Exploratory analysis
Graph Vaccination Intention
covid2[,60:117] <- as.numeric((unlist(covid2[,60:117])))
# NA removal
covid2 <- covid2[!is.na(covid2$V87), ]
covid2 <- covid2[!is.na(covid2$V60), ]
covid2 <- covid2[!covid2$V23==17, ]
covid2 <- covid2[!is.na(covid2$V88), ]
# children vaccination intention
covid2[,56:59] <- as.numeric((unlist(covid2[,56:59])))
covid2%>%
mutate(intention_children = rowSums(cbind(V57, V58, V59), na.rm=T)) ->covid2
# vaccination col to long format
longdata2 = covid2 %>%
gather("V56", "intention_children", key = Vaccination, value = Intention)
# rename value own & children
longdata2$Vaccination[longdata2$Vaccination=="V56"]<-"self"
longdata2$Vaccination[longdata2$Vaccination=="intention_children"]<-"children"
# group by Intention & Vaccination
longdata2 %>%
dplyr::group_by(Intention, Vaccination) %>%
summarise(Frequency = dplyr::n()) -> graph_mutate2
graph_mutate2$Frequency <- graph_mutate2$Frequency/649*100
# graph vaccination intention
graph_mutate2 %>%
mutate(Vaccination = as.factor(Vaccination)) %>%
ggplot(aes(x = Intention, y=Frequency, fill = Vaccination)) +
geom_bar(stat="identity", position=position_dodge()) + labs(x = "Intention to vaccinate", y= " Relative frequency (%)") +
scale_x_discrete(expand = c(0, 0), name ="Intention of having the COVID-19 vaccine",
limits=c("1","2","3","4","5","6","7")) + geom_text(aes(label=sprintf("%1.1f%%", Frequency)), position = position_dodge(0.9),
vjust = -0.1, size = 3) + scale_y_continuous(expand = c(0, 0), breaks = c(0,10,20,30,40,50,60,70))+
coord_cartesian(ylim=c(0, 70)) + theme_bw() +
theme_minimal() + theme(panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),panel.grid.major.x = element_blank() ) + theme(axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid')) +
theme(text=element_text(family="Times New Roman", size=12)) + theme(axis.text = element_text(size = 12),
axis.text.y = element_text(color = "black"), axis.text.x = element_text(color = "black")) + theme(plot.margin = unit(c(1,3,1,1), "cm")) + scale_fill_grey() + theme(panel.grid.major = element_blank())
Graph Vaccination Concerns
covid2[,118:123]<- as.numeric(unlist(covid2[,118:123]))
# matrix concerns_vaccine
concerns_vaccine <- matrix(c(sum(covid2$V119, na.rm = T), sum(covid2$V118, na.rm = T), sum(covid2$V120, na.rm = T), sum(covid2$V121, na.rm = T), sum(covid2$V122, na.rm = T)), ncol=5, byrow=TRUE)
# data_concerns to data.frame
data_concerns <-as.data.frame(concerns_vaccine)
# concerns to long format
longdata4 = data_concerns %>%
gather("V1", "V2", "V3","V4","V5", key = Concerns, value = Frequency)
longdata4$Frequency <- round(longdata4$Frequency/649*100, digits = 1)
# revalue concerns
longdata4$Concerns[longdata4$Concerns=="V1"]<-"record time"
longdata4$Concerns[longdata4$Concerns=="V2"]<-"side effects"
longdata4$Concerns[longdata4$Concerns=="V3"]<-"risk group"
longdata4$Concerns[longdata4$Concerns=="V4"]<-"ineffective"
longdata4$Concerns[longdata4$Concerns=="V5"]<-"microchip"
#concerns as factor
