| title | subtitle | author | date | output | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
**CLIMATEREADY** |
**Thermal Comfort Survey Amid Heatwaves** |
Juan Gamero-Salinas - Postdoc Researcher @ DATAI University of Navarra |
2024-08-07 |
|
This R Markdown file allows contains the code for answering the Research Objective 3 (RQ3): Which factors contribute to an increased likelihood of occupants being clustered into distinct groups of thermal comfort? of the case study on the research paper Exploring indoor thermal comfort and its causes and consequences amid heatwaves in a Southern European city— An unsupervised learning approach
Set the folder location
set.seed(42) # Replace 123 with your desired seed value df <- read.csv("R_logistic_kmeans.csv", sep=";")
head(df, 3)## meanTout Gender Age NatVent_night UsesCoolingAlternatives hw_True
## 1 27.5 True 39 True False False
## 2 27.5 True 29 True False False
## 3 18.1 False 55 False False False
## cte_23_7h_True cte_7_15h_True cte_15_23h_True ShadingDevices_No
## 1 False True False False
## 2 False True False False
## 3 False True False True
## ShadingDevices_WhenDirectSun ShadingDevices_AllAfternoon
## 1 False False
## 2 False False
## 3 False False
## ShadingDevices_AllMorning ShadingDevices_AllDay NatVent_day_ToutCool
## 1 False True True
## 2 False True True
## 3 False False True
## NatVent_day_Anytime NatVent_day_No hasAC_No hasAC_DormOrLiving
## 1 False False True False
## 2 False False True False
## 3 False False True False
## hasAC_DormAndLiving hasAC_AllRooms HouseholdSize is_31001 is_31002 is_31003
## 1 False False 2 False False False
## 2 False False 3 False False False
## 3 False False 4 False False False
## is_31004 is_31005 is_31006 is_31007 is_31008 is_31009 is_31010 is_31011
## 1 False False False False False False False False
## 2 False False False True False False False False
## 3 False False False False True False False False
## is_31012 is_31013 is_31014 is_31015 is_31016 isNot_pamplona before_1980
## 1 False False False False True False False
## 2 False False False False False False False
## 3 False False False False False False True
## between_1980_2006 after_2007 dwelling_OldTown dwelling_Block dwelling_Tower
## 1 True False False True False
## 2 True False False True False
## 3 False False False False True
## dwelling_Detached dwelling_Other Rehab_No Rehab_Yes Income_below1500
## 1 False False True False False
## 2 False False False True False
## 3 False False False True False
## Income_between_1500_3500 Income_above_3500 Occ_NormallyAtHome
## 1 False True True
## 2 True False False
## 3 False True False
## Occ_NotAlwaysAtHome SrfcArea_below90 Storey_UpperFloor Storey_NotApartment
## 1 False False False False
## 2 True True False False
## 3 True False False False
## numOrient_1 numOrient_above2 LivRoom_SrfcAreaWindow_below2
## 1 False True False
## 2 False True False
## 3 False True False
## LivRoom_SrfcAreaWindow_above2 Bedroom_SrfcAreaWindow_below2
## 1 True False
## 2 True False
## 3 True False
## Bedroom_SrfcAreaWindow_above2 AC_Installed_Yes WouldYouInstallAC_Yes
## 1 True False False
## 2 True False True
## 3 True False False
## hasCoolRoom hasCoolingAlternatives cluster_kmeans
## 1 True True 0
## 2 False True 0
## 3 False False 0
library(dplyr)##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
convert_to_binary <- function(x) {
as.numeric(x)
}###Apply the function to all columns except the first one (assuming it's the index)
data_transformed <- df %>%
mutate_all(~ ifelse(. == "True", 1, ifelse(. == "False", 0, .)))head(data_transformed, 3)## meanTout Gender Age NatVent_night UsesCoolingAlternatives hw_True
## 1 27.5 1 39 1 0 0
## 2 27.5 1 29 1 0 0
## 3 18.1 0 55 0 0 0
## cte_23_7h_True cte_7_15h_True cte_15_23h_True ShadingDevices_No
## 1 0 1 0 0
## 2 0 1 0 0
## 3 0 1 0 1
## ShadingDevices_WhenDirectSun ShadingDevices_AllAfternoon
## 1 0 0
## 2 0 0
## 3 0 0
## ShadingDevices_AllMorning ShadingDevices_AllDay NatVent_day_ToutCool
## 1 0 1 1
## 2 0 1 1
## 3 0 0 1
## NatVent_day_Anytime NatVent_day_No hasAC_No hasAC_DormOrLiving
## 1 0 0 1 0
## 2 0 0 1 0
## 3 0 0 1 0
## hasAC_DormAndLiving hasAC_AllRooms HouseholdSize is_31001 is_31002 is_31003
## 1 0 0 2 0 0 0
## 2 0 0 3 0 0 0
## 3 0 0 4 0 0 0
## is_31004 is_31005 is_31006 is_31007 is_31008 is_31009 is_31010 is_31011
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 1 0 0 0 0
