 1
## 79 64.868561700 64.868561700 64.868561700 SUB_ID 24
## 80 4.457698990 4.457698990 4.457698990 BONEMED_FU 24
## 81 3.836167641 3.836167641 3.836167641 FRACSCORE 24
## 82 3.822764109 3.822764109 3.822764109 NOPRIORFRACxAGE_STDZ 24
## 83 3.015866736 3.015866736 3.015866736 HEIGHT 24
## 84 2.948353992 2.948353992 2.948353992 AGE_STDZ 24
## 85 2.740958413 2.740958413 2.740958413 BMI 24
## 86 2.610613180 2.610613180 2.610613180 BONEMED 24
## 87 1.971584877 1.971584877 1.971584877 SITE_ID 24
## 88 1.968832279 1.968832279 1.968832279 PRIORFRAC 24
## 89 1.790337952 1.790337952 1.790337952 AGE 24
## 90 1.740583277 1.740583277 1.740583277 BONETREAT 24
## 91 1.729591717 1.729591717 1.729591717 PHY_ID 24
## 92 1.285970165 1.285970165 1.285970165 WEIGHT 24
## 93 1.265369909 1.265369909 1.265369909 SMOKE 24
## 94 0.980164947 0.980164947 0.980164947 RATERISK 24
## 95 0.824808905 0.824808905 0.824808905 ARMASSIST 24
## 96 0.612806180 0.612806180 0.612806180 PRIORFRACxAGE_STDZ 24
## 97 0.417727377 0.417727377 0.417727377 MOMFRAC 24
## 98 0.158625964 0.158625964 0.158625964 RATERISK_num 24
## 99 -0.078589030 -0.078589030 -0.078589030 RATERISK_EQ_3 24
## 100 -0.350030865 -0.350030865 -0.350030865 AGExPRIORFRAC 24
## 101 -0.732221030 -0.732221030 -0.732221030 MOMFRACxARMASSIST 24
## 102 -1.754783487 -1.754783487 -1.754783487 PREMENO 24
## 103 64.868561700 64.868561700 64.868561700 SUB_ID 5
## 104 4.457698990 4.457698990 4.457698990 BONEMED_FU 5
## 105 3.836167641 3.836167641 3.836167641 FRACSCORE 5
## 106 3.822764109 3.822764109 3.822764109 NOPRIORFRACxAGE_STDZ 5
## 107 3.015866736 3.015866736 3.015866736 HEIGHT 5
## 108 64.868561700 64.868561700 64.868561700 SUB_ID 4
## 109 4.457698990 4.457698990 4.457698990 BONEMED_FU 4
## 110 3.836167641 3.836167641 3.836167641 FRACSCORE 4
## 111 3.822764109 3.822764109 3.822764109 NOPRIORFRACxAGE_STDZ 4
## 112 64.868561700 64.868561700 64.868561700 SUB_ID 3
## 113 4.457698990 4.457698990 4.457698990 BONEMED_FU 3
## 114 3.836167641 3.836167641 3.836167641 FRACSCORE 3
## 115 64.868561700 64.868561700 64.868561700 SUB_ID 2
## 116 4.457698990 4.457698990 4.457698990 BONEMED_FU 2
## 117 64.868561700 64.868561700 64.868561700 SUB_ID 1
## 118 60.704082205 60.704082205 60.704082205 SUB_ID 24
## 119 4.170806311 4.170806311 4.170806311 BMI 24
## 120 3.445115607 3.445115607 3.445115607 FRACSCORE 24
## 121 3.146389278 3.146389278 3.146389278 NOPRIORFRACxAGE_STDZ 24
## 122 2.908817688 2.908817688 2.908817688 HEIGHT 24
## 123 2.703114015 2.703114015 2.703114015 AGE_STDZ 24
## 124 2.627908309 2.627908309 2.627908309 AGE 24
## 125 2.462877867 2.462877867 2.462877867 BONEMED 24
## 126 2.420006316 2.420006316 2.420006316 BONEMED_FU 24
## 127 1.973434853 1.973434853 1.973434853 RATERISK 24
## 128 1.908790777 1.908790777 1.908790777 WEIGHT 24
## 129 1.725237196 1.725237196 1.725237196 RATERISK_num 24
## 130 1.655126622 1.655126622 1.655126622 BONETREAT 24
## 131 1.219990546 1.219990546 1.219990546 RATERISK_EQ_3 24
## 132 1.035955898 1.035955898 1.035955898 PRIORFRACxAGE_STDZ 24
## 133 0.970049394 0.970049394 0.970049394 MOMFRAC 24
## 134 0.768288423 0.768288423 0.768288423 PRIORFRAC 24
## 135 0.628509324 0.628509324 0.628509324 ARMASSIST 24
## 136 0.413762484 0.413762484 0.413762484 SITE_ID 24
## 137 0.321212093 0.321212093 0.321212093 PHY_ID 24
## 138 -0.094692778 -0.094692778 -0.094692778 SMOKE 24
## 139 -0.359087484 -0.359087484 -0.359087484 AGExPRIORFRAC 24
## 140 -0.571232166 -0.571232166 -0.571232166 MOMFRACxARMASSIST 24
## 141 -0.636349282 -0.636349282 -0.636349282 PREMENO 24
## 142 60.704082205 60.704082205 60.704082205 SUB_ID 5
## 143 4.170806311 4.170806311 4.170806311 BMI 5
## 144 3.445115607 3.445115607 3.445115607 FRACSCORE 5
## 145 3.146389278 3.146389278 3.146389278 NOPRIORFRACxAGE_STDZ 5
## 146 2.908817688 2.908817688 2.908817688 HEIGHT 5
## 147 60.704082205 60.704082205 60.704082205 SUB_ID 4
## 148 4.170806311 4.170806311 4.170806311 BMI 4
## 149 3.445115607 3.445115607 3.445115607 FRACSCORE 4
## 150 3.146389278 3.146389278 3.146389278 NOPRIORFRACxAGE_STDZ 4
## 151 60.704082205 60.704082205 60.704082205 SUB_ID 3
## 152 4.170806311 4.170806311 4.170806311 BMI 3
## 153 3.445115607 3.445115607 3.445115607 FRACSCORE 3
## 154 60.704082205 60.704082205 60.704082205 SUB_ID 2
## 155 4.170806311 4.170806311 4.170806311 BMI 2
## 156 60.704082205 60.704082205 60.704082205 SUB_ID 1
## 157 62.014254305 62.014254305 62.014254305 SUB_ID 24
## 158 4.804068429 4.804068429 4.804068429 NOPRIORFRACxAGE_STDZ 24
## 159 3.587218592 3.587218592 3.587218592 FRACSCORE 24
## 160 2.969459221 2.969459221 2.969459221 BONEMED_FU 24
## 161 2.817837711 2.817837711 2.817837711 AGE 24
## 162 2.810708593 2.810708593 2.810708593 BMI 24
## 163 2.799589536 2.799589536 2.799589536 HEIGHT 24
## 164 2.265486066 2.265486066 2.265486066 AGE_STDZ 24
## 165 2.099997773 2.099997773 2.099997773 WEIGHT 24
## 166 1.899656303 1.899656303 1.899656303 BONEMED 24
## 167 1.868095343 1.868095343 1.868095343 BONETREAT 24
## 168 1.823136136 1.823136136 1.823136136 PRIORFRAC 24
## 169 1.487352232 1.487352232 1.487352232 RATERISK_num 24
## 170 1.176126814 1.176126814 1.176126814 PREMENO 24
## 171 1.092240152 1.092240152 1.092240152 RATERISK_EQ_3 24
## 172 0.490931515 0.490931515 0.490931515 PHY_ID 24
## 173 0.384846780 0.384846780 0.384846780 ARMASSIST 24
## 174 0.375829077 0.375829077 0.375829077 SITE_ID 24
## 175 0.166564255 0.166564255 0.166564255 MOMFRACxARMASSIST 24
## 176 -0.041467135 -0.041467135 -0.041467135 MOMFRAC 24
## 177 -0.152673285 -0.152673285 -0.152673285 SMOKE 24
## 178 -0.323175397 -0.323175397 -0.323175397 AGExPRIORFRAC 24
## 179 -0.375226290 -0.375226290 -0.375226290 RATERISK 24
## 180 -0.951728460 -0.951728460 -0.951728460 PRIORFRACxAGE_STDZ 24
## 181 62.014254305 62.014254305 62.014254305 SUB_ID 5
## 182 4.804068429 4.804068429 4.804068429 NOPRIORFRACxAGE_STDZ 5
## 183 3.587218592 3.587218592 3.587218592 FRACSCORE 5
## 184 2.969459221 2.969459221 2.969459221 BONEMED_FU 5
## 185 2.817837711 2.817837711 2.817837711 AGE 5
## 186 62.014254305 62.014254305 62.014254305 SUB_ID 4
## 187 4.804068429 4.804068429 4.804068429 NOPRIORFRACxAGE_STDZ 4
## 188 3.587218592 3.587218592 3.587218592 FRACSCORE 4
## 189 2.969459221 2.969459221 2.969459221 BONEMED_FU 4
## 190 62.014254305 62.014254305 62.014254305 SUB_ID 3
## 191 4.804068429 4.804068429 4.804068429 NOPRIORFRACxAGE_STDZ 3
## 192 3.587218592 3.587218592 3.587218592 FRACSCORE 3
## 193 62.014254305 62.014254305 62.014254305 SUB_ID 2
## 194 4.804068429 4.804068429 4.804068429 NOPRIORFRACxAGE_STDZ 2
## 195 62.014254305 62.014254305 62.014254305 SUB_ID 1
## 196 64.428242331 64.428242331 64.428242331 SUB_ID 24
## 197 4.605380189 4.605380189 4.605380189 BONEMED_FU 24
## 198 4.392259548 4.392259548 4.392259548 NOPRIORFRACxAGE_STDZ 24
## 199 4.120443751 4.120443751 4.120443751 WEIGHT 24
## 200 3.786738358 3.786738358 3.786738358 BMI 24
## 201 3.750144768 3.750144768 3.750144768 FRACSCORE 24
## 202 3.212990536 3.212990536 3.212990536 AGE_STDZ 24
## 203 3.100047228 3.100047228 3.100047228 BONEMED 24
## 204 2.781647440 2.781647440 2.781647440 BONETREAT 24
## 205 2.739360886 2.739360886 2.739360886 AGE 24
## 206 2.614621042 2.614621042 2.614621042 HEIGHT 24
## 207 1.469451773 1.469451773 1.469451773 MOMFRAC 24
## 208 1.081573482 1.081573482 1.081573482 ARMASSIST 24
## 209 0.778039950 0.778039950 0.778039950 AGExPRIORFRAC 24
## 210 0.683891129 0.683891129 0.683891129 PHY_ID 24
## 211 0.599734795 0.599734795 0.599734795 SMOKE 24
## 212 0.517938843 0.517938843 0.517938843 SITE_ID 24
## 213 0.504746517 0.504746517 0.504746517 MOMFRACxARMASSIST 24
## 214 0.498870765 0.498870765 0.498870765 PRIORFRACxAGE_STDZ 24
## 215 0.397244050 0.397244050 0.397244050 PRIORFRAC 24
## 216 0.204675708 0.204675708 0.204675708 RATERISK_EQ_3 24
## 217 0.128004383 0.128004383 0.128004383 RATERISK_num 24
## 218 -0.285668174 -0.285668174 -0.285668174 PREMENO 24
## 219 -0.698585608 -0.698585608 -0.698585608 RATERISK 24
## 220 64.428242331 64.428242331 64.428242331 SUB_ID 5
## 221 4.605380189 4.605380189 4.605380189 BONEMED_FU 5
## 222 4.392259548 4.392259548 4.392259548 NOPRIORFRACxAGE_STDZ 5
## 223 4.120443751 4.120443751 4.120443751 WEIGHT 5
## 224 3.786738358 3.786738358 3.786738358 BMI 5
## 225 64.428242331 64.428242331 64.428242331 SUB_ID 4
## 226 4.605380189 4.605380189 4.605380189 BONEMED_FU 4
## 227 4.392259548 4.392259548 4.392259548 NOPRIORFRACxAGE_STDZ 4
## 228 4.120443751 4.120443751 4.120443751 WEIGHT 4
## 229 64.428242331 64.428242331 64.428242331 SUB_ID 3
## 230 4.605380189 4.605380189 4.605380189 BONEMED_FU 3
## 231 4.392259548 4.392259548 4.392259548 NOPRIORFRACxAGE_STDZ 3
## 232 64.428242331 64.428242331 64.428242331 SUB_ID 2
## 233 4.605380189 4.605380189 4.605380189 BONEMED_FU 2
## 234 64.428242331 64.428242331 64.428242331 SUB_ID 1
## 235 66.133764401 66.133764401 66.133764401 SUB_ID 24
## 236 4.109521189 4.109521189 4.109521189 HEIGHT 24
## 237 3.684737228 3.684737228 3.684737228 FRACSCORE 24
## 238 3.402606780 3.402606780 3.402606780 BONEMED_FU 24
## 239 3.097924170 3.097924170 3.097924170 NOPRIORFRACxAGE_STDZ 24
## 240 3.016479718 3.016479718 3.016479718 BMI 24
## 241 2.808045339 2.808045339 2.808045339 AGE 24
## 242 2.672651597 2.672651597 2.672651597 AGE_STDZ 24
## 243 2.462908621 2.462908621 2.462908621 WEIGHT 24
## 244 2.427683610 2.427683610 2.427683610 BONETREAT 24
## 245 1.802728967 1.802728967 1.802728967 RATERISK_num 24
## 246 1.643977745 1.643977745 1.643977745 PRIORFRAC 24
## 247 1.280948105 1.280948105 1.280948105 RATERISK_EQ_3 24
## 248 1.136330917 1.136330917 1.136330917 PRIORFRACxAGE_STDZ 24
## 249 1.092603513 1.092603513 1.092603513 RATERISK 24
## 250 0.780742057 0.780742057 0.780742057 AGExPRIORFRAC 24
## 251 0.745339333 0.745339333 0.745339333 BONEMED 24
## 252 0.706376428 0.706376428 0.706376428 SITE_ID 24
## 253 0.627412079 0.627412079 0.627412079 PHY_ID 24
## 254 0.287197998 0.287197998 0.287197998 ARMASSIST 24
## 255 -0.273806919 -0.273806919 -0.273806919 PREMENO 24
## 256 -0.306308387 -0.306308387 -0.306308387 MOMFRAC 24
## 257 -0.339268774 -0.339268774 -0.339268774 SMOKE 24
## 258 -0.674896595 -0.674896595 -0.674896595 MOMFRACxARMASSIST 24
## 259 66.133764401 66.133764401 66.133764401 SUB_ID 5
## 260 4.109521189 4.109521189 4.109521189 HEIGHT 5
## 261 3.684737228 3.684737228 3.684737228 FRACSCORE 5
## 262 3.402606780 3.402606780 3.402606780 BONEMED_FU 5
## 263 3.097924170 3.097924170 3.097924170 NOPRIORFRACxAGE_STDZ 5
## 264 66.133764401 66.133764401 66.133764401 SUB_ID 4
## 265 4.109521189 4.109521189 4.109521189 HEIGHT 4
## 266 3.684737228 3.684737228 3.684737228 FRACSCORE 4
## 267 3.402606780 3.402606780 3.402606780 BONEMED_FU 4
## 268 66.133764401 66.133764401 66.133764401 SUB_ID 3
## 269 4.109521189 4.109521189 4.109521189 HEIGHT 3
## 270 3.684737228 3.684737228 3.684737228 FRACSCORE 3
## 271 66.133764401 66.133764401 66.133764401 SUB_ID 2
## 272 4.109521189 4.109521189 4.109521189 HEIGHT 2
## 273 66.133764401 66.133764401 66.133764401 SUB_ID 1
## 274 64.370936726 64.370936726 64.370936726 SUB_ID 24
## 275 4.121667641 4.121667641 4.121667641 FRACSCORE 24
## 276 3.818725825 3.818725825 3.818725825 NOPRIORFRACxAGE_STDZ 24
## 277 3.480570530 3.480570530 3.480570530 AGE 24
## 278 3.383970860 3.383970860 3.383970860 BMI 24
## 279 3.310161138 3.310161138 3.310161138 HEIGHT 24
## 280 2.913399316 2.913399316 2.913399316 WEIGHT 24
## 281 2.255453542 2.255453542 2.255453542 AGE_STDZ 24
## 282 1.899419886 1.899419886 1.899419886 BONEMED 24
## 283 1.760231260 1.760231260 1.760231260 RATERISK_num 24
## 284 1.350814663 1.350814663 1.350814663 BONEMED_FU 24
## 285 1.081309545 1.081309545 1.081309545 AGExPRIORFRAC 24
## 286 1.013353736 1.013353736 1.013353736 RATERISK 24
## 287 0.919453189 0.919453189 0.919453189 PRIORFRAC 24
## 288 0.912058796 0.912058796 0.912058796 ARMASSIST 24
## 289 0.816017904 0.816017904 0.816017904 SMOKE 24
## 290 0.649528436 0.649528436 0.649528436 PRIORFRACxAGE_STDZ 24
## 291 0.507917688 0.507917688 0.507917688 BONETREAT 24
## 292 0.503544482 0.503544482 0.503544482 SITE_ID 24
## 293 0.483162579 0.483162579 0.483162579 MOMFRAC 24
## 294 0.448468261 0.448468261 0.448468261 PHY_ID 24
## 295 0.414413565 0.414413565 0.414413565 MOMFRACxARMASSIST 24
## 296 -0.692811985 -0.692811985 -0.692811985 RATERISK_EQ_3 24
## 297 -0.780074633 -0.780074633 -0.780074633 PREMENO 24
## 298 64.370936726 64.370936726 64.370936726 SUB_ID 5
## 299 4.121667641 4.121667641 4.121667641 FRACSCORE 5
## 300 3.818725825 3.818725825 3.818725825 NOPRIORFRACxAGE_STDZ 5
## 301 3.480570530 3.480570530 3.480570530 AGE 5
## 302 3.383970860 3.383970860 3.383970860 BMI 5
## 303 64.370936726 64.370936726 64.370936726 SUB_ID 4
## 304 4.121667641 4.121667641 4.121667641 FRACSCORE 4
## 305 3.818725825 3.818725825 3.818725825 NOPRIORFRACxAGE_STDZ 4
## 306 3.480570530 3.480570530 3.480570530 AGE 4
## 307 64.370936726 64.370936726 64.370936726 SUB_ID 3
## 308 4.121667641 4.121667641 4.121667641 FRACSCORE 3
## 309 3.818725825 3.818725825 3.818725825 NOPRIORFRACxAGE_STDZ 3
## 310 64.370936726 64.370936726 64.370936726 SUB_ID 2
## 311 4.121667641 4.121667641 4.121667641 FRACSCORE 2
## 312 64.370936726 64.370936726 64.370936726 SUB_ID 1
## 313 63.125141920 63.125141920 63.125141920 SUB_ID 24
## 314 3.942219489 3.942219489 3.942219489 FRACSCORE 24
## 315 3.807479234 3.807479234 3.807479234 NOPRIORFRACxAGE_STDZ 24
## 316 3.508663495 3.508663495 3.508663495 AGE_STDZ 24
## 317 3.367260987 3.367260987 3.367260987 BMI 24
## 318 2.964456424 2.964456424 2.964456424 AGE 24
## 319 2.428819652 