longdata4$Concerns<-as.factor(longdata4$Concerns)
longdata4$Concerns <- factor(c("record time", "side effects", "risk group", "ineffective", "microchip"),
levels = c("record time", "side effects", "risk group", "ineffective", "microchip"))
# concerns graph
ggplot(data=longdata4, aes(x=Concerns, y=Frequency)) +
geom_bar(stat="identity")+ coord_cartesian(ylim = c(0, 70))+
theme_minimal() + theme(panel.grid.minor = element_blank()) +
scale_y_continuous(expand = c(0, 0), breaks = c(0,10,20,30,40,50,60,70))+
coord_cartesian(ylim=c(0, 70)) + theme(panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank() ) + theme(axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid')) +
geom_text(aes(label=sprintf("%1.1f%%", Frequency)), position = position_dodge(0.9),
vjust = -0.1, size = 3) + labs(x = "Concerns", y= " Relative frequency (%)") +
scale_x_discrete(expand = c(0, 0)) + theme(plot.margin = unit(c(5, 5, 5, 5), "mm")) +
theme(text=element_text(family="Times New Roman", size=12)) + theme(panel.grid.major = element_blank())
Coding variables
covid2$V2<-as.numeric(unlist(covid2$V2))
covid2$V21[covid2$V21==3]<-"male"
covid2$V21[covid2$V21==4]<-"female"
covid2$V21[covid2$V21==5]<- NA
covid2$V21<-as.factor(covid2$V21)
covid2$V23<-as.numeric(unlist(covid2$V23))
covid2$V25[covid2$V25==1]<-"aaprimary/basic"
covid2$V25[covid2$V25==2]<-"aaprimary/basic"
covid2$V25[covid2$V25==3]<-"aaprimary/basic"
covid2$V25[covid2$V25==4]<-"high school"
covid2$V25[covid2$V25==5]<-"academic"
covid2$V25[covid2$V25==6]<-"academic"
covid2$V25[covid2$V25==7]<-"academic"
covid2$V25[covid2$V25==8]<- NA
covid2$V25<-as.factor(covid2$V25)
covid2$V27[covid2$V27==1]<-"rural"
covid2$V27[covid2$V27==2]<-"urban"
covid2$V27<-as.factor(covid2$V27)
covid2$V28[covid2$V28==1]<-"yes"
covid2$V28[covid2$V28==2]<-"no"
covid2$V28<-as.factor(covid2$V28)
covid2$V29<-as.numeric(unlist(covid2$V29))
covid2$V30[covid2$V30==1]<-"working"
covid2$V30[covid2$V30==2]<-"unemployed"
covid2$V30[covid2$V30==3]<-"retired"
covid2$V30[covid2$V30==5]<-"student"
covid2$V30[covid2$V30==6]<-"working"
covid2$V30[covid2$V30==7]<- NA
covid2$V30<-as.factor(covid2$V30)
covid2$V32[covid2$V32==1]<-"yes"
covid2$V32[covid2$V32==2]<-"no"
covid2$V32<-as.factor(covid2$V32)
covid2$V48<-as.numeric(unlist(covid2$V48))
covid2$V49[covid2$V49==1]<-"yes"
covid2$V49[covid2$V49==2]<-"no"
covid2$V49<-as.factor(covid2$V49)
covid2$V51<-as.numeric(unlist(covid2$V51))
covid2$V54<-as.numeric(unlist(covid2$V54))
covid2$V55[covid2$V55==1]<-"no"
covid2$V55[covid2$V55==2]<-"no"
covid2$V55[covid2$V55==3]<-"yes"
covid2$V55[covid2$V55==4]<-"yes"
covid2$V55<-as.factor(covid2$V55)
Dimensionality reduction - Scales
PCA COVID-19 scale
pca <- PCA(covid2[,60:67], graph = F)
get_eig(pca)
## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 2.4022356 30.027945 30.02795
## Dim.2 1.4786966 18.483707 48.51165
## Dim.3 1.0151921 12.689901 61.20155
## Dim.4 0.8439997 10.549996 71.75155
## Dim.5 0.7378624 9.223280 80.97483
## Dim.6 0.6671026 8.338782 89.31361
## Dim.7 0.5906292 7.382865 96.69648
## Dim.8 0.2642818 3.303523 100.00000
fviz_screeplot(pca, addlabels = TRUE, ylim = c(0, 50))