## 3 0 0 0 0 1 0 0 0
## is_31012 is_31013 is_31014 is_31015 is_31016 isNot_pamplona before_1980
## 1 0 0 0 0 1 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 1
## between_1980_2006 after_2007 dwelling_OldTown dwelling_Block dwelling_Tower
## 1 1 0 0 1 0
## 2 1 0 0 1 0
## 3 0 0 0 0 1
## dwelling_Detached dwelling_Other Rehab_No Rehab_Yes Income_below1500
## 1 0 0 1 0 0
## 2 0 0 0 1 0
## 3 0 0 0 1 0
## Income_between_1500_3500 Income_above_3500 Occ_NormallyAtHome
## 1 0 1 1
## 2 1 0 0
## 3 0 1 0
## Occ_NotAlwaysAtHome SrfcArea_below90 Storey_UpperFloor Storey_NotApartment
## 1 0 0 0 0
## 2 1 1 0 0
## 3 1 0 0 0
## numOrient_1 numOrient_above2 LivRoom_SrfcAreaWindow_below2
## 1 0 1 0
## 2 0 1 0
## 3 0 1 0
## LivRoom_SrfcAreaWindow_above2 Bedroom_SrfcAreaWindow_below2
## 1 1 0
## 2 1 0
## 3 1 0
## Bedroom_SrfcAreaWindow_above2 AC_Installed_Yes WouldYouInstallAC_Yes
## 1 1 0 0
## 2 1 0 1
## 3 1 0 0
## hasCoolRoom hasCoolingAlternatives cluster_kmeans
## 1 1 1 0
## 2 0 1 0
## 3 0 0 0
names(data_transformed)## [1] "meanTout" "Gender"
## [3] "Age" "NatVent_night"
## [5] "UsesCoolingAlternatives" "hw_True"
## [7] "cte_23_7h_True" "cte_7_15h_True"
## [9] "cte_15_23h_True" "ShadingDevices_No"
## [11] "ShadingDevices_WhenDirectSun" "ShadingDevices_AllAfternoon"
## [13] "ShadingDevices_AllMorning" "ShadingDevices_AllDay"
## [15] "NatVent_day_ToutCool" "NatVent_day_Anytime"
## [17] "NatVent_day_No" "hasAC_No"
## [19] "hasAC_DormOrLiving" "hasAC_DormAndLiving"
## [21] "hasAC_AllRooms" "HouseholdSize"
## [23] "is_31001" "is_31002"
## [25] "is_31003" "is_31004"
## [27] "is_31005" "is_31006"
## [29] "is_31007" "is_31008"
## [31] "is_31009" "is_31010"
## [33] "is_31011" "is_31012"
## [35] "is_31013" "is_31014"
## [37] "is_31015" "is_31016"
## [39] "isNot_pamplona" "before_1980"
## [41] "between_1980_2006" "after_2007"
## [43] "dwelling_OldTown" "dwelling_Block"
## [45] "dwelling_Tower" "dwelling_Detached"
## [47] "dwelling_Other" "Rehab_No"
## [49] "Rehab_Yes" "Income_below1500"
## [51] "Income_between_1500_3500" "Income_above_3500"
## [53] "Occ_NormallyAtHome" "Occ_NotAlwaysAtHome"
## [55] "SrfcArea_below90" "Storey_UpperFloor"
## [57] "Storey_NotApartment" "numOrient_1"
## [59] "numOrient_above2" "LivRoom_SrfcAreaWindow_below2"
## [61] "LivRoom_SrfcAreaWindow_above2" "Bedroom_SrfcAreaWindow_below2"
## [63] "Bedroom_SrfcAreaWindow_above2" "AC_Installed_Yes"
## [65] "WouldYouInstallAC_Yes" "hasCoolRoom"
## [67] "hasCoolingAlternatives" "cluster_kmeans"
length(data_transformed)## [1] 68
nrow(data_transformed)## [1] 189
n = nrow(data_transformed)
n## [1] 189
# Specify the variable to be used as Y response
Y_response <- "cluster_kmeans"
# Extract the Y response
Y <- data_transformed[, Y_response]
# Extract the X response (all other variables)
X <- data_transformed[, setdiff(names(data_transformed), Y_response)]exclude_variables <- c("Age", "meanTout")X[, setdiff(names(X), exclude_variables)] <- lapply(X[, setdiff(names(X), exclude_variables)], as.integer)str(X)## 'data.frame': 189 obs. of 67 variables:
## $ meanTout : num 27.5 27.5 18.1 29.2 20.7 26.8 16.8 29.1 26.8 15.8 ...
## $ Gender : int 1 1 0 1 0 0 1 1 1 0 ...
## $ Age : int 39 29 55 44 43 43 54 40 26 44 ...
## $ NatVent_night : int 1 1 0 0 1 1 1 1 1 1 ...
## $ UsesCoolingAlternatives : int 0 0 0 0 1 1 0 0 1 0 ...
## $ hw_True : int 0 0 0 1 0 1 0 1 1 0 ...
## $ cte_23_7h_True : int 0 0 0 0 0 0 0 0 0 1 ...
## $ cte_7_15h_True : int 1 1 1 0 0 1 0 0 0 0 ...
## $ cte_15_23h_True : int 0 0 0 1 1 0 1 1 1 0 ...
## $ ShadingDevices_No : int 0 0 1 0 0 0 0 0 0 0 ...
## $ ShadingDevices_WhenDirectSun : int 0 0 0 0 0 0 0 0 1 1 ...
## $ ShadingDevices_AllAfternoon : int 0 0 0 0 0 1 0 0 0 0 ...
## $ ShadingDevices_AllMorning : int 0 0 0 0 0 0 0 0 0 0 ...
## $ ShadingDevices_AllDay : int 1 1 0 1 1 0 1 1 0 0 ...
## $ NatVent_day_ToutCool : int 1 1 1 1 1 1 1 1 1 1 ...
## $ NatVent_day_Anytime : int 0 0 0 0 0 0 0 0 0 0 ...
## $ NatVent_day_No : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hasAC_No : int 1 1 1 0 1 1 1 0 1 1 ...
## $ hasAC_DormOrLiving : int 0 0 0 1 0 0 0 1 0 0 ...
## $ hasAC_DormAndLiving : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hasAC_AllRooms : int 0 0 0 0 0 0 0 0 0 0 ...
## $ HouseholdSize : int 2 3 4 4 2 3 2 2 5 6 ...
## $ is_31001 : int 0 0 0 0 0 0 1 0 0 0 ...
## $ is_31002 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31003 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31004 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31005 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31006 : int 0 0 0 0 0 0 0 0 1 0 ...
## $ is_31007 : int 0 1 0 0 0 0 0 0 0 0 ...
## $ is_31008 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ is_31009 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31010 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31011 : int 0 0 0 0 0 0 0 1 0 0 ...
## $ is_31012 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31013 : int 0 0 0 0 1 0 0 0 0 0 ...
## $ is_31014 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31015 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31016 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ isNot_pamplona : int 0 0 0 1 0 1 0 0 0 1 ...