2.428819652 2.428819652 WEIGHT 24
## 320 2.382388047 2.382388047 2.382388047 PRIORFRAC 24
## 321 2.291639095 2.291639095 2.291639095 HEIGHT 24
## 322 1.940033035 1.940033035 1.940033035 BONEMED 24
## 323 1.561873520 1.561873520 1.561873520 BONEMED_FU 24
## 324 1.176172249 1.176172249 1.176172249 PHY_ID 24
## 325 0.996851154 0.996851154 0.996851154 SITE_ID 24
## 326 0.983529079 0.983529079 0.983529079 BONETREAT 24
## 327 0.814637047 0.814637047 0.814637047 ARMASSIST 24
## 328 0.604491879 0.604491879 0.604491879 RATERISK_num 24
## 329 0.567120091 0.567120091 0.567120091 RATERISK_EQ_3 24
## 330 0.391048636 0.391048636 0.391048636 PRIORFRACxAGE_STDZ 24
## 331 0.259463666 0.259463666 0.259463666 AGExPRIORFRAC 24
## 332 0.212670305 0.212670305 0.212670305 MOMFRAC 24
## 333 -0.040304679 -0.040304679 -0.040304679 SMOKE 24
## 334 -0.317433285 -0.317433285 -0.317433285 RATERISK 24
## 335 -0.727185660 -0.727185660 -0.727185660 MOMFRACxARMASSIST 24
## 336 -0.875799121 -0.875799121 -0.875799121 PREMENO 24
## 337 63.125141920 63.125141920 63.125141920 SUB_ID 5
## 338 3.942219489 3.942219489 3.942219489 FRACSCORE 5
## 339 3.807479234 3.807479234 3.807479234 NOPRIORFRACxAGE_STDZ 5
## 340 3.508663495 3.508663495 3.508663495 AGE_STDZ 5
## 341 3.367260987 3.367260987 3.367260987 BMI 5
## 342 63.125141920 63.125141920 63.125141920 SUB_ID 4
## 343 3.942219489 3.942219489 3.942219489 FRACSCORE 4
## 344 3.807479234 3.807479234 3.807479234 NOPRIORFRACxAGE_STDZ 4
## 345 3.508663495 3.508663495 3.508663495 AGE_STDZ 4
## 346 63.125141920 63.125141920 63.125141920 SUB_ID 3
## 347 3.942219489 3.942219489 3.942219489 FRACSCORE 3
## 348 3.807479234 3.807479234 3.807479234 NOPRIORFRACxAGE_STDZ 3
## 349 63.125141920 63.125141920 63.125141920 SUB_ID 2
## 350 3.942219489 3.942219489 3.942219489 FRACSCORE 2
## 351 63.125141920 63.125141920 63.125141920 SUB_ID 1
## 352 58.898982804 58.898982804 58.898982804 SUB_ID 24
## 353 4.832957850 4.832957850 4.832957850 FRACSCORE 24
## 354 3.473173018 3.473173018 3.473173018 NOPRIORFRACxAGE_STDZ 24
## 355 3.387269022 3.387269022 3.387269022 HEIGHT 24
## 356 3.184519271 3.184519271 3.184519271 BONEMED_FU 24
## 357 3.045256285 3.045256285 3.045256285 AGE_STDZ 24
## 358 2.789952906 2.789952906 2.789952906 WEIGHT 24
## 359 2.701867873 2.701867873 2.701867873 BMI 24
## 360 2.488195410 2.488195410 2.488195410 BONEMED 24
## 361 2.350417472 2.350417472 2.350417472 AGE 24
## 362 1.984149714 1.984149714 1.984149714 PHY_ID 24
## 363 1.767882598 1.767882598 1.767882598 BONETREAT 24
## 364 1.544718359 1.544718359 1.544718359 MOMFRAC 24
## 365 1.400388634 1.400388634 1.400388634 SITE_ID 24
## 366 1.164366806 1.164366806 1.164366806 PRIORFRAC 24
## 367 1.151190069 1.151190069 1.151190069 RATERISK_EQ_3 24
## 368 0.414599555 0.414599555 0.414599555 ARMASSIST 24
## 369 0.347032796 0.347032796 0.347032796 RATERISK 24
## 370 0.082316698 0.082316698 0.082316698 PRIORFRACxAGE_STDZ 24
## 371 -0.220317387 -0.220317387 -0.220317387 MOMFRACxARMASSIST 24
## 372 -0.235868235 -0.235868235 -0.235868235 RATERISK_num 24
## 373 -0.312752754 -0.312752754 -0.312752754 AGExPRIORFRAC 24
## 374 -0.382476684 -0.382476684 -0.382476684 PREMENO 24
## 375 -0.484148255 -0.484148255 -0.484148255 SMOKE 24
## 376 58.898982804 58.898982804 58.898982804 SUB_ID 5
## 377 4.832957850 4.832957850 4.832957850 FRACSCORE 5
## 378 3.473173018 3.473173018 3.473173018 NOPRIORFRACxAGE_STDZ 5
## 379 3.387269022 3.387269022 3.387269022 HEIGHT 5
## 380 3.184519271 3.184519271 3.184519271 BONEMED_FU 5
## 381 58.898982804 58.898982804 58.898982804 SUB_ID 4
## 382 4.832957850 4.832957850 4.832957850 FRACSCORE 4
## 383 3.473173018 3.473173018 3.473173018 NOPRIORFRACxAGE_STDZ 4
## 384 3.387269022 3.387269022 3.387269022 HEIGHT 4
## 385 58.898982804 58.898982804 58.898982804 SUB_ID 3
## 386 4.832957850 4.832957850 4.832957850 FRACSCORE 3
## 387 3.473173018 3.473173018 3.473173018 NOPRIORFRACxAGE_STDZ 3
## 388 58.898982804 58.898982804 58.898982804 SUB_ID 2
## 389 4.832957850 4.832957850 4.832957850 FRACSCORE 2
## 390 58.898982804 58.898982804 58.898982804 SUB_ID 1
## 391 61.592153876 61.592153876 61.592153876 SUB_ID 24
## 392 4.102182921 4.102182921 4.102182921 FRACSCORE 24
## 393 3.129707151 3.129707151 3.129707151 HEIGHT 24
## 394 2.847378757 2.847378757 2.847378757 NOPRIORFRACxAGE_STDZ 24
## 395 2.805512621 2.805512621 2.805512621 AGE 24
## 396 2.666626521 2.666626521 2.666626521 BONEMED 24
## 397 2.345887709 2.345887709 2.345887709 BMI 24
## 398 2.233838392 2.233838392 2.233838392 BONEMED_FU 24
## 399 2.201681255 2.201681255 2.201681255 WEIGHT 24
## 400 2.029842755 2.029842755 2.029842755 ARMASSIST 24
## 401 1.487991968 1.487991968 1.487991968 BONETREAT 24
## 402 1.094267713 1.094267713 1.094267713 RATERISK_EQ_3 24
## 403 1.058188072 1.058188072 1.058188072 PREMENO 24
## 404 0.970782814 0.970782814 0.970782814 AGE_STDZ 24
## 405 0.678008079 0.678008079 0.678008079 PRIORFRAC 24
## 406 0.590266341 0.590266341 0.590266341 SITE_ID 24
## 407 0.448088814 0.448088814 0.448088814 PHY_ID 24
## 408 0.365112245 0.365112245 0.365112245 AGExPRIORFRAC 24
## 409 0.332208697 0.332208697 0.332208697 RATERISK 24
## 410 -0.128796592 -0.128796592 -0.128796592 RATERISK_num 24
## 411 -0.259330290 -0.259330290 -0.259330290 MOMFRACxARMASSIST 24
## 412 -0.420008807 -0.420008807 -0.420008807 SMOKE 24
## 413 -0.636000940 -0.636000940 -0.636000940 PRIORFRACxAGE_STDZ 24
## 414 -0.921437030 -0.921437030 -0.921437030 MOMFRAC 24
## 415 61.592153876 61.592153876 61.592153876 SUB_ID 5
## 416 4.102182921 4.102182921 4.102182921 FRACSCORE 5
## 417 3.129707151 3.129707151 3.129707151 HEIGHT 5
## 418 2.847378757 2.847378757 2.847378757 NOPRIORFRACxAGE_STDZ 5
## 419 2.805512621 2.805512621 2.805512621 AGE 5
## 420 61.592153876 61.592153876 61.592153876 SUB_ID 4
## 421 4.102182921 4.102182921 4.102182921 FRACSCORE 4
## 422 3.129707151 3.129707151 3.129707151 HEIGHT 4
## 423 2.847378757 2.847378757 2.847378757 NOPRIORFRACxAGE_STDZ 4
## 424 61.592153876 61.592153876 61.592153876 SUB_ID 3
## 425 4.102182921 4.102182921 4.102182921 FRACSCORE 3
## 426 3.129707151 3.129707151 3.129707151 HEIGHT 3
## 427 61.592153876 61.592153876 61.592153876 SUB_ID 2
## 428 4.102182921 4.102182921 4.102182921 FRACSCORE 2
## 429 61.592153876 61.592153876 61.592153876 SUB_ID 1
## 430 58.420184251 58.420184251 58.420184251 SUB_ID 24
## 431 4.437892003 4.437892003 4.437892003 BONEMED_FU 24
## 432 3.939081478 3.939081478 3.939081478 NOPRIORFRACxAGE_STDZ 24
## 433 3.891240009 3.891240009 3.891240009 WEIGHT 24
## 434 3.875607521 3.875607521 3.875607521 BMI 24
## 435 3.749497481 3.749497481 3.749497481 AGE_STDZ 24
## 436 3.036473975 3.036473975 3.036473975 BONETREAT 24
## 437 2.677378533 2.677378533 2.677378533 FRACSCORE 24
## 438 2.270759118 2.270759118 2.270759118 AGE 24
## 439 1.912648176 1.912648176 1.912648176 PRIORFRAC 24
## 440 1.781586039 1.781586039 1.781586039 BONEMED 24
## 441 1.735775746 1.735775746 1.735775746 SMOKE 24
## 442 1.584261276 1.584261276 1.584261276 HEIGHT 24
## 443 1.570200128 1.570200128 1.570200128 SITE_ID 24
## 444 1.453981635 1.453981635 1.453981635 PRIORFRACxAGE_STDZ 24
## 445 1.211136661 1.211136661 1.211136661 RATERISK 24
## 446 0.917834464 0.917834464 0.917834464 ARMASSIST 24
## 447 0.686661902 0.686661902 0.686661902 RATERISK_EQ_3 24
## 448 0.472347799 0.472347799 0.472347799 RATERISK_num 24
## 449 0.275766056 0.275766056 0.275766056 PHY_ID 24
## 450 -0.059729430 -0.059729430 -0.059729430 AGExPRIORFRAC 24
## 451 -0.372313995 -0.372313995 -0.372313995 PREMENO 24
## 452 -0.527113930 -0.527113930 -0.527113930 MOMFRACxARMASSIST 24
## 453 -1.552054799 -1.552054799 -1.552054799 MOMFRAC 24
## 454 58.420184251 58.420184251 58.420184251 SUB_ID 5
## 455 4.437892003 4.437892003 4.437892003 BONEMED_FU 5
## 456 3.939081478 3.939081478 3.939081478 NOPRIORFRACxAGE_STDZ 5
## 457 3.891240009 3.891240009 3.891240009 WEIGHT 5
## 458 3.875607521 3.875607521 3.875607521 BMI 5
## 459 58.420184251 58.420184251 58.420184251 SUB_ID 4
## 460 4.437892003 4.437892003 4.437892003 BONEMED_FU 4
## 461 3.939081478 3.939081478 3.939081478 NOPRIORFRACxAGE_STDZ 4
## 462 3.891240009 3.891240009 3.891240009 WEIGHT 4
## 463 58.420184251 58.420184251 58.420184251 SUB_ID 3
## 464 4.437892003 4.437892003 4.437892003 BONEMED_FU 3
## 465 3.939081478 3.939081478 3.939081478 NOPRIORFRACxAGE_STDZ 3
## 466 58.420184251 58.420184251 58.420184251 SUB_ID 2
## 467 4.437892003 4.437892003 4.437892003 BONEMED_FU 2
## 468 58.420184251 58.420184251 58.420184251 SUB_ID 1
## 469 61.668546795 61.668546795 61.668546795 SUB_ID 24
## 470 4.784306790 4.784306790 4.784306790 FRACSCORE 24
## 471 4.041585024 4.041585024 4.041585024 NOPRIORFRACxAGE_STDZ 24
## 472 3.985860672 3.985860672 3.985860672 AGE 24
## 473 3.469612111 3.469612111 3.469612111 HEIGHT 24
## 474 2.860672368 2.860672368 2.860672368 BONEMED_FU 24
## 475 2.775784457 2.775784457 2.775784457 WEIGHT 24
## 476 2.692003541 2.692003541 2.692003541 AGE_STDZ 24
## 477 2.541397707 2.541397707 2.541397707 BMI 24
## 478 1.785356825 1.785356825 1.785356825 BONEMED 24
## 479 1.753387463 1.753387463 1.753387463 BONETREAT 24
## 480 1.487972671 1.487972671 1.487972671 PHY_ID 24
## 481 1.432751216 1.432751216 1.432751216 SITE_ID 24
## 482 1.340156442 1.340156442 1.340156442 PRIORFRACxAGE_STDZ 24
## 483 1.158435493 1.158435493 1.158435493 RATERISK_EQ_3 24
## 484 1.097618380 1.097618380 1.097618380 PRIORFRAC 24
## 485 1.026549111 1.026549111 1.026549111 MOMFRAC 24
## 486 0.552146508 0.552146508 0.552146508 RATERISK_num 24
## 487 0.483851917 0.483851917 0.483851917 SMOKE 24
## 488 0.388632218 0.388632218 0.388632218 ARMASSIST 24
## 489 0.268584845 0.268584845 0.268584845 RATERISK 24
## 490 0.264259572 0.264259572 0.264259572 PREMENO 24
## 491 -0.128534430 -0.128534430 -0.128534430 AGExPRIORFRAC 24
## 492 -0.429425688 -0.429425688 -0.429425688 MOMFRACxARMASSIST 24
## 493 61.668546795 61.668546795 61.668546795 SUB_ID 5
## 494 4.784306790 4.784306790 4.784306790 FRACSCORE 5
## 495 4.041585024 4.041585024 4.041585024 NOPRIORFRACxAGE_STDZ 5
## 496 3.985860672 3.985860672 3.985860672 AGE 5
## 497 3.469612111 3.469612111 3.469612111 HEIGHT 5
## 498 61.668546795 61.668546795 61.668546795 SUB_ID 4
## 499 4.784306790 4.784306790 4.784306790 FRACSCORE 4
## 500 4.041585024 4.041585024 4.041585024 NOPRIORFRACxAGE_STDZ 4
## 501 3.985860672 3.985860672 3.985860672 AGE 4
## 502 61.668546795 61.668546795 61.668546795 SUB_ID 3
## 503 4.784306790 4.784306790 4.784306790 FRACSCORE 3
## 504 4.041585024 4.041585024 4.041585024 NOPRIORFRACxAGE_STDZ 3
## 505 61.668546795 61.668546795 61.668546795 SUB_ID 2
## 506 4.784306790 4.784306790 4.784306790 FRACSCORE 2
## 507 61.668546795 61.668546795 61.668546795 SUB_ID 1
## 508 62.695852312 62.695852312 62.695852312 SUB_ID 24
## 509 3.669116716 3.669116716 3.669116716 NOPRIORFRACxAGE_STDZ 24
## 510 3.261061204 3.261061204 3.261061204 FRACSCORE 24
## 511 2.992964339 2.992964339 2.992964339 BONEMED_FU 24
## 512 2.558828839 2.558828839 2.558828839 HEIGHT 24
## 513 2.469205560 2.469205560 2.469205560 BMI 24
## 514 2.317561583 2.317561583 2.317561583 WEIGHT 24
## 515 2.230300331 2.230300331 2.230300331 AGE 24
## 516 2.052411350 2.052411350 2.052411350 AGE_STDZ 24
## 517 2.042411565 2.042411565 2.042411565 BONEMED 24
## 518 1.866638359 1.866638359 1.866638359 BONETREAT 24
## 519 0.917704819 0.917704819 0.917704819 ARMASSIST 24
## 520 0.875552517 0.875552517 0.875552517 SITE_ID 24
## 521 0.782697582 0.782697582 0.782697582 MOMFRAC 24
## 522 0.434098678 0.434098678 0.434098678 PRIORFRAC 24
## 523 0.390720467 0.390720467 0.390720467 RATERISK_num 24
## 524 -0.026208765 -0.026208765 -0.026208765 PHY_ID 24
## 525 -0.180958559 -0.180958559 -0.180958559 AGExPRIORFRAC 24
## 526 -0.201597232 -0.201597232 -0.201597232 RATERISK_EQ_3 24
## 527 -0.232520954 -0.232520954 -0.232520954 PREMENO 24
## 528 -0.243683594 -0.243683594 -0.243683594 RATERISK 24
## 529 -0.519554417 -0.519554417 -0.519554417 MOMFRACxARMASSIST 24
## 530 -0.640809723 -0.640809723 -0.640809723 SMOKE 24
## 531 -1.152241187 -1.152241187 -1.152241187 PRIORFRACxAGE_STDZ 24
## 532 62.695852312 62.695852312 62.695852312 SUB_ID 5
## 533 3.669116716 3.669116716 3.669116716 NOPRIORFRACxAGE_STDZ 5
## 534 3.261061204 3.261061204 3.261061204 FRACSCORE 5
## 535 2.992964339 2.992964339 2.992964339 BONEMED_FU 5
## 536 2.558828839 2.558828839 2.558828839 HEIGHT 5
## 537 62.695852312 62.695852312 62.695852312 SUB_ID 4
## 538 3.669116716 3.669116716 3.669116716 NOPRIORFRACxAGE_STDZ 4
## 539 3.261061204 3.261061204 3.261061204 FRACSCORE 4
## 540 2.992964339 2.992964339 2.992964339 BONEMED_FU 4
## 541 62.695852312 62.695852312 62.695852312 SUB_ID 3
## 542 3.669116716 3.669116716 3.669116716 NOPRIORFRACxAGE_STDZ 3
## 543 3.261061204 3.261061204 3.261061204 FRACSCORE 3
## 544 62.695852312 62.695852312 62.695852312 SUB_ID 2
## 545 3.669116716 3.669116716 3.669116716 NOPRIORFRACxAGE_STDZ 2
## 546 62.695852312 62.695852312 62.695852312 SUB_ID 1
## 547 57.447028118 57.447028118 57.447028118 SUB_ID 24
## 548 5.330440569 5.330440569 5.330440569 FRACSCORE 24
## 549 4.035837080 4.035837080 4.035837080 NOPRIORFRACxAGE_STDZ 24
## 550 3.238179235 3.238179235 3.238179235 BMI 24
## 551 2.728324178 2.728324178 2.728324178 AGE 24
## 552 2.429485368 2.429485368 2.429485368 HEIGHT 24
## 553 2.381492959 2.381492959 2.381492959 WEIGHT 24
## 554 2.286385150 2.286385150 2.286385150 BONEMED_FU 24
## 555 2.205912829 2.205912829 2.205912829 BONEMED 24
## 556 2.012309990 2.012309990 2.012309990 AGE_STDZ 24
## 557 1.381756194 1.381756194 1.381756194 SITE_ID 24
## 558 