## $ before_1980 : int 0 0 1 0 0 0 1 1 0 0 ...
## $ between_1980_2006 : int 1 1 0 1 1 0 0 0 0 1 ...
## $ after_2007 : int 0 0 0 0 0 1 0 0 1 0 ...
## $ dwelling_OldTown : int 0 0 0 0 0 0 1 0 0 0 ...
## $ dwelling_Block : int 1 1 0 1 0 0 0 1 1 0 ...
## $ dwelling_Tower : int 0 0 1 0 1 1 0 0 0 0 ...
## $ dwelling_Detached : int 0 0 0 0 0 0 0 0 0 1 ...
## $ dwelling_Other : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Rehab_No : int 1 0 0 0 0 0 0 1 1 1 ...
## $ Rehab_Yes : int 0 1 1 1 1 1 1 0 0 0 ...
## $ Income_below1500 : int 0 0 0 1 1 1 0 0 0 0 ...
## $ Income_between_1500_3500 : int 0 1 0 0 0 0 1 0 0 0 ...
## $ Income_above_3500 : int 1 0 1 0 0 0 0 1 1 1 ...
## $ Occ_NormallyAtHome : int 1 0 0 0 0 0 1 0 0 1 ...
## $ Occ_NotAlwaysAtHome : int 0 1 1 1 1 1 1 1 1 1 ...
## $ SrfcArea_below90 : int 0 1 0 1 1 0 1 0 0 0 ...
## $ Storey_UpperFloor : int 0 0 0 0 1 0 0 0 0 0 ...
## $ Storey_NotApartment : int 0 0 0 0 0 0 0 0 0 1 ...
## $ numOrient_1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numOrient_above2 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ LivRoom_SrfcAreaWindow_below2: int 0 0 0 0 0 1 0 0 0 0 ...
## $ LivRoom_SrfcAreaWindow_above2: int 1 1 1 1 1 0 1 1 1 1 ...
## $ Bedroom_SrfcAreaWindow_below2: int 0 0 0 0 1 1 1 0 0 1 ...
## $ Bedroom_SrfcAreaWindow_above2: int 1 1 1 1 0 0 0 1 1 0 ...
## $ AC_Installed_Yes : int 0 0 0 0 0 0 0 1 0 0 ...
## $ WouldYouInstallAC_Yes : int 0 1 0 0 0 0 1 0 1 1 ...
## $ hasCoolRoom : int 1 0 0 1 0 0 0 1 0 1 ...
## $ hasCoolingAlternatives : int 1 1 0 1 1 1 1 1 1 1 ...
X_matrix <- as.matrix(X)
sum(Y)## [1] 92
head(cor(X_matrix),3)## Warning in cor(X_matrix): La desviación estándar es cero
## meanTout Gender Age NatVent_night UsesCoolingAlternatives
## meanTout 1.0000000 0.1307767 -0.1101982 -0.04064627 0.06632550
## Gender 0.1307767 1.0000000 -0.1423769 0.03327403 -0.02237790
## Age -0.1101982 -0.1423769 1.0000000 -0.01101263 -0.06230988
## hw_True cte_23_7h_True cte_7_15h_True cte_15_23h_True
## meanTout 0.64636441 -0.10384495 -0.02166472 0.079502103
## Gender 0.13105838 0.10334686 -0.04560925 -0.009504714
## Age -0.04154947 -0.07961135 -0.01481895 0.059094025
## ShadingDevices_No ShadingDevices_WhenDirectSun
## meanTout -0.02250576 -0.072160114
## Gender 0.01119659 -0.079213768
## Age -0.18236903 0.009446952
## ShadingDevices_AllAfternoon ShadingDevices_AllMorning
## meanTout 0.01048686 -0.042545055
## Gender -0.10825968 -0.005800514
## Age -0.02431157 0.046055713
## ShadingDevices_AllDay NatVent_day_ToutCool NatVent_day_Anytime
## meanTout 0.08698008 -0.122249775 0.072688735
## Gender 0.08865921 0.014818372 0.006207313
## Age 0.05592127 -0.006389317 -0.001543991
## NatVent_day_No hasAC_No hasAC_DormOrLiving hasAC_DormAndLiving
## meanTout 0.09741886 -0.03281046 0.060098794 -0.01822256
## Gender -0.02850635 -0.02710528 0.002625832 0.08555758
## Age 0.01103213 -0.07684401 0.102986999 0.01915430
## hasAC_AllRooms HouseholdSize is_31001 is_31002 is_31003
## meanTout -0.008530845 -0.14867237 -0.09489291 0.051096534 -0.09636606
## Gender -0.010212858 0.04247325 -0.01021286 -0.005800514 0.04913324
## Age -0.011384744 0.12336614 -0.04521677 0.199778091 0.05513891
## is_31004 is_31005 is_31006 is_31007 is_31008 is_31009
## meanTout -0.03016554 0.051096534 0.07067259 0.09732405 0.118293314 NA
## Gender -0.02850635 -0.005800514 0.03089417 -0.09789402 -0.120300727 NA
## Age 0.09214483 -0.103823607 -0.20485991 -0.02857215 0.008472426 NA
## is_31010 is_31011 is_31012 is_31013 is_31014
## meanTout -0.19012245 -0.09273633 4.015141e-02 0.005229221 0.07663515
## Gender -0.04192035 0.02883180 -5.800514e-03 -0.008270131 0.08555758
## Age 0.05471559 0.05316732 -6.100094e-05 0.005392302 -0.03464854
## is_31015 is_31016 isNot_pamplona before_1980 between_1980_2006
## meanTout -0.08237367 0.08156518 -0.08957000 -0.04364346 0.04018246
## Gender -0.03124543 0.08491892 0.08158191 -0.05416186 -0.01231625
## Age 0.02585977 -0.07008056 0.08321341 0.06668020 0.12622650
## after_2007 dwelling_OldTown dwelling_Block dwelling_Tower
## meanTout 0.0004299108 -0.08371100 0.19635348 -0.10222558
## Gender 0.0645917881 -0.02761085 0.01971012 -0.06356862
## Age -0.1939338014 -0.05209269 -0.14112419 0.07572906
## dwelling_Detached dwelling_Other Rehab_No Rehab_Yes
## meanTout -0.10603402 NA -0.04049546 0.04049546
## Gender 0.04461155 NA -0.03470942 0.03470942
## Age 0.14058671 NA -0.02336132 0.02336132
## Income_below1500 Income_between_1500_3500 Income_above_3500
## meanTout 0.06066255 -0.11024342 0.03213456
## Gender 0.09010932 -0.05123298 -0.04078845
## Age -0.12362216 -0.14090091 0.22209714
## Occ_NormallyAtHome Occ_NotAlwaysAtHome SrfcArea_below90
## meanTout -0.14143557 0.07888693 -0.01742477
## Gender -0.01791303 -0.04378727 0.08630280
## Age 0.11047324 -0.08848730 -0.18775747
## Storey_UpperFloor Storey_NotApartment numOrient_1 numOrient_above2
## meanTout -0.01502230 -0.14237088 -0.07956716 0.07956716
## Gender -0.10938933 0.03600155 0.03089417 -0.03089417
## Age -0.03937537 0.14410453 0.03477198 -0.03477198
## LivRoom_SrfcAreaWindow_below2 LivRoom_SrfcAreaWindow_above2
## meanTout 0.08393268 -0.08393268
## Gender 0.06297808 -0.06297808
## Age -0.18178387 0.18178387
## Bedroom_SrfcAreaWindow_below2 Bedroom_SrfcAreaWindow_above2
## meanTout -0.16374220 0.16374220
## Gender -0.01941129 0.01941129
## Age -0.09087218 0.09087218
## AC_Installed_Yes WouldYouInstallAC_Yes hasCoolRoom
## meanTout 0.06000455 -0.11085214 0.120666791
## Gender 0.04661511 0.01688017 0.079648752
## Age 0.08765250 -0.13133360 -0.004316685
## hasCoolingAlternatives
## meanTout -0.09345654
## Gender 0.04218148
## Age -0.06412320
For preventing linear dependencies in the input matrix. We modify x so and remove similar columns.