1.311096458 1.311096458 1.311096458 RATERISK_EQ_3 24
## 559 1.120646091 1.120646091 1.120646091 PRIORFRAC 24
## 560 1.036560399 1.036560399 1.036560399 MOMFRACxARMASSIST 24
## 561 0.769114156 0.769114156 0.769114156 BONETREAT 24
## 562 0.691033689 0.691033689 0.691033689 RATERISK_num 24
## 563 0.612164297 0.612164297 0.612164297 PRIORFRACxAGE_STDZ 24
## 564 0.563754671 0.563754671 0.563754671 AGExPRIORFRAC 24
## 565 0.519056767 0.519056767 0.519056767 PHY_ID 24
## 566 0.270015614 0.270015614 0.270015614 MOMFRAC 24
## 567 0.135791661 0.135791661 0.135791661 ARMASSIST 24
## 568 -0.894247161 -0.894247161 -0.894247161 RATERISK 24
## 569 -0.924677848 -0.924677848 -0.924677848 PREMENO 24
## 570 -1.389592717 -1.389592717 -1.389592717 SMOKE 24
## 571 57.447028118 57.447028118 57.447028118 SUB_ID 5
## 572 5.330440569 5.330440569 5.330440569 FRACSCORE 5
## 573 4.035837080 4.035837080 4.035837080 NOPRIORFRACxAGE_STDZ 5
## 574 3.238179235 3.238179235 3.238179235 BMI 5
## 575 2.728324178 2.728324178 2.728324178 AGE 5
## 576 57.447028118 57.447028118 57.447028118 SUB_ID 4
## 577 5.330440569 5.330440569 5.330440569 FRACSCORE 4
## 578 4.035837080 4.035837080 4.035837080 NOPRIORFRACxAGE_STDZ 4
## 579 3.238179235 3.238179235 3.238179235 BMI 4
## 580 57.447028118 57.447028118 57.447028118 SUB_ID 3
## 581 5.330440569 5.330440569 5.330440569 FRACSCORE 3
## 582 4.035837080 4.035837080 4.035837080 NOPRIORFRACxAGE_STDZ 3
## 583 57.447028118 57.447028118 57.447028118 SUB_ID 2
## 584 5.330440569 5.330440569 5.330440569 FRACSCORE 2
## 585 57.447028118 57.447028118 57.447028118 SUB_ID 1
## 586 61.836966268 61.836966268 61.836966268 SUB_ID 24
## 587 3.706788600 3.706788600 3.706788600 FRACSCORE 24
## 588 3.048427174 3.048427174 3.048427174 NOPRIORFRACxAGE_STDZ 24
## 589 2.734911834 2.734911834 2.734911834 BMI 24
## 590 2.292450122 2.292450122 2.292450122 WEIGHT 24
## 591 2.183844823 2.183844823 2.183844823 BONEMED_FU 24
## 592 1.843785218 1.843785218 1.843785218 AGE 24
## 593 1.815426709 1.815426709 1.815426709 SITE_ID 24
## 594 1.808915355 1.808915355 1.808915355 BONEMED 24
## 595 1.650590922 1.650590922 1.650590922 BONETREAT 24
## 596 1.646546782 1.646546782 1.646546782 HEIGHT 24
## 597 1.494713264 1.494713264 1.494713264 ARMASSIST 24
## 598 1.281369014 1.281369014 1.281369014 AGE_STDZ 24
## 599 1.263070798 1.263070798 1.263070798 PHY_ID 24
## 600 1.084503998 1.084503998 1.084503998 RATERISK_num 24
## 601 0.921920302 0.921920302 0.921920302 SMOKE 24
## 602 0.797415132 0.797415132 0.797415132 RATERISK 24
## 603 0.693147939 0.693147939 0.693147939 MOMFRACxARMASSIST 24
## 604 0.658427790 0.658427790 0.658427790 PREMENO 24
## 605 0.513453878 0.513453878 0.513453878 PRIORFRAC 24
## 606 0.513174353 0.513174353 0.513174353 AGExPRIORFRAC 24
## 607 0.477363193 0.477363193 0.477363193 RATERISK_EQ_3 24
## 608 -0.227726900 -0.227726900 -0.227726900 PRIORFRACxAGE_STDZ 24
## 609 -0.687182515 -0.687182515 -0.687182515 MOMFRAC 24
## 610 61.836966268 61.836966268 61.836966268 SUB_ID 5
## 611 3.706788600 3.706788600 3.706788600 FRACSCORE 5
## 612 3.048427174 3.048427174 3.048427174 NOPRIORFRACxAGE_STDZ 5
## 613 2.734911834 2.734911834 2.734911834 BMI 5
## 614 2.292450122 2.292450122 2.292450122 WEIGHT 5
## 615 61.836966268 61.836966268 61.836966268 SUB_ID 4
## 616 3.706788600 3.706788600 3.706788600 FRACSCORE 4
## 617 3.048427174 3.048427174 3.048427174 NOPRIORFRACxAGE_STDZ 4
## 618 2.734911834 2.734911834 2.734911834 BMI 4
## 619 61.836966268 61.836966268 61.836966268 SUB_ID 3
## 620 3.706788600 3.706788600 3.706788600 FRACSCORE 3
## 621 3.048427174 3.048427174 3.048427174 NOPRIORFRACxAGE_STDZ 3
## 622 61.836966268 61.836966268 61.836966268 SUB_ID 2
## 623 3.706788600 3.706788600 3.706788600 FRACSCORE 2
## 624 61.836966268 61.836966268 61.836966268 SUB_ID 1
## 625 62.768087353 62.768087353 62.768087353 SUB_ID 24
## 626 4.392033633 4.392033633 4.392033633 FRACSCORE 24
## 627 2.969376347 2.969376347 2.969376347 NOPRIORFRACxAGE_STDZ 24
## 628 2.873720793 2.873720793 2.873720793 BONEMED_FU 24
## 629 2.714712111 2.714712111 2.714712111 WEIGHT 24
## 630 2.074876786 2.074876786 2.074876786 HEIGHT 24
## 631 1.977399772 1.977399772 1.977399772 BMI 24
## 632 1.790136357 1.790136357 1.790136357 AGE_STDZ 24
## 633 1.610943511 1.610943511 1.610943511 PRIORFRAC 24
## 634 1.595901584 1.595901584 1.595901584 BONETREAT 24
## 635 1.497184676 1.497184676 1.497184676 AGE 24
## 636 1.399781958 1.399781958 1.399781958 BONEMED 24
## 637 1.376657247 1.376657247 1.376657247 ARMASSIST 24
## 638 0.922449006 0.922449006 0.922449006 SITE_ID 24
## 639 0.607839131 0.607839131 0.607839131 RATERISK_num 24
## 640 0.599062199 0.599062199 0.599062199 PRIORFRACxAGE_STDZ 24
## 641 0.491198405 0.491198405 0.491198405 RATERISK 24
## 642 0.489936179 0.489936179 0.489936179 PHY_ID 24
## 643 0.038547897 0.038547897 0.038547897 MOMFRAC 24
## 644 -0.358162020 -0.358162020 -0.358162020 SMOKE 24
## 645 -0.773815768 -0.773815768 -0.773815768 RATERISK_EQ_3 24
## 646 -0.908691878 -0.908691878 -0.908691878 AGExPRIORFRAC 24
## 647 -1.034619993 -1.034619993 -1.034619993 MOMFRACxARMASSIST 24
## 648 -1.569112051 -1.569112051 -1.569112051 PREMENO 24
## 649 62.768087353 62.768087353 62.768087353 SUB_ID 5
## 650 4.392033633 4.392033633 4.392033633 FRACSCORE 5
## 651 2.969376347 2.969376347 2.969376347 NOPRIORFRACxAGE_STDZ 5
## 652 2.873720793 2.873720793 2.873720793 BONEMED_FU 5
## 653 2.714712111 2.714712111 2.714712111 WEIGHT 5
## 654 62.768087353 62.768087353 62.768087353 SUB_ID 4
## 655 4.392033633 4.392033633 4.392033633 FRACSCORE 4
## 656 2.969376347 2.969376347 2.969376347 NOPRIORFRACxAGE_STDZ 4
## 657 2.873720793 2.873720793 2.873720793 BONEMED_FU 4
## 658 62.768087353 62.768087353 62.768087353 SUB_ID 3
## 659 4.392033633 4.392033633 4.392033633 FRACSCORE 3
## 660 2.969376347 2.969376347 2.969376347 NOPRIORFRACxAGE_STDZ 3
## 661 62.768087353 62.768087353 62.768087353 SUB_ID 2
## 662 4.392033633 4.392033633 4.392033633 FRACSCORE 2
## 663 62.768087353 62.768087353 62.768087353 SUB_ID 1
## 664 61.637041973 61.637041973 61.637041973 SUB_ID 24
## 665 4.289607000 4.289607000 4.289607000 BONEMED_FU 24
## 666 3.955910779 3.955910779 3.955910779 FRACSCORE 24
## 667 3.929465585 3.929465585 3.929465585 BONETREAT 24
## 668 3.801161947 3.801161947 3.801161947 BMI 24
## 669 3.236188743 3.236188743 3.236188743 NOPRIORFRACxAGE_STDZ 24
## 670 2.815793695 2.815793695 2.815793695 AGE_STDZ 24
## 671 2.812823325 2.812823325 2.812823325 HEIGHT 24
## 672 2.750229651 2.750229651 2.750229651 WEIGHT 24
## 673 2.736029009 2.736029009 2.736029009 AGE 24
## 674 2.149736278 2.149736278 2.149736278 BONEMED 24
## 675 1.471576006 1.471576006 1.471576006 SITE_ID 24
## 676 1.393906637 1.393906637 1.393906637 SMOKE 24
## 677 1.366210669 1.366210669 1.366210669 PRIORFRAC 24
## 678 0.991084953 0.991084953 0.991084953 PRIORFRACxAGE_STDZ 24
## 679 0.738197707 0.738197707 0.738197707 RATERISK 24
## 680 0.642944667 0.642944667 0.642944667 AGExPRIORFRAC 24
## 681 0.502848710 0.502848710 0.502848710 RATERISK_EQ_3 24
## 682 0.459690974 0.459690974 0.459690974 ARMASSIST 24
## 683 0.409536618 0.409536618 0.409536618 MOMFRAC 24
## 684 0.095538930 0.095538930 0.095538930 RATERISK_num 24
## 685 0.057898195 0.057898195 0.057898195 PREMENO 24
## 686 -0.584921076 -0.584921076 -0.584921076 MOMFRACxARMASSIST 24
## 687 -0.621150310 -0.621150310 -0.621150310 PHY_ID 24
## 688 61.637041973 61.637041973 61.637041973 SUB_ID 5
## 689 4.289607000 4.289607000 4.289607000 BONEMED_FU 5
## 690 3.955910779 3.955910779 3.955910779 FRACSCORE 5
## 691 3.929465585 3.929465585 3.929465585 BONETREAT 5
## 692 3.801161947 3.801161947 3.801161947 BMI 5
## 693 61.637041973 61.637041973 61.637041973 SUB_ID 4
## 694 4.289607000 4.289607000 4.289607000 BONEMED_FU 4
## 695 3.955910779 3.955910779 3.955910779 FRACSCORE 4
## 696 3.929465585 3.929465585 3.929465585 BONETREAT 4
## 697 61.637041973 61.637041973 61.637041973 SUB_ID 3
## 698 4.289607000 4.289607000 4.289607000 BONEMED_FU 3
## 699 3.955910779 3.955910779 3.955910779 FRACSCORE 3
## 700 61.637041973 61.637041973 61.637041973 SUB_ID 2
## 701 4.289607000 4.289607000 4.289607000 BONEMED_FU 2
## 702 61.637041973 61.637041973 61.637041973 SUB_ID 1
## 703 63.193650370 63.193650370 63.193650370 SUB_ID 24
## 704 4.938527912 4.938527912 4.938527912 FRACSCORE 24
## 705 3.648092153 3.648092153 3.648092153 BMI 24
## 706 3.183048385 3.183048385 3.183048385 WEIGHT 24
## 707 2.910138810 2.910138810 2.910138810 AGE 24
## 708 2.767198614 2.767198614 2.767198614 NOPRIORFRACxAGE_STDZ 24
## 709 2.653604848 2.653604848 2.653604848 BONEMED_FU 24
## 710 2.363347929 2.363347929 2.363347929 HEIGHT 24
## 711 2.078012481 2.078012481 2.078012481 AGE_STDZ 24
## 712 1.788684941 1.788684941 1.788684941 BONEMED 24
## 713 1.242430424 1.242430424 1.242430424 RATERISK_num 24
## 714 0.988857044 0.988857044 0.988857044 ARMASSIST 24
## 715 0.972544504 0.972544504 0.972544504 BONETREAT 24
## 716 0.895067383 0.895067383 0.895067383 PRIORFRAC 24
## 717 0.825504318 0.825504318 0.825504318 PHY_ID 24
## 718 0.825187784 0.825187784 0.825187784 SITE_ID 24
## 719 0.685401382 0.685401382 0.685401382 SMOKE 24
## 720 0.568172008 0.568172008 0.568172008 AGExPRIORFRAC 24
## 721 0.431120711 0.431120711 0.431120711 RATERISK_EQ_3 24
## 722 0.263615674 0.263615674 0.263615674 PRIORFRACxAGE_STDZ 24
## 723 -0.326791154 -0.326791154 -0.326791154 MOMFRAC 24
## 724 -0.384299820 -0.384299820 -0.384299820 RATERISK 24
## 725 -0.479556220 -0.479556220 -0.479556220 MOMFRACxARMASSIST 24
## 726 -0.724873688 -0.724873688 -0.724873688 PREMENO 24
## 727 63.193650370 63.193650370 63.193650370 SUB_ID 5
## 728 4.938527912 4.938527912 4.938527912 FRACSCORE 5
## 729 3.648092153 3.648092153 3.648092153 BMI 5
## 730 3.183048385 3.183048385 3.183048385 WEIGHT 5
## 731 2.910138810 2.910138810 2.910138810 AGE 5
## 732 63.193650370 63.193650370 63.193650370 SUB_ID 4
## 733 4.938527912 4.938527912 4.938527912 FRACSCORE 4
## 734 3.648092153 3.648092153 3.648092153 BMI 4
## 735 3.183048385 3.183048385 3.183048385 WEIGHT 4
## 736 63.193650370 63.193650370 63.193650370 SUB_ID 3
## 737 4.938527912 4.938527912 4.938527912 FRACSCORE 3
## 738 3.648092153 3.648092153 3.648092153 BMI 3
## 739 63.193650370 63.193650370 63.193650370 SUB_ID 2
## 740 4.938527912 4.938527912 4.938527912 FRACSCORE 2
## 741 63.193650370 63.193650370 63.193650370 SUB_ID 1
## 742 63.969346754 63.969346754 63.969346754 SUB_ID 24
## 743 3.827840745 3.827840745 3.827840745 NOPRIORFRACxAGE_STDZ 24
## 744 3.413200251 3.413200251 3.413200251 FRACSCORE 24
## 745 3.227579160 3.227579160 3.227579160 BONEMED_FU 24
## 746 3.052294702 3.052294702 3.052294702 WEIGHT 24
## 747 2.833825644 2.833825644 2.833825644 HEIGHT 24
## 748 2.570195661 2.570195661 2.570195661 BMI 24
## 749 2.222476634 2.222476634 2.222476634 AGE 24
## 750 2.082988214 2.082988214 2.082988214 AGE_STDZ 24
## 751 1.781788271 1.781788271 1.781788271 RATERISK_num 24
## 752 1.641499219 1.641499219 1.641499219 BONETREAT 24
## 753 0.971476226 0.971476226 0.971476226 BONEMED 24
## 754 0.807867761 0.807867761 0.807867761 PRIORFRAC 24
## 755 0.741410789 0.741410789 0.741410789 PHY_ID 24
## 756 0.693002130 0.693002130 0.693002130 RATERISK 24
## 757 0.354461115 0.354461115 0.354461115 ARMASSIST 24
## 758 0.063050432 0.063050432 0.063050432 RATERISK_EQ_3 24
## 759 -0.226073464 -0.226073464 -0.226073464 PRIORFRACxAGE_STDZ 24
## 760 -0.283965965 -0.283965965 -0.283965965 SMOKE 24
## 761 -0.584181889 -0.584181889 -0.584181889 AGExPRIORFRAC 24
## 762 -0.685094131 -0.685094131 -0.685094131 PREMENO 24
## 763 -0.729537953 -0.729537953 -0.729537953 SITE_ID 24
## 764 -0.761415878 -0.761415878 -0.761415878 MOMFRAC 24
## 765 -1.325545732 -1.325545732 -1.325545732 MOMFRACxARMASSIST 24
## 766 63.969346754 63.969346754 63.969346754 SUB_ID 5
## 767 3.827840745 3.827840745 3.827840745 NOPRIORFRACxAGE_STDZ 5
## 768 3.413200251 3.413200251 3.413200251 FRACSCORE 5
## 769 3.227579160 3.227579160 3.227579160 BONEMED_FU 5
## 770 3.052294702 3.052294702 3.052294702 WEIGHT 5
## 771 63.969346754 63.969346754 63.969346754 SUB_ID 4
## 772 3.827840745 3.827840745 3.827840745 NOPRIORFRACxAGE_STDZ 4
## 773 3.413200251 3.413200251 3.413200251 FRACSCORE 4
## 774 3.227579160 3.227579160 3.227579160 BONEMED_FU 4
## 775 63.969346754 63.969346754 63.969346754 SUB_ID 3
## 776 3.827840745 3.827840745 3.827840745 NOPRIORFRACxAGE_STDZ 3
## 777 3.413200251 3.413200251 3.413200251 FRACSCORE 3
## 778 63.969346754 63.969346754 63.969346754 SUB_ID 2
## 779 3.827840745 3.827840745 3.827840745 NOPRIORFRACxAGE_STDZ 2
## 780 63.969346754 63.969346754 63.969346754 SUB_ID 1
## 781 64.228225800 64.228225800 64.228225800 SUB_ID 24
## 782 5.525078425 5.525078425 5.525078425 FRACSCORE 24
## 783 3.938222750 3.938222750 3.938222750 BMI 24
## 784 3.001561844 3.001561844 3.001561844 NOPRIORFRACxAGE_STDZ 24
## 785 2.665556378 2.665556378 2.665556378 RATERISK_num 24
## 786 2.590263526 2.590263526 2.590263526 HEIGHT 24
## 787 2.553609504 2.553609504 2.553609504 PRIORFRAC 24
## 788 1.887558643 1.887558643 1.887558643 BONEMED_FU 24
## 789 1.771231063 1.771231063 1.771231063 BONEMED 24
## 790 1.667303288 1.667303288 1.667303288 AGE_STDZ 24
## 791 1.324229082 1.324229082 1.324229082 WEIGHT 24
## 792 1.146777409 1.146777409 1.146777409 PHY_ID 24
## 793 1.134635364 1.134635364 1.134635364 ARMASSIST 24
## 794 1.115537114 1.115537114 1.115537114 BONETREAT 24
## 795 0.732377135 0.732377135 0.732377135 AGE 24
## 796 0.675940754 0.675940754 0.675940754 MOMFRAC 24
## 797 0.611064020 0.611064020 0.611064020 PREMENO 24
## 798 0.494200853 0.494200853 