columns_to_remove <- c("is_31009", "dwelling_Other")columns_to_keep <- !(colnames(X_matrix) %in% columns_to_remove)X_matrix <- X_matrix[, columns_to_keep]X_matrix <- scale(X_matrix)library(glmnet) # Install and load glmnet package## Warning: package 'glmnet' was built under R version 4.4.1
## Cargando paquete requerido: Matrix
## Loaded glmnet 4.1-8
# Example using cross-validation to find the best lambda with cv = 5
cv_model <- cv.glmnet(X_matrix, Y, alpha = 1, family = "binomial", nfolds = 5, type.measure = "class")
plot(cv_model)
# Find the alpha and lambda with the minimum mean cross-validated error
best_lambda <- cv_model$lambda.min
best_lambda## [1] 0.05781199
best_model <- glmnet(X_matrix, Y, alpha = 1, lambda = best_lambda, family = "binomial")
coef(best_model, s = best_lambda)## 66 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) -0.05821818
## meanTout .
## Gender 0.02402558
## Age .
## NatVent_night -0.09584514
## UsesCoolingAlternatives 0.05843147
## hw_True 0.43233428
## cte_23_7h_True .
## cte_7_15h_True .
## cte_15_23h_True .
## ShadingDevices_No -0.03302218
## ShadingDevices_WhenDirectSun .
## ShadingDevices_AllAfternoon .
## ShadingDevices_AllMorning .
## ShadingDevices_AllDay .
## NatVent_day_ToutCool .
## NatVent_day_Anytime .
## NatVent_day_No .
## hasAC_No .
## hasAC_DormOrLiving .
## hasAC_DormAndLiving 0.01877965
## hasAC_AllRooms -0.09910920
## HouseholdSize .
## is_31001 .
## is_31002 .
## is_31003 .
## is_31004 .
## is_31005 .
## is_31006 .
## is_31007 .
## is_31008 .
## is_31010 .
## is_31011 .
## is_31012 .
## is_31013 .
## is_31014 .
## is_31015 .
## is_31016 .
## isNot_pamplona .
## before_1980 .
## between_1980_2006 .
## after_2007 .
## dwelling_OldTown .
## dwelling_Block .
## dwelling_Tower .
## dwelling_Detached .
## Rehab_No .
## Rehab_Yes .
## Income_below1500 .
## Income_between_1500_3500 .
## Income_above_3500 .
## Occ_NormallyAtHome .
## Occ_NotAlwaysAtHome .
## SrfcArea_below90 .
## Storey_UpperFloor .
## Storey_NotApartment .
## numOrient_1 .
## numOrient_above2 .
## LivRoom_SrfcAreaWindow_below2 .
## LivRoom_SrfcAreaWindow_above2 .
## Bedroom_SrfcAreaWindow_below2 .
## Bedroom_SrfcAreaWindow_above2 .
## AC_Installed_Yes .
## WouldYouInstallAC_Yes .
## hasCoolRoom .
## hasCoolingAlternatives .
library(selectiveInference)## Warning: package 'selectiveInference' was built under R version 4.4.1