0.494200853 SITE_ID 24
## 799 0.434065649 0.434065649 0.434065649 PRIORFRACxAGE_STDZ 24
## 800 0.418243893 0.418243893 0.418243893 AGExPRIORFRAC 24
## 801 -0.182317741 -0.182317741 -0.182317741 RATERISK_EQ_3 24
## 802 -0.240958537 -0.240958537 -0.240958537 RATERISK 24
## 803 -0.340789940 -0.340789940 -0.340789940 MOMFRACxARMASSIST 24
## 804 -0.386247303 -0.386247303 -0.386247303 SMOKE 24
## 805 64.228225800 64.228225800 64.228225800 SUB_ID 5
## 806 5.525078425 5.525078425 5.525078425 FRACSCORE 5
## 807 3.938222750 3.938222750 3.938222750 BMI 5
## 808 3.001561844 3.001561844 3.001561844 NOPRIORFRACxAGE_STDZ 5
## 809 2.665556378 2.665556378 2.665556378 RATERISK_num 5
## 810 64.228225800 64.228225800 64.228225800 SUB_ID 4
## 811 5.525078425 5.525078425 5.525078425 FRACSCORE 4
## 812 3.938222750 3.938222750 3.938222750 BMI 4
## 813 3.001561844 3.001561844 3.001561844 NOPRIORFRACxAGE_STDZ 4
## 814 64.228225800 64.228225800 64.228225800 SUB_ID 3
## 815 5.525078425 5.525078425 5.525078425 FRACSCORE 3
## 816 3.938222750 3.938222750 3.938222750 BMI 3
## 817 64.228225800 64.228225800 64.228225800 SUB_ID 2
## 818 5.525078425 5.525078425 5.525078425 FRACSCORE 2
## 819 64.228225800 64.228225800 64.228225800 SUB_ID 1
## 820 61.307427980 61.307427980 61.307427980 SUB_ID 24
## 821 3.642943357 3.642943357 3.642943357 FRACSCORE 24
## 822 3.126485013 3.126485013 3.126485013 BONEMED_FU 24
## 823 2.968825203 2.968825203 2.968825203 AGE_STDZ 24
## 824 2.874093586 2.874093586 2.874093586 HEIGHT 24
## 825 2.751623932 2.751623932 2.751623932 NOPRIORFRACxAGE_STDZ 24
## 826 2.541647021 2.541647021 2.541647021 AGE 24
## 827 2.412517368 2.412517368 2.412517368 BONEMED 24
## 828 2.382723668 2.382723668 2.382723668 BMI 24
## 829 1.501166318 1.501166318 1.501166318 WEIGHT 24
## 830 1.354938946 1.354938946 1.354938946 PRIORFRAC 24
## 831 1.034382768 1.034382768 1.034382768 BONETREAT 24
## 832 0.984719757 0.984719757 0.984719757 SMOKE 24
## 833 0.846379830 0.846379830 0.846379830 AGExPRIORFRAC 24
## 834 0.803589005 0.803589005 0.803589005 PHY_ID 24
## 835 0.567817494 0.567817494 0.567817494 PRIORFRACxAGE_STDZ 24
## 836 0.524666141 0.524666141 0.524666141 PREMENO 24
## 837 0.249961390 0.249961390 0.249961390 MOMFRAC 24
## 838 -0.132595519 -0.132595519 -0.132595519 RATERISK 24
## 839 -0.253184144 -0.253184144 -0.253184144 RATERISK_EQ_3 24
## 840 -0.351390760 -0.351390760 -0.351390760 RATERISK_num 24
## 841 -0.431431631 -0.431431631 -0.431431631 SITE_ID 24
## 842 -0.561764525 -0.561764525 -0.561764525 ARMASSIST 24
## 843 -0.835087793 -0.835087793 -0.835087793 MOMFRACxARMASSIST 24
## 844 61.307427980 61.307427980 61.307427980 SUB_ID 5
## 845 3.642943357 3.642943357 3.642943357 FRACSCORE 5
## 846 3.126485013 3.126485013 3.126485013 BONEMED_FU 5
## 847 2.968825203 2.968825203 2.968825203 AGE_STDZ 5
## 848 2.874093586 2.874093586 2.874093586 HEIGHT 5
## 849 61.307427980 61.307427980 61.307427980 SUB_ID 4
## 850 3.642943357 3.642943357 3.642943357 FRACSCORE 4
## 851 3.126485013 3.126485013 3.126485013 BONEMED_FU 4
## 852 2.968825203 2.968825203 2.968825203 AGE_STDZ 4
## 853 61.307427980 61.307427980 61.307427980 SUB_ID 3
## 854 3.642943357 3.642943357 3.642943357 FRACSCORE 3
## 855 3.126485013 3.126485013 3.126485013 BONEMED_FU 3
## 856 61.307427980 61.307427980 61.307427980 SUB_ID 2
## 857 3.642943357 3.642943357 3.642943357 FRACSCORE 2
## 858 61.307427980 61.307427980 61.307427980 SUB_ID 1
## 859 60.323847503 60.323847503 60.323847503 SUB_ID 24
## 860 3.466126238 3.466126238 3.466126238 BONEMED_FU 24
## 861 3.423574779 3.423574779 3.423574779 FRACSCORE 24
## 862 3.421472614 3.421472614 3.421472614 BMI 24
## 863 3.375888412 3.375888412 3.375888412 WEIGHT 24
## 864 3.256215572 3.256215572 3.256215572 NOPRIORFRACxAGE_STDZ 24
## 865 2.903038225 2.903038225 2.903038225 AGE_STDZ 24
## 866 2.376497255 2.376497255 2.376497255 BONETREAT 24
## 867 2.202838263 2.202838263 2.202838263 RATERISK_num 24
## 868 2.085185661 2.085185661 2.085185661 AGE 24
## 869 2.052636345 2.052636345 2.052636345 PRIORFRAC 24
## 870 1.905856711 1.905856711 1.905856711 HEIGHT 24
## 871 1.519571794 1.519571794 1.519571794 PHY_ID 24
## 872 1.433213031 1.433213031 1.433213031 BONEMED 24
## 873 0.987520108 0.987520108 0.987520108 PRIORFRACxAGE_STDZ 24
## 874 0.973546005 0.973546005 0.973546005 MOMFRAC 24
## 875 0.844255657 0.844255657 0.844255657 RATERISK_EQ_3 24
## 876 0.827735240 0.827735240 0.827735240 ARMASSIST 24
## 877 0.591095320 0.591095320 0.591095320 SMOKE 24
## 878 0.458263659 0.458263659 0.458263659 SITE_ID 24
## 879 0.307486130 0.307486130 0.307486130 PREMENO 24
## 880 0.204900091 0.204900091 0.204900091 RATERISK 24
## 881 0.122298557 0.122298557 0.122298557 AGExPRIORFRAC 24
## 882 -0.664210149 -0.664210149 -0.664210149 MOMFRACxARMASSIST 24
## 883 60.323847503 60.323847503 60.323847503 SUB_ID 5
## 884 3.466126238 3.466126238 3.466126238 BONEMED_FU 5
## 885 3.423574779 3.423574779 3.423574779 FRACSCORE 5
## 886 3.421472614 3.421472614 3.421472614 BMI 5
## 887 3.375888412 3.375888412 3.375888412 WEIGHT 5
## 888 60.323847503 60.323847503 60.323847503 SUB_ID 4
## 889 3.466126238 3.466126238 3.466126238 BONEMED_FU 4
## 890 3.423574779 3.423574779 3.423574779 FRACSCORE 4
## 891 3.421472614 3.421472614 3.421472614 BMI 4
## 892 60.323847503 60.323847503 60.323847503 SUB_ID 3
## 893 3.466126238 3.466126238 3.466126238 BONEMED_FU 3
## 894 3.423574779 3.423574779 3.423574779 FRACSCORE 3
## 895 60.323847503 60.323847503 60.323847503 SUB_ID 2
## 896 3.466126238 3.466126238 3.466126238 BONEMED_FU 2
## 897 60.323847503 60.323847503 60.323847503 SUB_ID 1
## 898 60.815726096 60.815726096 60.815726096 SUB_ID 24
## 899 4.287209684 4.287209684 4.287209684 FRACSCORE 24
## 900 3.887636328 3.887636328 3.887636328 NOPRIORFRACxAGE_STDZ 24
## 901 3.606033041 3.606033041 3.606033041 HEIGHT 24
## 902 3.335665160 3.335665160 3.335665160 BMI 24
## 903 2.862675215 2.862675215 2.862675215 AGE_STDZ 24
## 904 2.575494058 2.575494058 2.575494058 BONEMED_FU 24
## 905 1.868641134 1.868641134 1.868641134 AGE 24
## 906 1.841438340 1.841438340 1.841438340 BONETREAT 24
## 907 1.745937546 1.745937546 1.745937546 WEIGHT 24
## 908 1.704967598 1.704967598 1.704967598 BONEMED 24
## 909 1.229801652 1.229801652 1.229801652 MOMFRAC 24
## 910 1.190051921 1.190051921 1.190051921 PRIORFRACxAGE_STDZ 24
## 911 1.185570275 1.185570275 1.185570275 SITE_ID 24
## 912 1.006419143 1.006419143 1.006419143 PRIORFRAC 24
## 913 0.937148934 0.937148934 0.937148934 SMOKE 24
## 914 0.834648084 0.834648084 0.834648084 RATERISK_num 24
## 915 0.774439646 0.774439646 0.774439646 PHY_ID 24
## 916 0.614112358 0.614112358 0.614112358 ARMASSIST 24
## 917 0.588522057 0.588522057 0.588522057 RATERISK 24
## 918 0.023986647 0.023986647 0.023986647 RATERISK_EQ_3 24
## 919 -0.003766571 -0.003766571 -0.003766571 AGExPRIORFRAC 24
## 920 -0.090651820 -0.090651820 -0.090651820 MOMFRACxARMASSIST 24
## 921 -1.349552661 -1.349552661 -1.349552661 PREMENO 24
## 922 60.815726096 60.815726096 60.815726096 SUB_ID 5
## 923 4.287209684 4.287209684 4.287209684 FRACSCORE 5
## 924 3.887636328 3.887636328 3.887636328 NOPRIORFRACxAGE_STDZ 5
## 925 3.606033041 3.606033041 3.606033041 HEIGHT 5
## 926 3.335665160 3.335665160 3.335665160 BMI 5
## 927 60.815726096 60.815726096 60.815726096 SUB_ID 4
## 928 4.287209684 4.287209684 4.287209684 FRACSCORE 4
## 929 3.887636328 3.887636328 3.887636328 NOPRIORFRACxAGE_STDZ 4
## 930 3.606033041 3.606033041 3.606033041 HEIGHT 4
## 931 60.815726096 60.815726096 60.815726096 SUB_ID 3
## 932 4.287209684 4.287209684 4.287209684 FRACSCORE 3
## 933 3.887636328 3.887636328 3.887636328 NOPRIORFRACxAGE_STDZ 3
## 934 60.815726096 60.815726096 60.815726096 SUB_ID 2
## 935 4.287209684 4.287209684 4.287209684 FRACSCORE 2
## 936 60.815726096 60.815726096 60.815726096 SUB_ID 1
## 937 63.612120606 63.612120606 63.612120606 SUB_ID 24
## 938 4.743125553 4.743125553 4.743125553 FRACSCORE 24
## 939 3.388125136 3.388125136 3.388125136 NOPRIORFRACxAGE_STDZ 24
## 940 3.204744970 3.204744970 3.204744970 AGE 24
## 941 2.779856850 2.779856850 2.779856850 AGE_STDZ 24
## 942 2.771065412 2.771065412 2.771065412 BONEMED_FU 24
## 943 2.709942178 2.709942178 2.709942178 WEIGHT 24
## 944 2.404400250 2.404400250 2.404400250 BONEMED 24
## 945 2.078438539 2.078438539 2.078438539 HEIGHT 24
## 946 1.899811666 1.899811666 1.899811666 BMI 24
## 947 1.860686772 1.860686772 1.860686772 RATERISK_EQ_3 24
## 948 1.744652958 1.744652958 1.744652958 PHY_ID 24
## 949 1.724770478 1.724770478 1.724770478 BONETREAT 24
## 950 1.285613878 1.285613878 1.285613878 MOMFRAC 24
## 951 1.259197689 1.259197689 1.259197689 SITE_ID 24
## 952 1.121420427 1.121420427 1.121420427 ARMASSIST 24
## 953 0.986469565 0.986469565 0.986469565 PRIORFRAC 24
## 954 0.114619429 0.114619429 0.114619429 SMOKE 24
## 955 -0.324091283 -0.324091283 -0.324091283 RATERISK 24
## 956 -0.331681022 -0.331681022 -0.331681022 RATERISK_num 24
## 957 -0.392449127 -0.392449127 -0.392449127 AGExPRIORFRAC 24
## 958 -0.423207961 -0.423207961 -0.423207961 MOMFRACxARMASSIST 24
## 959 -0.551531048 -0.551531048 -0.551531048 PRIORFRACxAGE_STDZ 24
## 960 -0.764610467 -0.764610467 -0.764610467 PREMENO 24
## 961 63.612120606 63.612120606 63.612120606 SUB_ID 5
## 962 4.743125553 4.743125553 4.743125553 FRACSCORE 5
## 963 3.388125136 3.388125136 3.388125136 NOPRIORFRACxAGE_STDZ 5
## 964 3.204744970 3.204744970 3.204744970 AGE 5
## 965 2.779856850 2.779856850 2.779856850 AGE_STDZ 5
## 966 63.612120606 63.612120606 63.612120606 SUB_ID 4
## 967 4.743125553 4.743125553 4.743125553 FRACSCORE 4
## 968 3.388125136 3.388125136 3.388125136 NOPRIORFRACxAGE_STDZ 4
## 969 3.204744970 3.204744970 3.204744970 AGE 4
## 970 63.612120606 63.612120606 63.612120606 SUB_ID 3
## 971 4.743125553 4.743125553 4.743125553 FRACSCORE 3
## 972 3.388125136 3.388125136 3.388125136 NOPRIORFRACxAGE_STDZ 3
## 973 63.612120606 63.612120606 63.612120606 SUB_ID 2
## 974 4.743125553 4.743125553 4.743125553 FRACSCORE 2
## 975 63.612120606 63.612120606 63.612120606 SUB_ID 1
## 976 65.444909586 65.444909586 65.444909586 SUB_ID 24
## 977 4.695182795 4.695182795 4.695182795 FRACSCORE 24
## 978 3.698515375 3.698515375 3.698515375 BONEMED_FU 24
## 979 3.289307564 3.289307564 3.289307564 BMI 24
## 980 3.225766030 3.225766030 3.225766030 NOPRIORFRACxAGE_STDZ 24
## 981 3.041191268 3.041191268 3.041191268 AGE 24
## 982 2.855214593 2.855214593 2.855214593 WEIGHT 24
## 983 2.672899615 2.672899615 2.672899615 HEIGHT 24
## 984 2.507206098 2.507206098 2.507206098 BONETREAT 24
## 985 1.849269621 1.849269621 1.849269621 AGE_STDZ 24
## 986 1.703480889 1.703480889 1.703480889 BONEMED 24
## 987 1.693851555 1.693851555 1.693851555 RATERISK_num 24
## 988 1.323798647 1.323798647 1.323798647 SITE_ID 24
## 989 1.206719519 1.206719519 1.206719519 PHY_ID 24
## 990 1.080871138 1.080871138 1.080871138 RATERISK_EQ_3 24
## 991 0.602390158 0.602390158 0.602390158 ARMASSIST 24
## 992 0.428024000 0.428024000 0.428024000 RATERISK 24
## 993 0.361021197 0.361021197 0.361021197 SMOKE 24
## 994 0.357075571 0.357075571 0.357075571 PRIORFRAC 24
## 995 0.313944704 0.313944704 0.313944704 MOMFRAC 24
## 996 -0.103371749 -0.103371749 -0.103371749 PRIORFRACxAGE_STDZ 24
## 997 -0.198426307 -0.198426307 -0.198426307 AGExPRIORFRAC 24
## 998 -0.457117212 -0.457117212 -0.457117212 PREMENO 24
## 999 -0.655533958 -0.655533958 -0.655533958 MOMFRACxARMASSIST 24
## 1000 65.444909586 65.444909586 65.444909586 SUB_ID 5
## 1001 4.695182795 4.695182795 4.695182795 FRACSCORE 5
## 1002 3.698515375 3.698515375 3.698515375 BONEMED_FU 5
## 1003 3.289307564 3.289307564 3.289307564 BMI 5
## 1004 3.225766030 3.225766030 3.225766030 NOPRIORFRACxAGE_STDZ 5
## 1005 65.444909586 65.444909586 65.444909586 SUB_ID 4
## 1006 4.695182795 4.695182795 4.695182795 FRACSCORE 4
## 1007 3.698515375 3.698515375 3.698515375 BONEMED_FU 4
## 1008 3.289307564 3.289307564 3.289307564 BMI 4
## 1009 65.444909586 65.444909586 65.444909586 SUB_ID 3
## 1010 4.695182795 4.695182795 4.695182795 FRACSCORE 3
## 1011 3.698515375 3.698515375 3.698515375 BONEMED_FU 3
## 1012 65.444909586 65.444909586 65.444909586 SUB_ID 2
## 1013 4.695182795 4.695182795 4.695182795 FRACSCORE 2
## 1014 65.444909586 65.444909586 65.444909586 SUB_ID 1
## 1015 61.128452940 61.128452940 61.128452940 SUB_ID 24
## 1016 4.657541081 4.657541081 4.657541081 BONEMED_FU 24
## 1017 3.733984885 3.733984885 3.733984885 BMI 24
## 1018 3.361193979 3.361193979 3.361193979 NOPRIORFRACxAGE_STDZ 24
## 1019 3.107358397 3.107358397 3.107358397 AGE 24
## 1020 3.073149578 3.073149578 3.073149578 BONEMED 24
## 1021 2.995318466 2.995318466 2.995318466 FRACSCORE 24
## 1022 2.731954966 2.731954966 2.731954966 AGE_STDZ 24
## 1023 2.510869040 2.510869040 2.510869040 WEIGHT 24
## 1024 2.211497716 2.211497716 2.211497716 PRIORFRAC 24
## 1025 2.201886706 2.201886706 2.201886706 BONETREAT 24
## 1026 2.044044539 2.044044539 2.044044539 HEIGHT 24
## 1027 2.000006538 2.000006538 2.000006538 SMOKE 24
## 1028 1.372533339 1.372533339 1.372533339 SITE_ID 24
## 1029 0.931997714 0.931997714 0.931997714 PRIORFRACxAGE_STDZ 24
## 1030 0.819162351 0.819162351 0.819162351 RATERISK 24
## 1031 0.413672576 0.413672576 0.413672576 RATERISK_EQ_3 24
## 1032 0.234416314 0.234416314 0.234416314 RATERISK_num 24
## 1033 0.150925944 0.150925944 0.150925944 MOMFRACxARMASSIST 24
## 1034 0.133282338 0.133282338 0.133282338 PHY_ID 24
## 1035 0.129845490 0.129845490 0.129845490 ARMASSIST 24
## 1036 -0.106999292 -0.106999292 -0.106999292 PREMENO 24
## 1037 -0.364679547 -0.364679547 -0.364679547 AGExPRIORFRAC 24
## 1038 -0.486542739 -0.486542739 -0.486542739 MOMFRAC 24
## 1039 61.128452940 61.128452940 61.128452940 SUB_ID 5
## 1040 4.657541081 4.657541081 4.657541081 BONEMED_FU 5
## 1041 3.733984885 3.733984885 3.733984885 BMI 5
## 1042 3.361193979 3.361193979 3.361193979 NOPRIORFRACxAGE_STDZ 5
## 1043 3.107358397 3.107358397 3.107358397 AGE 5
## 1044 61.128452940 61.128452940 61.128452940 SUB_ID 4
## 1045 4.657541081 4.657541081 4.657541081 BONEMED_FU 4
## 1046 3.733984885 3.733984885 3.733984885 BMI 4
## 1047 3.361193979 3.361193979 3.361193979 NOPRIORFRACxAGE_STDZ 4
## 1048 61.128452940 61.128452940 61.128452940 SUB_ID 3
## 1049 4.657541081 4.657541081 4.657541081 BONEMED_FU 3
## 1050 3.733984885 3.733984885 3.733984885 BMI 3
## 1051 61.128452940 61.128452940 61.128452940 SUB_ID 2
## 1052 4.657541081 4.657541081 4.657541081 BONEMED_FU 2
## 1053 61.128452940 61.128452940 61.128452940 SUB_ID 1
## 1054 59.444222591 59.444222591 59.444222591 SUB_ID 24
## 1055 4.249751531 4.249751531 4.249751531 FRACSCORE 24
## 1056 3.525528568 3.525528568 3.525528568 BONEMED 24
## 1057 3.402935150 3.402935150 3.402935150 BONEMED_FU 24
## 1058 3.402663045 3.402663045 3.402663045 NOPRIORFRACxAGE_STDZ 24
## 1059 3.332105480 3.332105480 3.332105480 BMI 24
## 1060 3.176056236 3.176056236 3.176056236 AGE 24
## 1061 2.758103675 2.758103675 2.758103675 HEIGHT 24
## 1062 2.628452139 2.628452139 2.628452139 AGE_STDZ 24
## 1063 2.402132948 2.402132948 2.402132948 BONETREAT 24
## 1064 2.278345526 2.278345526 2.278345526 PRIORFRAC 24
## 1065 1.823281385 1.823281385 1.823281385 WEIGHT 24
## 1066 1.001404497 1.001404497 1.001404497 AGExPRIORFRAC 24
## 1067 0.966089080 0.966089080 0.966089080 RATERISK_EQ_3 24
## 1068 0.941571366 0.941571366 0.941571366 ARMASSIST 24
## 1069 0.661002400 0.661002400 0.661002400 SITE_ID 24
## 1070 0.392112090 0.392112090 0.392112090 RATERISK 24
## 1071 0.350081499 0.350081499 0.350081499 PRIORFRACxAGE_STDZ 24
## 1072 0.229847025 0.229847025 0.229847025 PHY_ID 24
## 1073 0.058253129 0.058253129 0.058253129 SMOKE 24
## 1074 0.043808557 0.043808557 0.043808557 RATERISK_num 24
## 1075 -0.841763969 -0.841763969 -0.841763969 PREMENO 24
## 1076 -1.024278474 -1.024278474 -1.024278474 MOMFRAC 24
## 1077 -1.543059954 -1.543059954 -1.543059954 MOMFRACxARMASSIST 24
## 1078 59.444222591 59.444222591 59.444222591 SUB_ID 5
## 1079 4.249751531 4.249751531 4.249751531 FRACSCORE 5
## 1080 3.525528568 3.525528568 3.525528568 BONEMED 5
## 1081 3.402935150 3.402935150 3.402935150 BONEMED_FU 5
## 1082 3.402663045 3.402663045 3.402663045 NOPRIORFRACxAGE_STDZ 5
## 1083 59.444222591 59.444222591 59.444222591 SUB_ID 4
## 1084 4.249751531 4.249751531 4.249751531 FRACSCORE 4
## 1085 3.525528568 3.525528568 3.525528568 BONEMED 4
## 1086 3.402935150 3.402935150 3.402935150 BONEMED_FU 4
## 1087 59.444222591 59.444222591 59.444222591 SUB_ID 3
## 1088 4.249751531 4.249751531 4.249751531 FRACSCORE 3
## 1089 3.525528568 3.525528568 3.525528568 BONEMED 3
## 1090 59.444222591 59.444222591 59.444222591 SUB_ID 2
## 1091 4.249751531 4.249751531 4.249751531 FRACSCORE 2
## 1092 59.444222591 59.444222591 59.444222591 SUB_ID 1
## 1093 61.823900913 61.823900913 61.823900913 SUB_ID 24
## 1094 5.478907606 5.478907606 5.478907606 FRACSCORE 24
## 1095 3.365366939 3.365366939 3.365366939 WEIGHT 24
## 1096 3.113341275 3.113341275 3.113341275 NOPRIORFRACxAGE_STDZ 24
## 1097 2.984500476 2.984500476 2.984500476 BONEMED_FU 24
## 1098 2.893269364 2.893269364 2.893269364 AGE 24
## 1099 2.812089372 2.812089372 2.812089372 AGE_STDZ 24
## 1100 2.739196890 2.739196890 2.739196890 HEIGHT 24
## 1101 2.331895438 2.331895438 2.331895438 BMI 24
## 1102 2.184042295 2.184042295 2.184042295 RATERISK_num 24
## 1103 1.614971730 1.614971730 1.614971730 BONETREAT 24
## 1104 1.215158670 1.215158670 1.215158670 PRIORFRACxAGE_STDZ 24
## 1105 1.146944800 1.146944800 1.146944800 ARMASSIST 24
## 1106 1.109098483 1.109098483 1.109098483 PRIORFRAC 24
## 1107 0.980975758 0.980975758 0.980975758 BONEMED 24
## 1108 0.931928845 0.931928845 0.931928845 RATERISK_EQ_3 24
## 1109 0.677311498 0.677311498 0.677311498 SITE_ID 24
## 1110 0.613941646 0.613941646 0.613941646 MOMFRAC 24
## 1111 0.550488497 0.550488497 0.550488497 AGExPRIORFRAC 24
## 1112 0.226381777 0.226381777 0.226381777 SMOKE 24
## 1113 0.052500964 0.052500964 0.052500964 PHY_ID 24
## 1114 -0.277765503 -0.277765503 -0.277765503 RATERISK 24
## 1115 -0.759543953 -0.759543953 -0.759543953 MOMFRACxARMASSIST 24
## 1116 -1.037830064 -1.037830064 -1.037830064 PREMENO 24
## 1117 61.823900913 61.823900913 61.823900913 SUB_ID 5
## 1118 5.478907606 5.478907606 5.478907606 FRACSCORE 5
## 1119 3.365366939 3.365366939 3.365366939 WEIGHT 5
## 1120 3.113341275 3.113341275 3.113341275 NOPRIORFRACxAGE_STDZ 5
## 1121 2.984500476 2.984500476 2.984500476 BONEMED_FU 5
## 1122 61.823900913 61.823900913 61.823900913 SUB_ID 4
## 1123 5.478907606 5.478907606 5.478907606 FRACSCORE 4
## 1124 3.365366939 3.365366939 3.365366939 WEIGHT 4
## 1125 3.113341275 3.113341275 3.113341275 NOPRIORFRACxAGE_STDZ 4
## 1126 61.823900913 61.823900913 61.823900913 SUB_ID 3
## 1127 5.478907606 5.478907606 5.478907606 FRACSCORE 3
## 1128 3.365366939 3.365366939 3.365366939 WEIGHT 3
## 1129 61.823900913 61.823900913 61.823900913 SUB_ID 2
## 1130 5.478907606 5.478907606 5.478907606 FRACSCORE 2
## 1131 61.823900913 61.823900913 61.823900913 SUB_ID 1
## 1132 63.206866483 63.206866483 63.206866483 SUB_ID 24
## 1133 4.964597696 4.964597696 4.964597696 FRACSCORE 24
## 1134 3.156174035 3.156174035 3.156174035 NOPRIORFRACxAGE_STDZ 24
## 1135 2.923339648 2.923339648 2.923339648 BMI 24
## 1136 2.692662870 2.692662870 2.692662870 HEIGHT 24
## 1137 2.439681013 2.439681013 2.439681013 WEIGHT 24
## 1138 2.000634195 2.000634195 2.000634195 PRIORFRAC 24
## 1139 1.863039261 1.863039261 1.863039261 AGE_STDZ 24
## 1140 1.794044357 1.794044357 1.794044357 BONEMED_FU 24
## 1141 1.744703245 1.744703245 1.744703245 AGE 24
## 1142 1.489460437 1.489460437 1.489460437 PHY_ID 24
## 1143 1.369558901 1.369558901 1.369558901 ARMASSIST 24
## 1144 1.346729234 1.346729234 1.346729234 RATERISK 24
## 1145 0.902750565 0.902750565 0.902750565 AGExPRIORFRAC 24
## 1146 0.754316457 0.754316457 0.754316457 MOMFRACxARMASSIST 24
## 1147 0.574618561 0.574618561 0.574618561 RATERISK_num 24
## 1148 0.496669510 0.496669510 0.496669510 BONEMED 24
## 1149 0.160832701 0.160832701 0.160832701 RATERISK_EQ_3 24
## 1150 0.132458052 0.132458052 0.132458052 SITE_ID 24
## 1151 0.020754632 0.020754632 0.020754632 BONETREAT 24
## 1152 0.014856577 0.014856577 0.014856577 PREMENO 24
## 1153 -0.052335350 -0.052335350 -0.052335350 PRIORFRACxAGE_STDZ 24
## 1154 -0.158227393 -0.158227393 -0.158227393 SMOKE 24
## 1155 -0.541631056 -0.541631056 -0.541631056 MOMFRAC 24
## 1156 63.206866483 63.206866483 63.206866483 SUB_ID 5
## 1157 4.964597696 4.964597696 4.964597696 FRACSCORE 5
## 1158 3.156174035 3.156174035 3.156174035 NOPRIORFRACxAGE_STDZ 5
## 1159 2.923339648 2.923339648 2.923339648 BMI 5
## 1160 2.692662870 2.692662870 2.692662870 HEIGHT 5
## 1161 63.206866483 63.206866483 63.206866483 SUB_ID 4
## 1162 4.964597696 4.964597696 4.964597696 FRACSCORE 4
## 1163 3.156174035 3.156174035 3.156174035 NOPRIORFRACxAGE_STDZ 4
## 1164 2.923339648 2.923339648 2.923339648 BMI 4
## 1165 63.206866483 63.206866483 63.206866483 SUB_ID 3
## 1166 4.964597696 4.964597696 4.964597696 FRACSCORE 3
## 1167 3.156174035 3.156174035 3.156174035 NOPRIORFRACxAGE_STDZ 3
## 1168 63.206866483 63.206866483 63.206866483 SUB_ID 2
## 1169 4.964597696 4.964597696 4.964597696 FRACSCORE 2
## 1170 63.206866483 63.206866483 63.206866483 SUB_ID 1
## Resample
## 1 Fold01.Rep1
## 2 Fold01.Rep1
## 3 Fold01.Rep1
## 4 Fold01.Rep1
## 5 Fold01.Rep1
## 6 Fold01.Rep1
## 7 Fold01.Rep1
## 8 Fold01.Rep1
## 9 Fold01.Rep1
## 10 Fold01.Rep1
## 11 Fold01.Rep1
## 12 Fold01.Rep1
## 13 Fold01.Rep1
## 14 Fold01.Rep1
## 15 Fold01.Rep1
## 16 Fold01.Rep1
## 17 Fold01.Rep1
## 18 Fold01.Rep1
## 19 Fold01.Rep1
## 20 Fold01.Rep1
## 21 Fold01.Rep1
## 22 Fold01.Rep1
## 23 Fold01.Rep1
## 24 Fold01.Rep1
## 25 Fold01.Rep1
## 26 Fold01.Rep1
## 27 Fold01.Rep1
## 28 Fold01.Rep1
## 29 Fold01.Rep1
## 30 Fold01.Rep1
## 31 Fold01.Rep1
## 32 Fold01.Rep1
## 33 Fold01.Rep1
## 34 Fold01.Rep1
## 35 Fold01.Rep1
## 36 Fold01.Rep1
## 37 Fold01.Rep1
## 38 Fold01.Rep1
## 39 Fold01.Rep1
## 40 Fold02.Rep1
## 41 Fold02.Rep1
## 42 Fold02.Rep1
## 43 Fold02.Rep1
## 44 Fold02.Rep1
## 45 Fold02.Rep1
## 46 Fold02.Rep1
## 47 Fold02.Rep1
## 48 Fold02.Rep1
## 49 Fold02.Rep1
## 50 Fold02.Rep1
## 51 Fold02.Rep1
## 52 Fold02.Rep1
## 53 Fold02.Rep1
## 54 Fold02.Rep1
## 55 Fold02.Rep1
## 56 Fold02.Rep1
## 57 Fold02.Rep1
## 58 Fold02.Rep1
## 59 Fold02.Rep1
## 60 Fold02.Rep1
## 61 Fold02.Rep1
## 62 Fold02.Rep1
## 63 Fold02.Rep1
## 64 Fold02.Rep1
## 65 Fold02.Rep1
## 66 Fold02.Rep1
## 67 Fold02.Rep1
## 68 Fold02.Rep1
## 69 Fold02.Rep1
## 70 Fold02.Rep1
## 71 Fold02.Rep1
## 72 Fold02.Rep1
## 73 Fold02.Rep1
## 74 Fold02.Rep1
## 75 Fold02.Rep1
## 76 Fold02.Rep1
## 77 Fold02.Rep1
## 78 Fold02.Rep1
## 79 Fold03.Rep1
## 80 Fold03.Rep1
## 81 Fold03.Rep1
## 82 Fold03.Rep1
## 83 Fold03.Rep1
## 84 Fold03.Rep1
## 85 Fold03.Rep1
## 86 Fold03.Rep1
## 87 Fold03.Rep1
## 88 Fold03.Rep1
## 89 Fold03.Rep1
## 90 Fold03.Rep1
## 91 Fold03.Rep1
## 92 Fold03.Rep1
## 93 Fold03.Rep1
## 94 Fold03.Rep1
## 95 Fold03.Rep1
## 96 Fold03.Rep1
## 97 Fold03.Rep1
## 98 Fold03.Rep1
## 99 Fold03.Rep1
## 100 Fold03.Rep1
## 101 Fold03.Rep1
## 102 Fold03.Rep1
## 103 Fold03.Rep1
## 104 Fold03.Rep1
## 105 Fold03.Rep1
## 106 Fold03.Rep1
## 107 Fold03.Rep1
## 108 Fold03.Rep1
## 109 Fold03.Rep1
## 110 Fold03.Rep1
## 111 Fold03.Rep1
## 112 Fold03.Rep1
## 113 Fold03.Rep1
## 114 Fold03.Rep1
## 115 Fold03.Rep1
## 116 Fold03.Rep1
## 117 Fold03.Rep1
## 118 Fold04.Rep1
## 119 Fold04.Rep1
## 120 Fold04.Rep1
## 121 Fold04.Rep1
## 122 Fold04.Rep1
## 123 Fold04.Rep1
## 124 Fold04.Rep1
## 125 Fold04.Rep1
## 126 Fold04.Rep1
## 127 Fold04.Rep1
## 128 Fold04.Rep1
## 129 Fold04.Rep1
## 130 Fold04.Rep1
## 131 Fold04.Rep1
## 132 Fold04.Rep1
## 133 Fold04.Rep1
## 134 Fold04.Rep1
## 135 Fold04.Rep1
## 136 Fold04.Rep1
## 137 Fold04.Rep1
## 138 Fold04.Rep1
## 139 Fold04.Rep1
## 140 Fold04.Rep1
## 141 Fold04.Rep1
## 142 Fold04.Rep1
## 143 Fold04.Rep1
## 144 Fold04.Rep1
## 145 Fold04.Rep1
## 146 Fold04.Rep1
## 147 Fold04.Rep1
## 148 Fold04.Rep1
## 149 Fold04.Rep1
## 150 Fold04.Rep1
## 151 Fold04.Rep1
## 152 Fold04.Rep1
## 153 Fold04.Rep1
## 154 Fold04.Rep1
## 155 Fold04.Rep1
## 156 Fold04.Rep1
## 157 Fold05.Rep1
## 158 Fold05.Rep1
## 159 Fold05.Rep1
## 160 Fold05.Rep1
## 161 Fold05.Rep1
## 162 Fold05.Rep1
## 163 Fold05.Rep1
## 164 Fold05.Rep1
## 165 Fold05.Rep1
## 166 Fold05.Rep1
## 167 Fold05.Rep1
## 168 Fold05.Rep1
## 169 Fold05.Rep1
## 170 Fold05.Rep1
## 171 Fold05.Rep1
## 172 Fold05.Rep1
## 173 Fold05.Rep1
## 174 Fold05.Rep1
## 175 Fold05.Rep1
## 176 Fold05.Rep1
## 177 Fold05.Rep1
## 178 Fold05.Rep1
## 179 Fold05.Rep1
## 180 Fold05.Rep1
## 181 Fold05.Rep1
## 182 Fold05.Rep1
## 183 Fold05.Rep1
## 184 Fold05.Rep1
## 185 Fold05.Rep1
## 186 Fold05.Rep1
## 187 Fold05.Rep1
## 188 Fold05.Rep1
## 189 Fold05.Rep1
## 190 Fold05.Rep1
## 191 Fold05.Rep1
## 192 Fold05.Rep1
## 193 Fold05.Rep1
## 194 Fold05.Rep1
## 195 Fold05.Rep1
## 196 Fold06.Rep1
## 197 Fold06.Rep1
## 198 Fold06.Rep1
## 199 Fold06.Rep1
## 200 Fold06.Rep1
## 201 Fold06.Rep1
## 202 Fold06.Rep1
## 203 Fold06.Rep1
## 204 Fold06.Rep1
## 205 Fold06.Rep1
## 206 Fold06.Rep1
## 207 Fold06.Rep1
## 208 Fold06.Rep1
## 209 Fold06.Rep1
## 210 Fold06.Rep1
## 211 Fold06.Rep1
## 212 Fold06.Rep1
## 213 Fold06.Rep1
## 214 Fold06.Rep1
## 215 Fold06.Rep1
## 216 Fold06.Rep1
## 217 Fold06.Rep1
## 218 Fold06.Rep1
## 219 Fold06.Rep1
## 220 Fold06.Rep1
## 221 Fold06.Rep1
## 222 Fold06.Rep1
## 223 Fold06.Rep1
## 224 Fold06.Rep1
## 225 Fold06.Rep1
## 226 Fold06.Rep1
## 227 Fold06.Rep1
## 228 Fold06.Rep1
## 229 Fold06.Rep1
## 230 Fold06.Rep1
## 231 Fold06.Rep1
## 232 Fold06.Rep1
## 233 Fold06.Rep1
## 234 Fold06.Rep1
## 235 Fold07.Rep1
## 236 Fold07.Rep1
## 237 Fold07.Rep1
## 238 Fold07.Rep1
## 239 Fold07.Rep1
## 240 Fold07.Rep1
## 241 Fold07.Rep1
## 242 Fold07.Rep1
## 243 Fold07.Rep1
## 244 Fold07.Rep1
## 245 Fold07.Rep1
## 246 Fold07.Rep1
## 247 Fold07.Rep1
## 248 Fold07.Rep1
## 249 Fold07.Rep1
## 250 Fold07.Rep1
## 251 Fold07.Rep1
## 252 Fold07.Rep1
## 253 Fold07.Rep1
## 254 Fold07.Rep1
## 255 Fold07.Rep1
## 256 Fold07.Rep1
## 257 Fold07.Rep1
## 258 Fold07.Rep1
## 259 Fold07.Rep1
## 260 Fold07.Rep1
## 261 Fold07.Rep1
## 262 Fold07.Rep1
## 263 Fold07.Rep1
## 264 Fold07.Rep1
## 265 Fold07.Rep1
## 266 Fold07.Rep1
## 267 Fold07.Rep1
## 268 Fold07.Rep1
## 269 Fold07.Rep1
## 270 Fold07.Rep1
## 271 Fold07.Rep1
## 272 Fold07.Rep1
## 273 Fold07.Rep1
## 274 Fold08.Rep1
## 275 Fold08.Rep1
## 276 Fold08.Rep1
## 277 Fold08.Rep1
## 278 Fold08.Rep1
## 279 Fold08.Rep1
## 280 Fold08.Rep1
## 281 Fold08.Rep1
## 282 Fold08.Rep1
## 283 Fold08.Rep1
## 284 Fold08.Rep1
## 285 Fold08.Rep1
## 286 Fold08.Rep1
## 287 Fold08.Rep1
## 288 Fold08.Rep1
## 289 Fold08.Rep1
## 290 Fold08.Rep1
## 291 Fold08.Rep1
## 292 Fold08.Rep1
## 293 Fold08.Rep1
## 294 Fold08.Rep1
## 295 Fold08.Rep1
## 296 Fold08.Rep1
## 297 Fold08.Rep1
## 298 Fold08.Rep1
## 299 Fold08.Rep1
## 300 Fold08.Rep1
## 301 Fold08.Rep1
## 302 Fold08.Rep1
## 303 Fold08.Rep1
## 304 Fold08.Rep1
## 305 Fold08.Rep1
## 306 Fold08.Rep1
## 307 Fold08.Rep1
## 308 Fold08.Rep1
## 309 Fold08.Rep1
## 310 Fold08.Rep1
## 311 Fold08.Rep1
## 312 Fold08.Rep1
## 313 Fold09.Rep1
## 314 Fold09.Rep1
## 315 Fold09.Rep1
## 316 Fold09.Rep1
## 317 Fold09.Rep1
## 318 Fold09.Rep1
## 