## Cargando paquete requerido: intervals
##
## Adjuntando el paquete: 'intervals'
## The following object is masked from 'package:Matrix':
##
## expand
## Cargando paquete requerido: survival
## Cargando paquete requerido: adaptMCMC
## Warning: package 'adaptMCMC' was built under R version 4.4.1
## Cargando paquete requerido: parallel
## Cargando paquete requerido: coda
## Warning: package 'coda' was built under R version 4.4.1
## Cargando paquete requerido: MASS
##
## Adjuntando el paquete: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
# extract coef for a given lambda; note the 1/n factor!
# (and here we DO include the intercept term)
beta = coef(best_model, x=X_matrix, y=Y, s = best_lambda/n)# compute fixed lambda p-values and selection intervals
model = fixedLassoInf(X_matrix,Y,beta,best_lambda,family="binomial")## Warning in fixedLogitLassoInf(x, y, beta, lambda, alpha = alpha, type = type, :
## Solution beta does not satisfy the KKT conditions (to within specified
## tolerances)
model##
## Call:
## fixedLassoInf(x = X_matrix, y = Y, beta = beta, lambda = best_lambda,
## family = "binomial")
##
## Testing results at lambda = 0.058, with alpha = 0.100
##
## Var Coef Z-score P-value LowConfPt UpConfPt LowTailArea UpTailArea
## 2 0.237 1.561 0.733 -2.639 0.432 0.050 0.050
## 4 -0.448 -2.871 0.170 -0.708 0.333 0.049 0.049
## 5 0.281 1.831 0.455 -0.994 0.556 0.000 0.050
## 6 0.699 4.552 0.000 0.437 1.092 0.049 0.049
## 10 -0.330 -2.109 0.605 -0.506 1.893 0.049 0.050
## 20 0.232 1.548 0.785 -3.363 0.357 0.050 0.049
## 21 -0.336 -2.151 0.243 -0.587 0.419 0.050 0.050
##
## Note: coefficients shown are full regression coefficients
Set the folder location
set.seed(42) # Replace 123 with your desired seed value df <- read.csv("R_logistic_hclust.csv", sep=";")
head(df, 3)## meanTout Gender Age NatVent_night UsesCoolingAlternatives hw_True
## 1 27.5 True 39 True False False
## 2 27.5 True 29 True False False
## 3 18.1 False 55 False False False
## cte_23_7h_True cte_7_15h_True cte_15_23h_True ShadingDevices_No
## 1 False True False False
## 2 False True False False
## 3 False True False True
## ShadingDevices_WhenDirectSun ShadingDevices_AllAfternoon
## 1 False False
## 2 False False
## 3 False False
## ShadingDevices_AllMorning ShadingDevices_AllDay NatVent_day_ToutCool
## 1 False True True
## 2 False True True
## 3 False False True
## NatVent_day_Anytime NatVent_day_No hasAC_No hasAC_DormOrLiving
## 1 False False True False
## 2 False False True False
## 3 False False True False
## hasAC_DormAndLiving hasAC_AllRooms HouseholdSize is_31001 is_31002 is_31003
## 1 False False 2 False False False
## 2 False False 3 False False False
## 3 False False 4 False False False
## is_31004 is_31005 is_31006 is_31007 is_31008 is_31009 is_31010 is_31011
## 1 False False False False False False False False
## 2 False False False True False False False False
## 3 False False False False True False False False
## is_31012 is_31013 is_31014 is_31015 is_31016 isNot_pamplona before_1980
## 1 False False False False True False False
## 2 False False False False False False False
## 3 False False False False False False True
## between_1980_2006 after_2007 dwelling_OldTown dwelling_Block dwelling_Tower
## 1 True False False True False
## 2 True False False True False
## 3 False False False False True
## dwelling_Detached dwelling_Other Rehab_No Rehab_Yes Income_below1500
## 1 False False True False False
## 2 False False False True False
## 3 False False False True False
## Income_between_1500_3500 Income_above_3500 Occ_NormallyAtHome
## 1 False True True
## 2 True False False
## 3 False True False
## Occ_NotAlwaysAtHome SrfcArea_below90 Storey_UpperFloor Storey_NotApartment
## 1 False False False False
## 2 True True False False
## 3 True False False False
## numOrient_1 numOrient_above2 LivRoom_SrfcAreaWindow_below2
## 1 False True False
## 2 False True False
## 3 False True False
## LivRoom_SrfcAreaWindow_above2 Bedroom_SrfcAreaWindow_below2
## 1 True False
## 2 True False
## 3 True False
## Bedroom_SrfcAreaWindow_above2 AC_Installed_Yes WouldYouInstallAC_Yes
## 1 True False False
## 2 True False True
## 3 True False False
## hasCoolRoom hasCoolingAlternatives cluster_hierarchical
## 1 True True 0
## 2 False True 0
## 3 False False 0
library(dplyr)
convert_to_binary <- function(x) {
as.numeric(x)
}###Apply the function to all columns except the first one (assuming it's the index)
data_transformed <- df %>%
mutate_all(~ ifelse(. == "True", 1, ifelse(. == "False", 0, .)))head(data_transformed,3)## meanTout Gender Age NatVent_night UsesCoolingAlternatives hw_True
## 1 27.5 1 39 1 0 0
## 2 27.5 1 29 1 0 0
## 3 18.1 0 55 0 0 0
## cte_23_7h_True cte_7_15h_True cte_15_23h_True ShadingDevices_No
## 1 0 1 0 0
## 2 0 1 0 0
## 3 0 1 0 1
## ShadingDevices_WhenDirectSun ShadingDevices_AllAfternoon
## 1 0 0
## 2 0 0
## 3 0 0
## ShadingDevices_AllMorning ShadingDevices_AllDay NatVent_day_ToutCool
## 1 0 1 1
## 2 0 1 1
## 3 0 0 1
## NatVent_day_Anytime NatVent_day_No hasAC_No hasAC_DormOrLiving
## 1 0 0 1 0
## 2 0 0 1 0
## 3 0 0 1 0
## hasAC_DormAndLiving hasAC_AllRooms HouseholdSize is_31001 is_31002 is_31003
## 1 0 0 2 0 0 0
## 2 0 0 3 0 0 0
## 3 0 0 4 0 0 0
## is_31004 is_31005 is_31006 is_31007 is_31008 is_31009 is_31010 is_31011
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 1 0 0 0 0
## 3 0 0 0 0 1 0 0 0
## is_31012 is_31013 is_31014 is_31015 is_31016 isNot_pamplona before_1980
## 1 0 0 0 0 1 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 1
## between_1980_2006 after_2007 dwelling_OldTown dwelling_Block dwelling_Tower
## 1 1 0 0 1 0
## 2 1 0 0 1 0
## 3 0 0 0 0 1
## dwelling_Detached dwelling_Other Rehab_No Rehab_Yes Income_below1500
## 1 0 0 1 0 0
## 2 0 0 0 1 0
## 3 0 0 0 1 0
## Income_between_1500_3500 Income_above_3500 Occ_NormallyAtHome
## 1 0 1 1
## 2 1 0 0
## 3 0 1 0
## Occ_NotAlwaysAtHome SrfcArea_below90 Storey_UpperFloor Storey_NotApartment
## 1 0 0 0 0
## 2 1 1 0 0
## 3 1 0 0 0
## numOrient_1 numOrient_above2 LivRoom_SrfcAreaWindow_below2
## 1 0 1 0
## 2 0 1 0
## 3 0 1 0
## LivRoom_SrfcAreaWindow_above2 Bedroom_SrfcAreaWindow_below2
## 1 1 0
## 2 1 0
## 3 1 0
## Bedroom_SrfcAreaWindow_above2 AC_Installed_Yes WouldYouInstallAC_Yes
## 1 1 0 0
## 2 1 0 1
## 3 1 0 0
## hasCoolRoom hasCoolingAlternatives cluster_hierarchical
## 1 1 1 0
## 2 0 1 0
## 3 0 0 0
names(data_transformed)## [1] "meanTout" "Gender"
## [3] "Age" "NatVent_night"
## [5] "UsesCoolingAlternatives" "hw_True"
## [7] "cte_23_7h_True" "cte_7_15h_True"
## [9] "cte_15_23h_True" "ShadingDevices_No"
## [11] "ShadingDevices_WhenDirectSun" "ShadingDevices_AllAfternoon"
## [13] "ShadingDevices_AllMorning" "ShadingDevices_AllDay"
## [15] "NatVent_day_ToutCool" "NatVent_day_Anytime"
## [17] "NatVent_day_No" "hasAC_No"
## [19] "hasAC_DormOrLiving" "hasAC_DormAndLiving"
## [21] "hasAC_AllRooms" "HouseholdSize"
## [23] "is_31001" "is_31002"
## [25] "is_31003" "is_31004"
## [27] "is_31005" "is_31006"
## [29] "is_31007" "is_31008"
## [31] "is_31009" "is_31010"
## [33] "is_31011" "is_31012"
## [35] "is_31013" "is_31014"
## [37] "is_31015" "is_31016"
## [39] "isNot_pamplona" "before_1980"
## [41] "between_1980_2006" "after_2007"
## [43] "dwelling_OldTown" "dwelling_Block"
## [45] "dwelling_Tower" "dwelling_Detached"
## [47] "dwelling_Other" "Rehab_No"
## [49] "Rehab_Yes" "Income_below1500"
## [51] "Income_between_1500_3500" "Income_above_3500"
## [53] "Occ_NormallyAtHome" "Occ_NotAlwaysAtHome"
## [55] "SrfcArea_below90" "Storey_UpperFloor"
## [57] "Storey_NotApartment" "numOrient_1"
## [59] "numOrient_above2" "LivRoom_SrfcAreaWindow_below2"
## [61] "LivRoom_SrfcAreaWindow_above2" "Bedroom_SrfcAreaWindow_below2"
## [63] "Bedroom_SrfcAreaWindow_above2" "AC_Installed_Yes"
## [65] "WouldYouInstallAC_Yes" "hasCoolRoom"
## [67] "hasCoolingAlternatives" "cluster_hierarchical"
length(data_transformed)## [1] 68
nrow(data_transformed)## [1] 189
n = nrow(data_transformed)
n## [1] 189
# Specify the variable to be used as Y response
Y_response <- "cluster_hierarchical"
# Extract the Y response
Y <- data_transformed[, Y_response]
# Extract the X response (all other variables)
X <- data_transformed[, setdiff(names(data_transformed), Y_response)]exclude_variables <- c("Age", "meanTout")X[, setdiff(names(X), exclude_variables)] <- lapply(X[, setdiff(names(X), exclude_variables)], as.integer)str(X)## 'data.frame': 189 obs. of 67 variables:
## $ meanTout : num 27.5 27.5 18.1 29.2 20.7 26.8 16.8 29.1 26.8 15.8 ...
## $ Gender : int 1 1 0 1 0 0 1 1 1 0 ...
## $ Age : int 39 29 55 44 43 43 54 40 26 44 ...
## $ NatVent_night : int 1 1 0 0 1 1 1 1 1 1 ...
## $ UsesCoolingAlternatives : int 0 0 0 0 1 1 0 0 1 0 ...
## $ hw_True : int 0 0 0 1 0 1 0 1 1 0 ...
## $ cte_23_7h_True : int 0 0 0 0 0 0 0 0 0 1 ...
## $ cte_7_15h_True : int 1 1 1 0 0 1 0 0 0 0 ...
## $ cte_15_23h_True : int 0 0 0 1 1 0 1 1 1 0 ...
## $ ShadingDevices_No : int 0 0 1 0 0 0 0 0 0 0 ...
## $ ShadingDevices_WhenDirectSun : int 0 0 0 0 0 0 0 0 1 1 ...
## $ ShadingDevices_AllAfternoon : int 0 0 0 0 0 1 0 0 0 0 ...
## $ ShadingDevices_AllMorning : int 0 0 0 0 0 0 0 0 0 0 ...
## $ ShadingDevices_AllDay : int 1 1 0 1 1 0 1 1 0 0 ...
## $ NatVent_day_ToutCool : int 1 1 1 1 1 1 1 1 1 1 ...
## $ NatVent_day_Anytime : int 0 0 0 0 0 0 0 0 0 0 ...
## $ NatVent_day_No : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hasAC_No : int 1 1 1 0 1 1 1 0 1 1 ...
## $ hasAC_DormOrLiving : int 0 0 0 1 0 0 0 1 0 0 ...
## $ hasAC_DormAndLiving : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hasAC_AllRooms : int 0 0 0 0 0 0 0 0 0 0 ...
## $ HouseholdSize : int 2 3 4 4 2 3 2 2 5 6 ...
## $ is_31001 : int 0 0 0 0 0 0 1 0 0 0 ...
## $ is_31002 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31003 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31004 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31005 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31006 : int 0 0 0 0 0 0 0 0 1 0 ...
## $ is_31007 : int 0 1 0 0 0 0 0 0 0 0 ...
## $ is_31008 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ is_31009 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31010 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31011 : int 0 0 0 0 0 0 0 1 0 0 ...
## $ is_31012 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31013 : int 0 0 0 0 1 0 0 0 0 0 ...
## $ is_31014 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31015 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ is_31016 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ isNot_pamplona : int 0 0 0 1 0 1 0 0 0 1 ...