319 Fold09.Rep1
## 320 Fold09.Rep1
## 321 Fold09.Rep1
## 322 Fold09.Rep1
## 323 Fold09.Rep1
## 324 Fold09.Rep1
## 325 Fold09.Rep1
## 326 Fold09.Rep1
## 327 Fold09.Rep1
## 328 Fold09.Rep1
## 329 Fold09.Rep1
## 330 Fold09.Rep1
## 331 Fold09.Rep1
## 332 Fold09.Rep1
## 333 Fold09.Rep1
## 334 Fold09.Rep1
## 335 Fold09.Rep1
## 336 Fold09.Rep1
## 337 Fold09.Rep1
## 338 Fold09.Rep1
## 339 Fold09.Rep1
## 340 Fold09.Rep1
## 341 Fold09.Rep1
## 342 Fold09.Rep1
## 343 Fold09.Rep1
## 344 Fold09.Rep1
## 345 Fold09.Rep1
## 346 Fold09.Rep1
## 347 Fold09.Rep1
## 348 Fold09.Rep1
## 349 Fold09.Rep1
## 350 Fold09.Rep1
## 351 Fold09.Rep1
## 352 Fold10.Rep1
## 353 Fold10.Rep1
## 354 Fold10.Rep1
## 355 Fold10.Rep1
## 356 Fold10.Rep1
## 357 Fold10.Rep1
## 358 Fold10.Rep1
## 359 Fold10.Rep1
## 360 Fold10.Rep1
## 361 Fold10.Rep1
## 362 Fold10.Rep1
## 363 Fold10.Rep1
## 364 Fold10.Rep1
## 365 Fold10.Rep1
## 366 Fold10.Rep1
## 367 Fold10.Rep1
## 368 Fold10.Rep1
## 369 Fold10.Rep1
## 370 Fold10.Rep1
## 371 Fold10.Rep1
## 372 Fold10.Rep1
## 373 Fold10.Rep1
## 374 Fold10.Rep1
## 375 Fold10.Rep1
## 376 Fold10.Rep1
## 377 Fold10.Rep1
## 378 Fold10.Rep1
## 379 Fold10.Rep1
## 380 Fold10.Rep1
## 381 Fold10.Rep1
## 382 Fold10.Rep1
## 383 Fold10.Rep1
## 384 Fold10.Rep1
## 385 Fold10.Rep1
## 386 Fold10.Rep1
## 387 Fold10.Rep1
## 388 Fold10.Rep1
## 389 Fold10.Rep1
## 390 Fold10.Rep1
## 391 Fold01.Rep2
## 392 Fold01.Rep2
## 393 Fold01.Rep2
## 394 Fold01.Rep2
## 395 Fold01.Rep2
## 396 Fold01.Rep2
## 397 Fold01.Rep2
## 398 Fold01.Rep2
## 399 Fold01.Rep2
## 400 Fold01.Rep2
## 401 Fold01.Rep2
## 402 Fold01.Rep2
## 403 Fold01.Rep2
## 404 Fold01.Rep2
## 405 Fold01.Rep2
## 406 Fold01.Rep2
## 407 Fold01.Rep2
## 408 Fold01.Rep2
## 409 Fold01.Rep2
## 410 Fold01.Rep2
## 411 Fold01.Rep2
## 412 Fold01.Rep2
## 413 Fold01.Rep2
## 414 Fold01.Rep2
## 415 Fold01.Rep2
## 416 Fold01.Rep2
## 417 Fold01.Rep2
## 418 Fold01.Rep2
## 419 Fold01.Rep2
## 420 Fold01.Rep2
## 421 Fold01.Rep2
## 422 Fold01.Rep2
## 423 Fold01.Rep2
## 424 Fold01.Rep2
## 425 Fold01.Rep2
## 426 Fold01.Rep2
## 427 Fold01.Rep2
## 428 Fold01.Rep2
## 429 Fold01.Rep2
## 430 Fold02.Rep2
## 431 Fold02.Rep2
## 432 Fold02.Rep2
## 433 Fold02.Rep2
## 434 Fold02.Rep2
## 435 Fold02.Rep2
## 436 Fold02.Rep2
## 437 Fold02.Rep2
## 438 Fold02.Rep2
## 439 Fold02.Rep2
## 440 Fold02.Rep2
## 441 Fold02.Rep2
## 442 Fold02.Rep2
## 443 Fold02.Rep2
## 444 Fold02.Rep2
## 445 Fold02.Rep2
## 446 Fold02.Rep2
## 447 Fold02.Rep2
## 448 Fold02.Rep2
## 449 Fold02.Rep2
## 450 Fold02.Rep2
## 451 Fold02.Rep2
## 452 Fold02.Rep2
## 453 Fold02.Rep2
## 454 Fold02.Rep2
## 455 Fold02.Rep2
## 456 Fold02.Rep2
## 457 Fold02.Rep2
## 458 Fold02.Rep2
## 459 Fold02.Rep2
## 460 Fold02.Rep2
## 461 Fold02.Rep2
## 462 Fold02.Rep2
## 463 Fold02.Rep2
## 464 Fold02.Rep2
## 465 Fold02.Rep2
## 466 Fold02.Rep2
## 467 Fold02.Rep2
## 468 Fold02.Rep2
## 469 Fold03.Rep2
## 470 Fold03.Rep2
## 471 Fold03.Rep2
## 472 Fold03.Rep2
## 473 Fold03.Rep2
## 474 Fold03.Rep2
## 475 Fold03.Rep2
## 476 Fold03.Rep2
## 477 Fold03.Rep2
## 478 Fold03.Rep2
## 479 Fold03.Rep2
## 480 Fold03.Rep2
## 481 Fold03.Rep2
## 482 Fold03.Rep2
## 483 Fold03.Rep2
## 484 Fold03.Rep2
## 485 Fold03.Rep2
## 486 Fold03.Rep2
## 487 Fold03.Rep2
## 488 Fold03.Rep2
## 489 Fold03.Rep2
## 490 Fold03.Rep2
## 491 Fold03.Rep2
## 492 Fold03.Rep2
## 493 Fold03.Rep2
## 494 Fold03.Rep2
## 495 Fold03.Rep2
## 496 Fold03.Rep2
## 497 Fold03.Rep2
## 498 Fold03.Rep2
## 499 Fold03.Rep2
## 500 Fold03.Rep2
## 501 Fold03.Rep2
## 502 Fold03.Rep2
## 503 Fold03.Rep2
## 504 Fold03.Rep2
## 505 Fold03.Rep2
## 506 Fold03.Rep2
## 507 Fold03.Rep2
## 508 Fold04.Rep2
## 509 Fold04.Rep2
## 510 Fold04.Rep2
## 511 Fold04.Rep2
## 512 Fold04.Rep2
## 513 Fold04.Rep2
## 514 Fold04.Rep2
## 515 Fold04.Rep2
## 516 Fold04.Rep2
## 517 Fold04.Rep2
## 518 Fold04.Rep2
## 519 Fold04.Rep2
## 520 Fold04.Rep2
## 521 Fold04.Rep2
## 522 Fold04.Rep2
## 523 Fold04.Rep2
## 524 Fold04.Rep2
## 525 Fold04.Rep2
## 526 Fold04.Rep2
## 527 Fold04.Rep2
## 528 Fold04.Rep2
## 529 Fold04.Rep2
## 530 Fold04.Rep2
## 531 Fold04.Rep2
## 532 Fold04.Rep2
## 533 Fold04.Rep2
## 534 Fold04.Rep2
## 535 Fold04.Rep2
## 536 Fold04.Rep2
## 537 Fold04.Rep2
## 538 Fold04.Rep2
## 539 Fold04.Rep2
## 540 Fold04.Rep2
## 541 Fold04.Rep2
## 542 Fold04.Rep2
## 543 Fold04.Rep2
## 544 Fold04.Rep2
## 545 Fold04.Rep2
## 546 Fold04.Rep2
## 547 Fold05.Rep2
## 548 Fold05.Rep2
## 549 Fold05.Rep2
## 550 Fold05.Rep2
## 551 Fold05.Rep2
## 552 Fold05.Rep2
## 553 Fold05.Rep2
## 554 Fold05.Rep2
## 555 Fold05.Rep2
## 556 Fold05.Rep2
## 557 Fold05.Rep2
## 558 Fold05.Rep2
## 559 Fold05.Rep2
## 560 Fold05.Rep2
## 561 Fold05.Rep2
## 562 Fold05.Rep2
## 563 Fold05.Rep2
## 564 Fold05.Rep2
## 565 Fold05.Rep2
## 566 Fold05.Rep2
## 567 Fold05.Rep2
## 568 Fold05.Rep2
## 569 Fold05.Rep2
## 570 Fold05.Rep2
## 571 Fold05.Rep2
## 572 Fold05.Rep2
## 573 Fold05.Rep2
## 574 Fold05.Rep2
## 575 Fold05.Rep2
## 576 Fold05.Rep2
## 577 Fold05.Rep2
## 578 Fold05.Rep2
## 579 Fold05.Rep2
## 580 Fold05.Rep2
## 581 Fold05.Rep2
## 582 Fold05.Rep2
## 583 Fold05.Rep2
## 584 Fold05.Rep2
## 585 Fold05.Rep2
## 586 Fold06.Rep2
## 587 Fold06.Rep2
## 588 Fold06.Rep2
## 589 Fold06.Rep2
## 590 Fold06.Rep2
## 591 Fold06.Rep2
## 592 Fold06.Rep2
## 593 Fold06.Rep2
## 594 Fold06.Rep2
## 595 Fold06.Rep2
## 596 Fold06.Rep2
## 597 Fold06.Rep2
## 598 Fold06.Rep2
## 599 Fold06.Rep2
## 600 Fold06.Rep2
## 601 Fold06.Rep2
## 602 Fold06.Rep2
## 603 Fold06.Rep2
## 604 Fold06.Rep2
## 605 Fold06.Rep2
## 606 Fold06.Rep2
## 607 Fold06.Rep2
## 608 Fold06.Rep2
## 609 Fold06.Rep2
## 610 Fold06.Rep2
## 611 Fold06.Rep2
## 612 Fold06.Rep2
## 613 Fold06.Rep2
## 614 Fold06.Rep2
## 615 Fold06.Rep2
## 616 Fold06.Rep2
## 617 Fold06.Rep2
## 618 Fold06.Rep2
## 619 Fold06.Rep2
## 620 Fold06.Rep2
## 621 Fold06.Rep2
## 622 Fold06.Rep2
## 623 Fold06.Rep2
## 624 Fold06.Rep2
## 625 Fold07.Rep2
## 626 Fold07.Rep2
## 627 Fold07.Rep2
## 628 Fold07.Rep2
## 629 Fold07.Rep2
## 630 Fold07.Rep2
## 631 Fold07.Rep2
## 632 Fold07.Rep2
## 633 Fold07.Rep2
## 634 Fold07.Rep2
## 635 Fold07.Rep2
## 636 Fold07.Rep2
## 637 Fold07.Rep2
## 638 Fold07.Rep2
## 639 Fold07.Rep2
## 640 Fold07.Rep2
## 641 Fold07.Rep2
## 642 Fold07.Rep2
## 643 Fold07.Rep2
## 644 Fold07.Rep2
## 645 Fold07.Rep2
## 646 Fold07.Rep2
## 647 Fold07.Rep2
## 648 Fold07.Rep2
## 649 Fold07.Rep2
## 650 Fold07.Rep2
## 651 Fold07.Rep2
## 652 Fold07.Rep2
## 653 Fold07.Rep2
## 654 Fold07.Rep2
## 655 Fold07.Rep2
## 656 Fold07.Rep2
## 657 Fold07.Rep2
## 658 Fold07.Rep2
## 659 Fold07.Rep2
## 660 Fold07.Rep2
## 661 Fold07.Rep2
## 662 Fold07.Rep2
## 663 Fold07.Rep2
## 664 Fold08.Rep2
## 665 Fold08.Rep2
## 666 Fold08.Rep2
## 667 Fold08.Rep2
## 668 Fold08.Rep2
## 669 Fold08.Rep2
## 670 Fold08.Rep2
## 671 Fold08.Rep2
## 672 Fold08.Rep2
## 673 Fold08.Rep2
## 674 Fold08.Rep2
## 675 Fold08.Rep2
## 676 Fold08.Rep2
## 677 Fold08.Rep2
## 678 Fold08.Rep2
## 679 Fold08.Rep2
## 680 Fold08.Rep2
## 681 Fold08.Rep2
## 682 Fold08.Rep2
## 683 Fold08.Rep2
## 684 Fold08.Rep2
## 685 Fold08.Rep2
## 686 Fold08.Rep2
## 687 Fold08.Rep2
## 688 Fold08.Rep2
## 689 Fold08.Rep2
## 690 Fold08.Rep2
## 691 Fold08.Rep2
## 692 Fold08.Rep2
## 693 Fold08.Rep2
## 694 Fold08.Rep2
## 695 Fold08.Rep2
## 696 Fold08.Rep2
## 697 Fold08.Rep2
## 698 Fold08.Rep2
## 699 Fold08.Rep2
## 700 Fold08.Rep2
## 701 Fold08.Rep2
## 702 Fold08.Rep2
## 703 Fold09.Rep2
## 704 Fold09.Rep2
## 705 Fold09.Rep2
## 706 Fold09.Rep2
## 707 Fold09.Rep2
## 708 Fold09.Rep2
## 709 Fold09.Rep2
## 710 Fold09.Rep2
## 711 Fold09.Rep2
## 712 Fold09.Rep2
## 713 Fold09.Rep2
## 714 Fold09.Rep2
## 715 Fold09.Rep2
## 716 Fold09.Rep2
## 717 Fold09.Rep2
## 718 Fold09.Rep2
## 719 Fold09.Rep2
## 720 Fold09.Rep2
## 721 Fold09.Rep2
## 722 Fold09.Rep2
## 723 Fold09.Rep2
## 724 Fold09.Rep2
## 725 Fold09.Rep2
## 726 Fold09.Rep2
## 727 Fold09.Rep2
## 728 Fold09.Rep2
## 729 Fold09.Rep2
## 730 Fold09.Rep2
## 731 Fold09.Rep2
## 732 Fold09.Rep2
## 733 Fold09.Rep2
## 734 Fold09.Rep2
## 735 Fold09.Rep2
## 736 Fold09.Rep2
## 737 Fold09.Rep2
## 738 Fold09.Rep2
## 739 Fold09.Rep2
## 740 Fold09.Rep2
## 741 Fold09.Rep2
## 742 Fold10.Rep2
## 743 Fold10.Rep2
## 744 Fold10.Rep2
## 745 Fold10.Rep2
## 746 Fold10.Rep2
## 747 Fold10.Rep2
## 748 Fold10.Rep2
## 749 Fold10.Rep2
## 750 Fold10.Rep2
## 751 Fold10.Rep2
## 752 Fold10.Rep2
## 753 Fold10.Rep2
## 754 Fold10.Rep2
## 755 Fold10.Rep2
## 756 Fold10.Rep2
## 757 Fold10.Rep2
## 758 Fold10.Rep2
## 759 Fold10.Rep2
## 760 Fold10.Rep2
## 761 Fold10.Rep2
## 762 Fold10.Rep2
## 763 Fold10.Rep2
## 764 Fold10.Rep2
## 765 Fold10.Rep2
## 766 Fold10.Rep2
## 767 Fold10.Rep2
## 768 Fold10.Rep2
## 769 Fold10.Rep2
## 770 Fold10.Rep2
## 771 Fold10.Rep2
## 772 Fold10.Rep2
## 773 Fold10.Rep2
## 774 Fold10.Rep2
## 775 Fold10.Rep2
## 776 Fold10.Rep2
## 777 Fold10.Rep2
## 778 Fold10.Rep2
## 779 Fold10.Rep2
## 780 Fold10.Rep2
## 781 Fold01.Rep3
## 782 Fold01.Rep3
## 783 Fold01.Rep3
## 784 Fold01.Rep3
## 785 Fold01.Rep3
## 786 Fold01.Rep3
## 787 Fold01.Rep3
## 788 Fold01.Rep3
## 789 Fold01.Rep3
## 790 Fold01.Rep3
## 791 Fold01.Rep3
## 792 Fold01.Rep3
## 793 Fold01.Rep3
## 794 Fold01.Rep3
## 795 Fold01.Rep3
## 796 Fold01.Rep3
## 797 Fold01.Rep3
## 798 Fold01.Rep3
## 799 Fold01.Rep3
## 800 Fold01.Rep3
## 801 Fold01.Rep3
## 802 Fold01.Rep3
## 803 Fold01.Rep3
## 804 Fold01.Rep3
## 805 Fold01.Rep3
## 806 Fold01.Rep3
## 807 Fold01.Rep3
## 808 Fold01.Rep3
## 809 Fold01.Rep3
## 810 Fold01.Rep3
## 811 Fold01.Rep3
## 812 Fold01.Rep3
## 813 Fold01.Rep3
## 814 Fold01.Rep3
## 815 Fold01.Rep3
## 816 Fold01.Rep3
## 817 Fold01.Rep3
## 818 Fold01.Rep3
## 819 Fold01.Rep3
## 820 Fold02.Rep3
## 821 Fold02.Rep3
## 822 Fold02.Rep3
## 823 Fold02.Rep3
## 824 Fold02.Rep3
## 825 Fold02.Rep3
## 826 Fold02.Rep3
## 827 Fold02.Rep3
## 828 Fold02.Rep3
## 829 Fold02.Rep3
## 830 Fold02.Rep3
## 831 Fold02.Rep3
## 832 Fold02.Rep3
## 833 Fold02.Rep3
## 834 Fold02.Rep3
## 835 Fold02.Rep3
## 836 Fold02.Rep3
## 837 Fold02.Rep3
## 838 Fold02.Rep3
## 839 Fold02.Rep3
## 840 Fold02.Rep3
## 841 Fold02.Rep3
## 842 Fold02.Rep3
## 843 Fold02.Rep3
## 844 Fold02.Rep3
## 845 Fold02.Rep3
## 846 Fold02.Rep3
## 847 Fold02.Rep3
## 848 Fold02.Rep3
## 849 Fold02.Rep3
## 850 Fold02.Rep3
## 851 Fold02.Rep3
## 852 Fold02.Rep3
## 853 Fold02.Rep3
## 854 Fold02.Rep3
## 855 Fold02.Rep3
## 856 Fold02.Rep3
## 857 Fold02.Rep3
## 858 Fold02.Rep3
## 859 Fold03.Rep3
## 860 Fold03.Rep3
## 861 Fold03.Rep3
## 862 Fold03.Rep3
## 863 Fold03.Rep3
## 864 Fold03.Rep3
## 865 Fold03.Rep3
## 866 Fold03.Rep3
## 867 Fold03.Rep3
## 868 Fold03.Rep3
## 869 Fold03.Rep3
## 870 Fold03.Rep3
## 871 Fold03.Rep3
## 872 Fold03.Rep3
## 873 Fold03.Rep3
## 874 Fold03.Rep3
## 875 Fold03.Rep3
## 876 Fold03.Rep3
## 877 Fold03.Rep3
## 878 Fold03.Rep3
## 879 Fold03.Rep3
## 880 Fold03.Rep3
## 881 Fold03.Rep3
## 882 Fold03.Rep3
## 883 Fold03.Rep3
## 884 Fold03.Rep3
## 885 Fold03.Rep3
## 886 Fold03.Rep3
## 887 Fold03.Rep3
## 888 Fold03.Rep3
## 889 Fold03.Rep3
## 890 Fold03.Rep3
## 891 Fold03.Rep3
## 892 Fold03.Rep3
## 893 Fold03.Rep3
## 894 Fold03.Rep3
## 895 Fold03.Rep3
## 896 Fold03.Rep3
## 897 Fold03.Rep3
## 898 Fold04.Rep3
## 899 Fold04.Rep3
## 900 Fold04.Rep3
## 901 Fold04.Rep3
## 902 Fold04.Rep3
## 903 Fold04.Rep3
## 904 Fold04.Rep3
## 905 Fold04.Rep3
## 906 Fold04.Rep3
## 907 Fold04.Rep3
## 908 Fold04.Rep3
## 909 Fold04.Rep3
## 910 Fold04.Rep3
## 911 Fold04.Rep3
## 912 Fold04.Rep3
## 913 Fold04.Rep3
## 914 Fold04.Rep3
## 915 Fold04.Rep3
## 916 Fold04.Rep3
## 917 Fold04.Rep3
## 918 Fold04.Rep3
## 919 Fold04.Rep3
## 920 Fold04.Rep3
## 921 Fold04.Rep3
## 922 Fold04.Rep3
## 923 Fold04.Rep3
## 924 Fold04.Rep3
## 925 Fold04.Rep3
## 926 Fold04.Rep3
## 927 Fold04.Rep3
## 928 Fold04.Rep3
## 929 Fold04.Rep3
## 930 Fold04.Rep3
## 931 Fold04.Rep3
## 932 Fold04.Rep3
## 933 Fold04.Rep3
## 934 Fold04.Rep3
## 935 Fold04.Rep3
## 936 Fold04.Rep3
## 937 Fold05.Rep3
## 938 Fold05.Rep3
## 939 Fold05.Rep3
## 940 Fold05.Rep3
## 941 Fold05.Rep3
## 942 Fold05.Rep3
## 943 Fold05.Rep3
## 944 Fold05.Rep3
## 945 Fold05.Rep3
## 946 Fold05.Rep3
## 947 Fold05.Rep3
## 948 Fold05.Rep3
## 949 Fold05.Rep3
## 950 Fold05.Rep3
## 951 Fold05.Rep3
## 952 Fold05.Rep3
## 953 Fold05.Rep3
## 954 Fold05.Rep3
## 955 Fold05.Rep3
## 956 Fold05.Rep3
## 957 Fold05.Rep3
## 958 Fold05.Rep3
## 959 Fold05.Rep3
## 960 Fold05.Rep3
## 961 Fold05.Rep3
## 962 Fold05.Rep3
## 963 Fold05.Rep3
## 964 Fold05.Rep3
## 965 Fold05.Rep3
## 966 Fold05.Rep3
## 967 Fold05.Rep3
## 968 Fold05.Rep3
## 969 Fold05.Rep3
## 970 Fold05.Rep3
## 971 Fold05.Rep3
## 972 Fold05.Rep3
## 973 Fold05.Rep3
## 974 Fold05.Rep3
## 975 Fold05.Rep3
## 976 Fold06.Rep3
## 977 Fold06.Rep3
## 978 Fold06.Rep3
## 979 Fold06.Rep3
## 980 Fold06.Rep3
## 981 Fold06.Rep3
## 982 Fold06.Rep3
## 983 Fold06.Rep3
## 984 Fold06.Rep3
## 985 Fold06.Rep3
## 986 Fold06.Rep3
## 987 Fold06.Rep3
## 988 Fold06.Rep3
## 989 Fold06.Rep3
## 990 Fold06.Rep3
## 991 Fold06.Rep3
## 992 Fold06.Rep3
## 993 Fold06.Rep3
## 994 Fold06.Rep3
## 995 Fold06.Rep3
## 996 Fold06.Rep3
## 997 Fold06.Rep3
## 998 Fold06.Rep3
## 999 Fold06.Rep3
## 1000 Fold06.Rep3
## 1001 Fold06.Rep3
## 1002 Fold06.Rep3
## 1003 Fold06.Rep3
## 1004 Fold06.Rep3
## 1005 Fold06.Rep3
## 1006 Fold06.Rep3
## 1007 Fold06.Rep3
## 1008 Fold06.Rep3
## 1009 Fold06.Rep3
## 1010 Fold06.Rep3
## 1011 Fold06.Rep3
## 1012 Fold06.Rep3
## 1013 Fold06.Rep3
## 1014 Fold06.Rep3
## 1015 Fold07.Rep3
## 1016 Fold07.Rep3
## 1017 Fold07.Rep3
## 1018 Fold07.Rep3
## 1019 Fold07.Rep3
## 1020 Fold07.Rep3
## 1021 Fold07.Rep3
## 1022 Fold07.Rep3
## 1023 Fold07.Rep3
## 1024 Fold07.Rep3
## 1025 Fold07.Rep3
## 1026 Fold07.Rep3
## 1027 Fold07.Rep3
## 1028 Fold07.Rep3
## 1029 Fold07.Rep3
## 