## $ before_1980 : int 0 0 1 0 0 0 1 1 0 0 ...
## $ between_1980_2006 : int 1 1 0 1 1 0 0 0 0 1 ...
## $ after_2007 : int 0 0 0 0 0 1 0 0 1 0 ...
## $ dwelling_OldTown : int 0 0 0 0 0 0 1 0 0 0 ...
## $ dwelling_Block : int 1 1 0 1 0 0 0 1 1 0 ...
## $ dwelling_Tower : int 0 0 1 0 1 1 0 0 0 0 ...
## $ dwelling_Detached : int 0 0 0 0 0 0 0 0 0 1 ...
## $ dwelling_Other : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Rehab_No : int 1 0 0 0 0 0 0 1 1 1 ...
## $ Rehab_Yes : int 0 1 1 1 1 1 1 0 0 0 ...
## $ Income_below1500 : int 0 0 0 1 1 1 0 0 0 0 ...
## $ Income_between_1500_3500 : int 0 1 0 0 0 0 1 0 0 0 ...
## $ Income_above_3500 : int 1 0 1 0 0 0 0 1 1 1 ...
## $ Occ_NormallyAtHome : int 1 0 0 0 0 0 1 0 0 1 ...
## $ Occ_NotAlwaysAtHome : int 0 1 1 1 1 1 1 1 1 1 ...
## $ SrfcArea_below90 : int 0 1 0 1 1 0 1 0 0 0 ...
## $ Storey_UpperFloor : int 0 0 0 0 1 0 0 0 0 0 ...
## $ Storey_NotApartment : int 0 0 0 0 0 0 0 0 0 1 ...
## $ numOrient_1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numOrient_above2 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ LivRoom_SrfcAreaWindow_below2: int 0 0 0 0 0 1 0 0 0 0 ...
## $ LivRoom_SrfcAreaWindow_above2: int 1 1 1 1 1 0 1 1 1 1 ...
## $ Bedroom_SrfcAreaWindow_below2: int 0 0 0 0 1 1 1 0 0 1 ...
## $ Bedroom_SrfcAreaWindow_above2: int 1 1 1 1 0 0 0 1 1 0 ...
## $ AC_Installed_Yes : int 0 0 0 0 0 0 0 1 0 0 ...
## $ WouldYouInstallAC_Yes : int 0 1 0 0 0 0 1 0 1 1 ...
## $ hasCoolRoom : int 1 0 0 1 0 0 0 1 0 1 ...
## $ hasCoolingAlternatives : int 1 1 0 1 1 1 1 1 1 1 ...
X_matrix <- as.matrix(X)
sum(Y) ## [1] 113
head(cor(X_matrix),3)## Warning in cor(X_matrix): La desviación estándar es cero
## meanTout Gender Age NatVent_night UsesCoolingAlternatives
## meanTout 1.0000000 0.1307767 -0.1101982 -0.04064627 0.06632550
## Gender 0.1307767 1.0000000 -0.1423769 0.03327403 -0.02237790
## Age -0.1101982 -0.1423769 1.0000000 -0.01101263 -0.06230988
## hw_True cte_23_7h_True cte_7_15h_True cte_15_23h_True
## meanTout 0.64636441 -0.10384495 -0.02166472 0.079502103
## Gender 0.13105838 0.10334686 -0.04560925 -0.009504714
## Age -0.04154947 -0.07961135 -0.01481895 0.059094025
## ShadingDevices_No ShadingDevices_WhenDirectSun
## meanTout -0.02250576 -0.072160114
## Gender 0.01119659 -0.079213768
## Age -0.18236903 0.009446952
## ShadingDevices_AllAfternoon ShadingDevices_AllMorning
## meanTout 0.01048686 -0.042545055
## Gender -0.10825968 -0.005800514
## Age -0.02431157 0.046055713
## ShadingDevices_AllDay NatVent_day_ToutCool NatVent_day_Anytime
## meanTout 0.08698008 -0.122249775 0.072688735
## Gender 0.08865921 0.014818372 0.006207313
## Age 0.05592127 -0.006389317 -0.001543991
## NatVent_day_No hasAC_No hasAC_DormOrLiving hasAC_DormAndLiving
## meanTout 0.09741886 -0.03281046 0.060098794 -0.01822256
## Gender -0.02850635 -0.02710528 0.002625832 0.08555758
## Age 0.01103213 -0.07684401 0.102986999 0.01915430
## hasAC_AllRooms HouseholdSize is_31001 is_31002 is_31003
## meanTout -0.008530845 -0.14867237 -0.09489291 0.051096534 -0.09636606
## Gender -0.010212858 0.04247325 -0.01021286 -0.005800514 0.04913324
## Age -0.011384744 0.12336614 -0.04521677 0.199778091 0.05513891
## is_31004 is_31005 is_31006 is_31007 is_31008 is_31009
## meanTout -0.03016554 0.051096534 0.07067259 0.09732405 0.118293314 NA
## Gender -0.02850635 -0.005800514 0.03089417 -0.09789402 -0.120300727 NA
## Age 0.09214483 -0.103823607 -0.20485991 -0.02857215 0.008472426 NA
## is_31010 is_31011 is_31012 is_31013 is_31014
## meanTout -0.19012245 -0.09273633 4.015141e-02 0.005229221 0.07663515
## Gender -0.04192035 0.02883180 -5.800514e-03 -0.008270131 0.08555758
## Age 0.05471559 0.05316732 -6.100094e-05 0.005392302 -0.03464854
## is_31015 is_31016 isNot_pamplona before_1980 between_1980_2006
## meanTout -0.08237367 0.08156518 -0.08957000 -0.04364346 0.04018246
## Gender -0.03124543 0.08491892 0.08158191 -0.05416186 -0.01231625
## Age 0.02585977 -0.07008056 0.08321341 0.06668020 0.12622650
## after_2007 dwelling_OldTown dwelling_Block dwelling_Tower
## meanTout 0.0004299108 -0.08371100 0.19635348 -0.10222558
## Gender 0.0645917881 -0.02761085 0.01971012 -0.06356862
## Age -0.1939338014 -0.05209269 -0.14112419 0.07572906
## dwelling_Detached dwelling_Other Rehab_No Rehab_Yes
## meanTout -0.10603402 NA -0.04049546 0.04049546
## Gender 0.04461155 NA -0.03470942 0.03470942
## Age 0.14058671 NA -0.02336132 0.02336132
## Income_below1500 Income_between_1500_3500 Income_above_3500
## meanTout 0.06066255 -0.11024342 0.03213456