1030 Fold07.Rep3
## 1031 Fold07.Rep3
## 1032 Fold07.Rep3
## 1033 Fold07.Rep3
## 1034 Fold07.Rep3
## 1035 Fold07.Rep3
## 1036 Fold07.Rep3
## 1037 Fold07.Rep3
## 1038 Fold07.Rep3
## 1039 Fold07.Rep3
## 1040 Fold07.Rep3
## 1041 Fold07.Rep3
## 1042 Fold07.Rep3
## 1043 Fold07.Rep3
## 1044 Fold07.Rep3
## 1045 Fold07.Rep3
## 1046 Fold07.Rep3
## 1047 Fold07.Rep3
## 1048 Fold07.Rep3
## 1049 Fold07.Rep3
## 1050 Fold07.Rep3
## 1051 Fold07.Rep3
## 1052 Fold07.Rep3
## 1053 Fold07.Rep3
## 1054 Fold08.Rep3
## 1055 Fold08.Rep3
## 1056 Fold08.Rep3
## 1057 Fold08.Rep3
## 1058 Fold08.Rep3
## 1059 Fold08.Rep3
## 1060 Fold08.Rep3
## 1061 Fold08.Rep3
## 1062 Fold08.Rep3
## 1063 Fold08.Rep3
## 1064 Fold08.Rep3
## 1065 Fold08.Rep3
## 1066 Fold08.Rep3
## 1067 Fold08.Rep3
## 1068 Fold08.Rep3
## 1069 Fold08.Rep3
## 1070 Fold08.Rep3
## 1071 Fold08.Rep3
## 1072 Fold08.Rep3
## 1073 Fold08.Rep3
## 1074 Fold08.Rep3
## 1075 Fold08.Rep3
## 1076 Fold08.Rep3
## 1077 Fold08.Rep3
## 1078 Fold08.Rep3
## 1079 Fold08.Rep3
## 1080 Fold08.Rep3
## 1081 Fold08.Rep3
## 1082 Fold08.Rep3
## 1083 Fold08.Rep3
## 1084 Fold08.Rep3
## 1085 Fold08.Rep3
## 1086 Fold08.Rep3
## 1087 Fold08.Rep3
## 1088 Fold08.Rep3
## 1089 Fold08.Rep3
## 1090 Fold08.Rep3
## 1091 Fold08.Rep3
## 1092 Fold08.Rep3
## 1093 Fold09.Rep3
## 1094 Fold09.Rep3
## 1095 Fold09.Rep3
## 1096 Fold09.Rep3
## 1097 Fold09.Rep3
## 1098 Fold09.Rep3
## 1099 Fold09.Rep3
## 1100 Fold09.Rep3
## 1101 Fold09.Rep3
## 1102 Fold09.Rep3
## 1103 Fold09.Rep3
## 1104 Fold09.Rep3
## 1105 Fold09.Rep3
## 1106 Fold09.Rep3
## 1107 Fold09.Rep3
## 1108 Fold09.Rep3
## 1109 Fold09.Rep3
## 1110 Fold09.Rep3
## 1111 Fold09.Rep3
## 1112 Fold09.Rep3
## 1113 Fold09.Rep3
## 1114 Fold09.Rep3
## 1115 Fold09.Rep3
## 1116 Fold09.Rep3
## 1117 Fold09.Rep3
## 1118 Fold09.Rep3
## 1119 Fold09.Rep3
## 1120 Fold09.Rep3
## 1121 Fold09.Rep3
## 1122 Fold09.Rep3
## 1123 Fold09.Rep3
## 1124 Fold09.Rep3
## 1125 Fold09.Rep3
## 1126 Fold09.Rep3
## 1127 Fold09.Rep3
## 1128 Fold09.Rep3
## 1129 Fold09.Rep3
## 1130 Fold09.Rep3
## 1131 Fold09.Rep3
## 1132 Fold10.Rep3
## 1133 Fold10.Rep3
## 1134 Fold10.Rep3
## 1135 Fold10.Rep3
## 1136 Fold10.Rep3
## 1137 Fold10.Rep3
## 1138 Fold10.Rep3
## 1139 Fold10.Rep3
## 1140 Fold10.Rep3
## 1141 Fold10.Rep3
## 1142 Fold10.Rep3
## 1143 Fold10.Rep3
## 1144 Fold10.Rep3
## 1145 Fold10.Rep3
## 1146 Fold10.Rep3
## 1147 Fold10.Rep3
## 1148 Fold10.Rep3
## 1149 Fold10.Rep3
## 1150 Fold10.Rep3
## 1151 Fold10.Rep3
## 1152 Fold10.Rep3
## 1153 Fold10.Rep3
## 1154 Fold10.Rep3
## 1155 Fold10.Rep3
## 1156 Fold10.Rep3
## 1157 Fold10.Rep3
## 1158 Fold10.Rep3
## 1159 Fold10.Rep3
## 1160 Fold10.Rep3
## 1161 Fold10.Rep3
## 1162 Fold10.Rep3
## 1163 Fold10.Rep3
## 1164 Fold10.Rep3
## 1165 Fold10.Rep3
## 1166 Fold10.Rep3
## 1167 Fold10.Rep3
## 1168 Fold10.Rep3
## 1169 Fold10.Rep3
## 1170 Fold10.Rep3# The optimal number of features determined by the RFE process is 5.
# The top 5 variables selected are FRACSCORE, WEIGHT, BMI, HEIGHT, and AGE_STDZxNOPRIOR# RANDOM FOREST
# Ensure FRACTURE is a factor if it's categorical
GLOW_data$FRACTURE <- as.factor(GLOW_data$FRACTURE)
# Build the random forest model
rf_model <- randomForest(FRACTURE ~ ., data=GLOW_data, ntree=500, importance=TRUE)
# Print the importance of each variable
print(importance(rf_model))## 0 1 MeanDecreaseAccuracy
## SUB_ID 62.6121879 65.715482904 67.08370002
## SITE_ID 2.4985095 -0.212001626 2.38049773
## PHY_ID 1.1306586 0.176230208 1.12326494
## PRIORFRAC 3.5586719 1.291933074 3.61322142
## AGE 4.5245942 0.341649995 4.31086931
## WEIGHT 5.9583739 -0.757895657 5.04256662
## HEIGHT 2.4831513 1.898654477 3.11310891
## BMI 8.4760980 -0.008161530 7.46166891
## PREMENO -1.2633360 0.325914944 -0.67325726
## MOMFRAC 0.0380801 -0.916736366 -0.44918254
## ARMASSIST 1.8319557 -0.082007382 1.48800959
## SMOKE 0.5829811 -1.077956978 -0.02888994
## RATERISK 1.0425834 1.995687899 2.01243508
## FRACSCORE 6.8392742 3.494309715 7.46335355
## BONEMED 4.8554062 -2.010361346 4.13443523
## BONEMED_FU 6.0979212 0.273828229 5.97286350
## BONETREAT 4.8335809 -0.509607481 4.52005198
## RATERISK_EQ_3 2.4538964 0.137860152 2.20026871
## RATERISK_num -0.8066306 2.090266963 0.69317951
## AGE_STDZ 5.6714141 -0.295374932 4.88828644
## AGExPRIORFRAC 0.6799395 0.962445643 1.06704087
## MOMFRACxARMASSIST 0.9806671 0.005158267 0.87793266
## PRIORFRACxAGE_STDZ 0.8544333 -1.799052771 -0.40288044
## NOPRIORFRACxAGE_STDZ 4.8100556 2.189911868 5.37778120
## MeanDecreaseGini
## SUB_ID 122.7330419
## SITE_ID 2.4933762
## PHY_ID 5.6309217
## PRIORFRAC 1.7654977
## AGE 4.5232876
## WEIGHT 5.1066637
## HEIGHT 6.0252334
## BMI 6.4377834
## PREMENO 0.7015662
## MOMFRAC 1.2943913
## ARMASSIST 1.2210545
## SMOKE 0.4642600
## RATERISK 1.5981293
## FRACSCORE 5.1039134
## BONEMED 0.9710159
## BONEMED_FU 1.6896958
## BONETREAT 0.8693418
## RATERISK_EQ_3 0.9241034
## RATERISK_num 1.5116333
## AGE_STDZ 4.6902943
## AGExPRIORFRAC 2.6647241
## MOMFRACxARMASSIST 0.3369723
## PRIORFRACxAGE_STDZ 2.8592081
## NOPRIORFRACxAGE_STDZ 4.8683982# Plot variable importance
varImpPlot(rf_model)
# RANDOM FOREST
# Ensure FRACTURE is a factor if it's categorical
GLOW_data$FRACTURE <- as.factor(GLOW_data$FRACTURE)
# Build the random forest model
rf_model <- randomForest(FRACTURE ~ ., data=GLOW_data, ntree=500, importance=TRUE)
# Print the importance of each variable
print(importance(rf_model))## 0 1 MeanDecreaseAccuracy
## SUB_ID 60.16663720 67.4610174 65.34765401
## SITE_ID 1.30569171 -1.6626086 0.11624490
## PHY_ID 2.89723929 -1.8107504 1.56372457
## PRIORFRAC 3.05775685 -0.8151036 2.04340106
## AGE 6.10992332 0.1221043 5.43553008
## WEIGHT 5.64673538 -1.5585010 4.36418179
## HEIGHT 1.63782194 0.7700751 1.67873246
## BMI 8.18352552 -0.7434904 7.34504388
## PREMENO -0.78279524 -0.3512526 -0.82418082
## MOMFRAC 0.82064084 -0.4710652 0.44331323
## ARMASSIST 2.65380873 -0.5908957 1.85284397
## SMOKE 1.22911986 0.3556638 1.23176302
## RATERISK -0.08129908 0.7318728 0.29254444
## FRACSCORE 5.38718149 4.0868331 7.00321557
## BONEMED 6.10597708 0.3166537 5.46981026
## BONEMED_FU 5.63340861 0.7353121 5.36272155
## BONETREAT 3.06225394 -1.0757157 2.13524203
## RATERISK_EQ_3 2.00763766 0.5269715 2.09121189
## RATERISK_num -0.92198682 1.7590908 0.52276713
## AGE_STDZ 4.86638369 0.3492855 4.29208421
## AGExPRIORFRAC 3.27763723 -1.7006615 1.67031301
## MOMFRACxARMASSIST -0.41999782 0.8433497 0.04198686
## PRIORFRACxAGE_STDZ 1.63446093 -2.2570610 0.10056339
## NOPRIORFRACxAGE_STDZ 3.52678142 4.0267914 5.45518575
## MeanDecreaseGini
## SUB_ID 122.1622138
## SITE_ID 2.2593862
## PHY_ID 5.2316616
## PRIORFRAC 1.6909397
## AGE 4.5117954
## WEIGHT 5.4508460
## HEIGHT 5.8765275
## BMI 6.3249037
## PREMENO 0.7445076
## MOMFRAC 1.3813428
## ARMASSIST 1.0533234
## SMOKE 0.4489627
## RATERISK 1.6999218
## FRACSCORE 5.4248189
## BONEMED 0.9983017
## BONEMED_FU 1.7777501
## BONETREAT 0.8504035
## RATERISK_EQ_3 0.9447167
## RATERISK_num 1.5866790
## AGE_STDZ 4.5215574
## AGExPRIORFRAC 2.8864676
## MOMFRACxARMASSIST 0.4285930
## PRIORFRACxAGE_STDZ 2.8623326
## NOPRIORFRACxAGE_STDZ 4.5978523# Plot variable importance
varImpPlot(rf_model)
# Random Forest W SEED
GLOW_data$FRACTURE <- as.factor(GLOW_data$FRACTURE)
set.seed(123) # For reproducibility
rf_model <- randomForest(FRACTURE ~ ., data=GLOW_data, ntree=500, importance=TRUE)
importance(rf_model) # Shows importance score for each variable## 0 1 MeanDecreaseAccuracy
## SUB_ID 57.7788117 64.7201775 63.22439311
## SITE_ID 3.0265127 -1.2136723 2.13673684
## PHY_ID 1.5228799 -0.7425820 0.80350194
## PRIORFRAC 2.3205249 0.7059509 2.36535767
## AGE 5.1009957 0.5552545 4.89401905
## WEIGHT 6.2717122 -1.0568195 5.26060721
## HEIGHT 4.1288809 2.3824576 4.72345294
## BMI 7.2333438 -1.5298134 5.91426141
## PREMENO -0.9159542 0.7758452 -0.02627726
## MOMFRAC 1.9913930 -1.3713291 0.85247675
## ARMASSIST 1.1244073 -0.1765833 0.74740612
## SMOKE 0.5666124 -0.4811641 0.26609402
## RATERISK -1.2362910 1.4672427 -0.07975750
## FRACSCORE 6.1613400 2.0099281 6.82616729
## BONEMED 5.3839874 -0.9626865 4.82202599
## BONEMED_FU 5.0975261 1.8355321 5.11289900
## BONETREAT 2.6006868 1.2425631 2.81217249
## RATERISK_EQ_3 2.3659117 -1.5811427 1.07816317
## RATERISK_num 0.2026713 -1.4906767 -0.90332043
## AGE_STDZ 3.5561482 0.6940959 3.81292352
## AGExPRIORFRAC 3.1599722 -0.8063485 2.26311639
## MOMFRACxARMASSIST -1.4565590 -0.2946979 -1.26243862
## PRIORFRACxAGE_STDZ 2.3032139 -1.7357001 1.07800326
## NOPRIORFRACxAGE_STDZ 4.7749760 1.2349391 5.08783678
## MeanDecreaseGini
## SUB_ID 121.6988191
## SITE_ID 2.3330145
## PHY_ID 5.6066219
## PRIORFRAC 1.6651829
## AGE 4.8114544
## WEIGHT 5.5597058
## HEIGHT 5.7925347
## BMI 6.5129065
## PREMENO 0.7395337
## MOMFRAC 1.3454730
## ARMASSIST 1.0115536
## SMOKE 0.4689098
## RATERISK 1.6297359
## FRACSCORE 5.4296917
## BONEMED 1.1499490
## BONEMED_FU 1.8871480
## BONETREAT 0.7417384
## RATERISK_EQ_3 0.9478571
## RATERISK_num 1.5842613
## AGE_STDZ 4.8099313
## AGExPRIORFRAC 2.8208520
## MOMFRACxARMASSIST 0.4396781
## PRIORFRACxAGE_STDZ 2.8700394
## NOPRIORFRACxAGE_STDZ 4.4753365varImpPlot(rf_model) # Plots variable importance
# PRINCIPAL COMPONENT ANALYSIS
library(FactoMineR)
# Select only numeric columns for PCA
numerical_data <- GLOW_data[sapply(GLOW_data, is.numeric)]
# Perform PCA
res.pca <- PCA(numerical_data, graph=FALSE)
# Print PCA results
print(res.pca)## **Results for the Principal Component Analysis (PCA)**
## The analysis was performed on 500 individuals, described by 19 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. for the variables"
## 4 "$var$cor" "correlations variables - dimensions"
## 5 "$var$cos2" "cos2 for the variables"
## 6 "$var$contrib" "contributions of the variables"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "summary statistics"
## 12 "$call$centre" "mean of the variables"
## 13 "$call$ecart.type" "standard error of the variables"
## 14 "$call$row.w" "weights for the individuals"
## 15 "$call$col.w" "weights for the variables"# Optionally, plot the PCA
plot(res.pca, choix="var") # For variable contributions
plot(res.pca, choix="ind") # For individual (observation) contributions
# COMPUTING CORRELATION COEFFICIENTS
# Ensure FRACTURE is numeric for correlation computation
GLOW_data$FRACTURE <- as.numeric(as.factor(GLOW_data$FRACTURE)) - 1
# Re-run correlation with FRACTURE included if it's binary numeric
numerical_vars <- sapply(GLOW_data, is.numeric) # Re-check numerical variables including FRACTURE
correlations <- cor(GLOW_data[, numerical_vars], use="pairwise.complete.obs") # Compute the correlation matrix
fracture_correlations <- correlations[,"FRACTURE", drop = FALSE] # Extract correlations with FRACTURE
print(fracture_correlations)## FRACTURE
## SUB_ID 0.75000150
## SITE_ID 0.06935643
## PHY_ID 0.06745920
## PRIORFRAC 0.21808819
## AGE 0.20765352
## WEIGHT -0.03625944
## HEIGHT -0.13640055
## BMI 0.01498506
## MOMFRAC 0.10643875
## ARMASSIST 0.15256788
## SMOKE -0.03167940
## FRACSCORE 0.26447951
## FRACTURE 1.00000000
## RATERISK_EQ_3 0.12419080
## RATERISK_num 0.15173188
## AGE_STDZ 0.20765352
## AGExPRIORFRAC 0.09727651
## MOMFRACxARMASSIST 0.05827942
## PRIORFRACxAGE_STDZ 0.09727651
## NOPRIORFRACxAGE_STDZ 0.18931686# Computing Correlation Coefficients:
# GLOW_data is our dataset and FRACTURE is our binary target variable
numerical_vars <- sapply(GLOW_data, is.numeric) # Identify numerical variables
correlations <- cor(GLOW_data[, numerical_vars]) # Compute the correlation matrix
# Extract the correlations of all variables with FRACTURE
fracture_correlations <- correlations[,"FRACTURE", drop = FALSE] # Preserves the dataframe structure
sorted_correlations <- sort(fracture_correlations, decreasing = TRUE) # Sort by absolute value
print(sorted_correlations)## [1] 1.00000000 0.75000150 0.26447951 0.21808819 0.20765352 0.20765352
## [7] 0.18931686 0.15256788 0.15173188 0.12419080 0.10643875 0.09727651
## [13] 0.09727651 0.06935643 0.06745920 0.05827942 0.01498506 -0.03167940
## [19] -0.03625944 -0.13640055# FEATURE SELECTION
# Recursive Feature Elimination (RFE) to Select Predictive Variables:
# FRACTURE is our first column
control <- rfeControl(functions=rfFuncs, method="cv", number=10)
results <- rfe(GLOW_data[, -1], GLOW_data[, 1],
sizes=c(1:5), rfeControl=control)
print(results)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables RMSE Rsquared MAE RMSESD RsquaredSD MAESD Selected
## 1 95.47 0.5639 78.23 3.750 0.04155 3.786 *
## 2 96.81 0.5543 79.17 3.930 0.04536 3.745
## 3 97.60 0.5498 79.88 2.353 0.04308 2.787
## 4 98.88 0.5407 81.12 2.619 0.03719 3.472
## 5 100.13 0.5392 83.28 3.284 0.03631 4.451
## 24 98.37 0.5374 80.03 3.916 0.04202 3.995
##
## The top 1 variables (out of 1):
## FRACTURE# CHI SQUARED
#Chi-Squared Test for Categorical Variables: to see their relationship with the binary target FRACTURE, we perform a chi-squared test for each categorical variable:
# Identify categorical variables
categorical_vars <- sapply(GLOW_data, is.factor) | sapply(GLOW_data, is.character)
# Names of categorical variables
categorical_var_names <- names(GLOW_data)[categorical_vars]
# Perform a Chi-squared test for each categorical variable
for(var in categorical_var_names) {
tryCatch({
cat_table <- table(GLOW_data[[var]], GLOW_data$FRACTURE)
# Ensure the table has more than one level for both rows and columns
if (all(dim(cat_table) > 1)) {
chi_res <- chisq.test(cat_table)
print(paste("Chi-squared test for variable:", var))
print(chi_res)
} else {
print(paste("Variable", var, "cannot be tested due to insufficient data or lack of variability."))
}
}, error = function(e) {
print(paste("Error in chi-squared test for variable:", var))
print(e)
})
}## [1] "Chi-squared test for variable: PREMENO"
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: cat_table
## X-squared = 0.0042636, df = 1, p-value = 0.9479
##
## [1] "Chi-squared test for variable: RATERISK"
##
## Pearson's Chi-squared test
##
## data: cat_table
## X-squared = 11.547, df = 2, p-value = 0.003109
##
## [1] "Chi-squared test for variable: BONEMED"
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: cat_table
## X-squared = 9.7822, df = 1, p-value = 0.001762
##
## [1] "Chi-squared test for variable: BONEMED_FU"
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: cat_table
## X-squared = 16.743, df = 1, p-value = 4.279e-05
##
## [1] "Chi-squared test for variable: BONETREAT"
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: cat_table
## X-squared = 5.9159, df = 1, p-value = 0.015# NONPARAMETRIC
# Decision Tree w rpart
# Split the data into training and testing sets
set.seed(123) # For reproducibility
indices <- sample(1:nrow(GLOW_data), size = 0.7 * nrow(GLOW_data))
train_data <- GLOW_data[indices, ]
test_data <- GLOW_data[-indices, ]
# Fit the decision tree model
model <- rpart(FRACTURE ~ ., data = train_data, method = "class")
# Summary of the model
summary(model)## Call:
## rpart(formula = FRACTURE ~ ., data = train_data, method = "class")
## n= 350
##
## CP nsplit rel error xerror xstd
## 1 1.00 0 1 1.00000000 0.09437989
## 2 0.01 1 0 0.02352941 0.01659020
##
## Variable importance
## SUB_ID FRACSCORE AGExPRIORFRAC PRIORFRACxAGE_STDZ
## 79 6 6 6
## HEIGHT
## 4
##
## Node number 1: 350 observations, complexity param=1
## predicted class=0 expected loss=0.2428571 P(node) =1
## class counts: 265 85
## probabilities: 0.757 0.243
## left son=2 (265 obs) right son=3 (85 obs)
## Primary splits:
## SUB_ID < 375.5 to the left, improve=128.714300, (0 missing)
## FRACSCORE < 4.5 to the left, improve= 10.000520, (0 missing)
## AGExPRIORFRAC < 0.7717861 to the left, improve= 9.964286, (0 missing)
## PRIORFRACxAGE_STDZ < 0.7717861 to the left, improve= 9.964286, (0 missing)
## NOPRIORFRACxAGE_STDZ < -0.03125856 to the left, improve= 9.575968, (0 missing)
## Surrogate splits:
## FRACSCORE < 7.5 to the left, agree=0.777, adj=0.082, (0 split)
## AGExPRIORFRAC < 0.7717861 to the left, agree=0.774, adj=0.071, (0 split)
## PRIORFRACxAGE_STDZ < 0.7717861 to the left, agree=0.774, adj=0.071, (0 split)
## HEIGHT < 151.5 to the right, agree=0.769, adj=0.047, (0 split)
##
## Node number 2: 265 observations
## predicted class=0 expected loss=0 P(node) =0.7571429
## class counts: 265 0
## probabilities: 1.000 0.000
##
## Node number 3: 85 observations
## predicted class=1 expected loss=0 P(node) =0.2428571
## class counts: 0 85
## probabilities: 0.000 1.000# Predict on the test data
predictions <- predict(model, test_data, type = "class")
# Evaluate the model
table(Predicted = predictions, Actual = test_data$FRACTURE)## Actual
## Predicted 0 1
## 0 110 0
## 1 0 40# Confusion matrix
confusion_matrix <- table(Predicted = predictions, Actual = test_data$FRACTURE)
# Accuracy
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
# Precision
precision <- confusion_matrix[2, 2] / sum(confusion_matrix[2, ])
# Recall
recall <- confusion_matrix[2, 2] / sum(confusion_matrix[, 2])
# F1-score
f1_score <- 2 * (precision * recall) / (precision + recall)
# Print the results
print(paste("Accuracy:", accuracy))## [1] "Accuracy: 1"print(paste("Precision:", precision))## [1] "Precision: 1"print(paste("Recall:", recall))## [1] "Recall: 1"print(paste("F1 Score:", f1_score))## [1] "F1 Score: 1"# Not great results here# Lets create a model with variables : FRACSCORE, WEIGHT, BMI, HEIGHT, and NOPRIORFRACxAGE_STDZ and then one that also includes AGExPRIORFRAC to test
# Model 1 without 'AGExPRIORFRAC'
# Define the formula for the model without AGExPRIORFRAC
formula1 <- FRACTURE ~ FRACSCORE + WEIGHT + BMI + HEIGHT
# Train the model on the training data
model1 <- rpart(formula1, data = train_data, method = "class")
# Predict on the test data
predictions1 <- predict(model1, test_data, type = "class")
# Evaluate the model
confusion_matrix1 <- table(Predicted = predictions1, Actual = test_data$FRACTURE)
accuracy1 <- sum(diag(confusion_matrix1)) / sum(confusion_matrix1)
# Print the results
print(paste("Accuracy for Model 1:", accuracy1))## [1] "Accuracy for Model 1: 0.666666666666667"# Model 2 with 'AGExPRIORFRAC'
# Define the formula for the model with AGExPRIORFRAC
formula2 <- FRACTURE ~ FRACSCORE + WEIGHT + BMI + HEIGHT + AGExPRIORFRAC
# Train the model on the training data
model2 <- rpart(formula2, data = train_data, method = "class")
# Predict on the test data
predictions2 <- predict(model2, test_data, type = "class")
# Evaluate the model
confusion_matrix2 <- table(Predicted = predictions2, Actual = test_data$FRACTURE)
accuracy2 <- sum(diag(confusion_matrix2)) / sum(confusion_matrix2)
# Print the results
print(paste("Accuracy for Model 2:", accuracy2))## [1] "Accuracy for Model 2: 0.666666666666667"# Model 3 with AGExPRIORFRAC and MOMFRACxARMASSIST--as well as AGE, HEIGHT, PRIORFRAC, MOMFRAC, ARMASSIST, and RATERISK_EQ_3.
# Split the data into training and testing sets
set.seed(123) # for reproducibility
indices <- sample(1:nrow(GLOW_data), size = 0.8 * nrow(GLOW_data))
train_data <- GLOW_data[indices, ]
test_data <- GLOW_data[-indices, ]
# Define the model formula
formula <- FRACTURE ~ AGExPRIORFRAC + MOMFRACxARMASSIST + AGE + HEIGHT + PRIORFRAC + MOMFRAC + ARMASSIST + RATERISK_EQ_3
# Train the model on the training data
model <- rpart(formula, data = train_data, method = "class")
# Predict on the test data
predictions <- predict(model, test_data, type = "class")
# Evaluate the model
confusion_matrix <- table(Predicted = predictions, Actual = test_data$FRACTURE)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
# Print the confusion matrix and accuracy
print(confusion_matrix)## Actual
## Predicted 0 1
## 0 60 24
## 1 8 8print(paste("Accuracy:", accuracy))## [1] "Accuracy: 0.68"# Now using only FRACSCORE, AGExPRIORFRAC, MOMFRACxARMASSIST
# Split the data into training and testing sets
set.seed(123) # for reproducibility
indices <- sample(1:nrow(GLOW_data), size = 0.8 * nrow(GLOW_data))
train_data <- GLOW_data[indices, ]
test_data <- GLOW_data[-indices, ]
# Define the model formula with the specified variables
formula <- FRACTURE ~ FRACSCORE + AGExPRIORFRAC + MOMFRACxARMASSIST
# Train the model on the training data
model <- rpart(formula, data = train_data, method = "class")
# Predict on the test data
predictions <- predict(model, test_data, type = "class")
# Evaluate the model
confusion_matrix <- table(Predicted = predictions, Actual = test_data$FRACTURE)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
# Print the confusion matrix and accuracy
print(confusion_matrix)## Actual
## Predicted 0 1
## 0 65 28
## 1 3 4print(paste("Accuracy:", accuracy))## [1] "Accuracy: 0.69"# Rename factor levels for FRACTURE
glow_bonemed_NEW$FRACTURE <- factor(glow_bonemed_NEW$FRACTURE, levels = c("0", "1"), labels = c("Class0", "Class1"))
# Confirm the change
print(table(glow_bonemed_NEW$FRACTURE)) # This should now show the renamed classes##
## Class0 Class1
## 375 125# Set seed for reproducibility
set.seed(123)
# Splitting the data into training and testing sets again
trainIndex <- createDataPartition(glow_bonemed_NEW$FRACTURE, p = 0.8, list = FALSE)
train_data <- glow_bonemed_NEW[trainIndex, ]
test_data <- glow_bonemed_NEW[-trainIndex, ]
# Verifying that FRACTURE is included and properly formatted
head(train_data$FRACTURE)## [1] Class0 Class0 Class0 Class0 Class0 Class0
## Levels: Class0 Class1head(test_data$FRACTURE)## [1] Class0 Class0 Class0 Class0 Class0 Class0
## Levels: Class0 Class1## Set seed for reproducibility
set.seed(123)
# Define training control
train_control <- trainControl(method = "cv", number = 10, savePredictions = "final", classProbs = TRUE)
# Train the model using caret with cross-validation
model_caret <- train(FRACTURE ~ ., data = glow_bonemed_NEW, method = "rpart",
trControl = train_control, tuneLength = 10)
# Print the best model's results
print(model_caret)## CART
##
## 500 samples
## 24 predictor
## 2 classes: 'Class0', 'Class1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 449, 449, 451, 451, 449, 451, ...
## Resampling results across tuning parameters:
##
## cp Accuracy Kappa
## 0.0000000 0.9979592 0.9946331
## 0.1111111 0.9979592 0.9946331
## 0.2222222 0.9979592 0.9946331
## 0.3333333 0.9979592 0.9946331
## 0.4444444 0.9979592 0.9946331
## 0.5555556 0.9979592 0.9946331
## 0.6666667 0.9979592 0.9946331
## 0.7777778 0.9979592 0.9946331
## 0.8888889 0.9979592 0.9946331
## 1.0000000 0.7501000 0.0000000
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.8888889.# Ensure test data FRACTURE is also a factor (if it's not already)
test_data$FRACTURE <- factor(test_data$FRACTURE)
# Predict on the test data
predictions <- predict(model_caret, newdata = test_data, type = "raw")
# Evaluate the model using confusionMatrix from caret
conf_matrix <- confusionMatrix(predictions, test_data$FRACTURE)
print(conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction Class0 Class1
## Class0 75 0
## Class1 0 25
##
## Accuracy : 1
## 95% CI : (0.9638, 1)
## No Information Rate : 0.75
## P-Value [Acc > NIR] : 3.207e-13
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.00
## Specificity : 1.00
## Pos Pred Value : 1.00
## Neg Pred Value : 1.00
## Prevalence : 0.75
## Detection Rate : 0.75
## Detection Prevalence : 0.75
## Balanced Accuracy : 1.00
##
## 'Positive' Class : Class0
## # Model importance
importance <- varImp(model_caret, scale = FALSE)
print(importance)## rpart variable importance
##
## only 20 most important variables shown (out of 25)
##
## Overall
## SUB_ID 187.500
## NOPRIORFRACxAGE_STDZ 10.974
## FRACSCORE 10.202
## PRIORFRAC 8.918
## AGE 7.261
## PREMENOYes 0.000
## RATERISK_EQ_3 0.000
## HEIGHT 0.000
## PHY_ID 0.000
## SMOKE 0.000
## AGExPRIORFRAC 0.000
## AGE_STDZ 0.000
## WEIGHT 0.000
## MOMFRAC 0.000
## BONETREATYes 0.000
## PRIORFRACxAGE_STDZ 0.000
## RATERISKSame 0.000
## ARMASSIST 0.000
## BMI 0.000
## BONEMEDYes 0.000plot(importance)
# Probability predictions for ROC curve
prob_predictions <- predict(model_caret, newdata = test_data, type = "prob")
roc_curve <- roc(response = test_data$FRACTURE, predictor = prob_predictions$Class1)## Setting levels: control = Class0, case = Class1## Setting direction: controls < casesplot(roc_curve)
# Check the current size of classes in training data
table(train_data$FRACTURE)##
## Class0 Class1
## 300 100# Apply SMOTE to balance the classes, ensuring we have an even number of cases for each class
# Here we calculate the number of cases needed to balance the classes
majority_size <- max(table(train_data$FRACTURE))
minority_size <- min(table(train_data$FRACTURE))
desired_size <- 2 * majority_size # Desired total size after oversampling
# Using SMOTE for oversampling the minority class
if (minority_size < majority_size) {
smote_data <- ovun.sample(FRACTURE ~ ., data = train_data, method = "over", N = desired_size, seed = 123)$data
} else {
smote_data <- train_data # No need for oversampling if classes are balanced
}
# Check the new balance of the dataset after SMOTE
table(smote_data$FRACTURE)##
## Class0 Class1
## 300 300# Retrain the model using the balanced dataset
balanced_model <- train(FRACTURE ~ ., data = smote_data, method = "rpart",
trControl = train_control, tuneLength = 10)
# Predict on the original test set
balanced_predictions <- predict(balanced_model, newdata = test_data, type = "raw")
# Confusion matrix to evaluate the model
balanced_conf_matrix <- confusionMatrix(balanced_predictions, test_data$FRACTURE)
print(balanced_conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction Class0 Class1
## Class0 75 0
## Class1 0 25
##
## Accuracy : 1
## 95% CI : (0.9638, 1)
## No Information Rate : 0.75
## P-Value [Acc > NIR] : 3.207e-13
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.00
## Specificity : 1.00
## Pos Pred Value : 1.00
## Neg Pred Value : 1.00
## Prevalence : 0.75
## Detection Rate : 0.75
## Detection Prevalence : 0.75
## Balanced Accuracy : 1.00
##
## 'Positive' Class : Class0
## # Probability predictions for ROC curve
balanced_prob_predictions <- predict(balanced_model, newdata = test_data, type = "prob")
balanced_roc_curve <- roc(response = test_data$FRACTURE, predictor = balanced_prob_predictions$Class1)## Setting levels: control = Class0, case = Class1
## Setting direction: controls < casesplot(balanced_roc_curve)
# Model importance
balanced_importance <- varImp(balanced_model, scale = FALSE)
print(balanced_importance)## rpart variable importance
##
## only 20 most important variables shown (out of 25)
##
## Overall
## SUB_ID 300.00
## PRIORFRAC 33.40
## NOPRIORFRACxAGE_STDZ 32.83
## FRACSCORE 27.65
## BONEMED_FUYes 21.58
## RATERISK_num 0.00
## RATERISK_EQ_3 0.00
## MOMFRACxARMASSIST 0.00
## MOMFRAC 0.00
## AGE_STDZ 0.00
## SMOKE 0.00
## AGE 0.00
## SITE_ID 0.00
## PHY_ID 0.00
## HEIGHT 0.00
## WEIGHT 0.00
## BONEMEDYes 0.00
## RATERISKGreater 0.00
## RATERISKSame 0.00
## BONETREATYes 0.00plot(balanced_importance)
# Model Iteration
# Adjust dataset based on feature importance if necessary # For example, dropping a less important feature:
# train_data_adjusted <- train_data[, !(names(train_data) %in% c("LEAST_IMPORTANT_FEATURE"))]
# test_data_adjusted <- test_data[, !(names(test_data) %in% c("LEAST_IMPORTANT_FEATURE"))]
# Retrain the model on the adjusted data
# model_adjusted <- train(FRACTURE ~ ., data = train_data_adjusted, method = "rpart",
# trControl = train_control, tuneLength = 10)
# Cross-Validation Reevaluation
# Adjusted training control with class probabilities
# train_control <- trainControl(method = "cv", number = 10, savePredictions = "final", classProbs = TRUE)
# Train the models (for both cv_model and rf_model, this is just a placeholder for the complete training code)
# Predict probabilities from both models
# cv_prob_predictions <- predict(cv_model, newdata = test_data_adjusted, type = "prob")
# rf_prob_predictions <- predict(rf_model, newdata = test_data_adjusted, type = "prob")
# Create ensemble predictions
# ensemble_prob <- (cv_prob_predictions$Class1 + rf_prob_predictions$Class1) / 2
# ensemble_predictions <- ifelse(ensemble_prob > 0.5, "Class1", "Class0")
# Evaluate ensemble model
# ensemble_conf_matrix <- confusionMatrix(as.factor(ensemble_predictions), test_data_adjusted$FRACTURE)
# print(ensemble_conf_matrix)
# Calculate different performance metrics
# conf_matrix <- confusionMatrix(predictions, test_data$FRACTURE)
# print(conf_matrix$byClass) # Gives you Precision, Recall, F1 score etc.# CV
train_control <- trainControl(method = "repeatedcv", number = 10, repeats = 3, savePredictions = "final", classProbs = TRUE)
model <- train(FRACTURE ~ ., data = train_data, method = "rf", trControl = train_control)# Feature Importance Analysis
importance <- varImp(model, scale = FALSE)
print(importance)## rf variable importance
##
## only 20 most important variables shown (out of 25)
##
## Overall
## SUB_ID 136.99942
## PRIORFRAC 2.50617
## FRACSCORE 2.22789
## BMI 1.12568
## NOPRIORFRACxAGE_STDZ 1.08681
## HEIGHT 0.82258
## AGExPRIORFRAC 0.74321
## PRIORFRACxAGE_STDZ 0.70363
## PHY_ID 0.63552
## AGE_STDZ 0.60221
## WEIGHT 0.44571
## AGE 0.40569
## BONEMED_FUYes 0.29818
## BONEMEDYes 0.22496
## RATERISK_num 0.20971
## SITE_ID 0.20385
## ARMASSIST 0.16815
## BONETREATYes 0.13213
## PREMENOYes 0.08728
## MOMFRAC 0.08713