## Gender 0.09010932 -0.05123298 -0.04078845
## Age -0.12362216 -0.14090091 0.22209714
## Occ_NormallyAtHome Occ_NotAlwaysAtHome SrfcArea_below90
## meanTout -0.14143557 0.07888693 -0.01742477
## Gender -0.01791303 -0.04378727 0.08630280
## Age 0.11047324 -0.08848730 -0.18775747
## Storey_UpperFloor Storey_NotApartment numOrient_1 numOrient_above2
## meanTout -0.01502230 -0.14237088 -0.07956716 0.07956716
## Gender -0.10938933 0.03600155 0.03089417 -0.03089417
## Age -0.03937537 0.14410453 0.03477198 -0.03477198
## LivRoom_SrfcAreaWindow_below2 LivRoom_SrfcAreaWindow_above2
## meanTout 0.08393268 -0.08393268
## Gender 0.06297808 -0.06297808
## Age -0.18178387 0.18178387
## Bedroom_SrfcAreaWindow_below2 Bedroom_SrfcAreaWindow_above2
## meanTout -0.16374220 0.16374220
## Gender -0.01941129 0.01941129
## Age -0.09087218 0.09087218
## AC_Installed_Yes WouldYouInstallAC_Yes hasCoolRoom
## meanTout 0.06000455 -0.11085214 0.120666791
## Gender 0.04661511 0.01688017 0.079648752
## Age 0.08765250 -0.13133360 -0.004316685
## hasCoolingAlternatives
## meanTout -0.09345654
## Gender 0.04218148
## Age -0.06412320
For preventing linear dependencies in the input matrix. We modify x so and remove similar columns.
columns_to_remove <- c("is_31009", "dwelling_Other")columns_to_keep <- !(colnames(X_matrix) %in% columns_to_remove)X_matrix <- X_matrix[, columns_to_keep]X_matrix <- scale(X_matrix)library(glmnet) # Install and load glmnet package# Example using cross-validation to find the best lambda with cv = 5
cv_model <- cv.glmnet(X_matrix, Y, alpha = 1, family = "binomial", nfolds = 5, type.measure = "class")
plot(cv_model)
# Find the alpha and lambda with the minimum mean cross-validated error
best_lambda <- cv_model$lambda.min
best_lambda## [1] 0.05266225
best_model <- glmnet(X_matrix, Y, alpha = 1, lambda = best_lambda, family = "binomial")
coef(best_model, s = best_lambda)## 66 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 0.41614486
## meanTout .
## Gender 0.02864172
## Age .
## NatVent_night .
## UsesCoolingAlternatives 0.17456155
## hw_True 0.40843268
## cte_23_7h_True .
## cte_7_15h_True .
## cte_15_23h_True .
## ShadingDevices_No .
## ShadingDevices_WhenDirectSun .
## ShadingDevices_AllAfternoon .
## ShadingDevices_AllMorning .
## ShadingDevices_AllDay .
## NatVent_day_ToutCool .
## NatVent_day_Anytime .
## NatVent_day_No .
## hasAC_No .
## hasAC_DormOrLiving .
## hasAC_DormAndLiving .
## hasAC_AllRooms -0.21428389
## HouseholdSize 0.06018011
## is_31001 .
## is_31002 .
## is_31003 .
## is_31004 .
## is_31005 .
## is_31006 .
## is_31007 .
## is_31008 .
## is_31010 .
## is_31011 .
## is_31012 .
## is_31013 0.04436435
## is_31014 .
## is_31015 .
## is_31016 .
## isNot_pamplona .
## before_1980 .
## between_1980_2006 .
## after_2007 .
## dwelling_OldTown .
## dwelling_Block .
## dwelling_Tower .
## dwelling_Detached .
## Rehab_No .
## Rehab_Yes .
## Income_below1500 .
## Income_between_1500_3500 .
## Income_above_3500 .
## Occ_NormallyAtHome .
## Occ_NotAlwaysAtHome .
## SrfcArea_below90 .
## Storey_UpperFloor .
## Storey_NotApartment .
## numOrient_1 .
## numOrient_above2 .
## LivRoom_SrfcAreaWindow_below2 .
## LivRoom_SrfcAreaWindow_above2 .
## Bedroom_SrfcAreaWindow_below2 .
## Bedroom_SrfcAreaWindow_above2 .
## AC_Installed_Yes .
## WouldYouInstallAC_Yes .
## hasCoolRoom -0.02924841
## hasCoolingAlternatives .
library(selectiveInference)# extract coef for a given lambda; note the 1/n factor!
# (and here we DO include the intercept term)
beta = coef(best_model, x=X_matrix, y=Y, s = best_lambda/n)# compute fixed lambda p-values and selection intervals
model = fixedLassoInf(X_matrix,Y,beta,best_lambda,family="binomial")## Warning in fixedLogitLassoInf(x, y, beta, lambda, alpha = alpha, type = type, :
## Solution beta does not satisfy the KKT conditions (to within specified
## tolerances)
model##
## Call:
## fixedLassoInf(x = X_matrix, y = Y, beta = beta, lambda = best_lambda,
## family = "binomial")
##
## Testing results at lambda = 0.053, with alpha = 0.100
##
## Var Coef Z-score P-value LowConfPt UpConfPt LowTailArea UpTailArea
## 2 0.240 1.552 0.703 -2.267 0.386 0.050 0.049
## 5 0.342 2.156 0.107 -0.125 0.659 0.050 0.049
## 6 0.685 4.413 0.002 0.341 1.047 0.049 0.049
## 21 -0.458 -2.913 0.030 -0.717 -0.066 0.048 0.049
## 22 0.389 2.458 0.371 -0.926 0.603 0.000 0.049
## 34 0.259 1.568 0.603 -1.598 0.461 0.050 0.050
## 64 -0.322 -2.037 0.650 -0.472 2.238 0.049 0.050
##
## Note: coefficients shown are full regression coefficients