var <- get_pca_var(pca)
fviz_pca_var(pca, col.var="contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE
)
summary(pca,nbelements = 10, ncp = 3)
##
## Call:
## PCA(X = covid2[, 60:67], graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 2.402 1.479 1.015 0.844 0.738 0.667 0.591
## % of var. 30.028 18.484 12.690 10.550 9.223 8.339 7.383
## Cumulative % of var. 30.028 48.512 61.202 71.752 80.975 89.314 96.696
## Dim.8
## Variance 0.264
## % of var. 3.304
## Cumulative % of var. 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 2 | 2.410 | 0.962 0.059 0.159 | 1.987 0.412 0.680 | 0.821 0.102
## 3 | 2.898 | 2.245 0.323 0.600 | 0.080 0.001 0.001 | -0.168 0.004
## 6 | 2.534 | 0.021 0.000 0.000 | 1.761 0.323 0.483 | 0.324 0.016
## 9 | 2.014 | 1.298 0.108 0.415 | -0.561 0.033 0.078 | 0.874 0.116
## 10 | 2.797 | 2.201 0.311 0.619 | 0.857 0.077 0.094 | -1.059 0.170
## 11 | 4.853 | -1.447 0.134 0.089 | 0.650 0.044 0.018 | -3.941 2.357
## 12 | 2.563 | -1.258 0.102 0.241 | -0.668 0.046 0.068 | 0.541 0.044
## 13 | 2.096 | 1.984 0.253 0.896 | -0.308 0.010 0.022 | -0.002 0.000
## 14 | 2.669 | 1.256 0.101 0.221 | 0.437 0.020 0.027 | -1.715 0.446
## 15 | 3.277 | -0.626 0.025 0.037 | 2.252 0.528 0.472 | -1.235 0.232
## cos2
## 2 0.116 |
## 3 0.003 |
## 6 0.016 |
## 9 0.188 |
## 10 0.143 |
## 11 0.659 |
## 12 0.045 |
## 13 0.000 |
## 14 0.413 |
## 15 0.142 |
##
## Variables
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## V60 | 0.621 16.064 0.386 | -0.394 10.482 0.155 | 0.138 1.868 0.019 |
## V61 | -0.340 4.820 0.116 | 0.502 17.034 0.252 | 0.275 7.434 0.075 |
## V62 | -0.628 16.400 0.394 | 0.184 2.284 0.034 | 0.259 6.607 0.067 |
## V63 | 0.622 16.090 0.387 | 0.012 0.010 0.000 | -0.156 2.399 0.024 |
## V64 | -0.441 8.101 0.195 | 0.504 17.184 0.254 | 0.028 0.075 0.001 |
## V65 | 0.163 1.110 0.027 | -0.288 5.592 0.083 | 0.898 79.501 0.807 |
## V66 | 0.689 19.781 0.475 | 0.574 22.298 0.330 | 0.131 1.697 0.017 |
## V67 | 0.651 17.634 0.424 | 0.609 25.116 0.371 | 0.065 0.419 0.004 |
CFA COVID-19 scale
model<-'
covid_threat=~V60 + V62 + V63 + V64
trust=~V66 + V67
covid_impact=~V65 + V61'
fit<- cfa(model, data=covid2)
summary(fit, fit.measures=T,standardized=T)
## lavaan 0.6-8 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 19
##
## Number of observations 649
##
## Model Test User Model:
##
## Test statistic 72.992
## Degrees of freedom 17
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 943.049
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.939
## Tucker-Lewis Index (TLI) 0.899
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8838.469
## Loglikelihood unrestricted model (H1) -8801.973
##
## Akaike (AIC) 17714.938
## Bayesian (BIC) 17799.971
## Sample-size adjusted Bayesian (BIC) 17739.647
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.071
## 90 Percent confidence interval - lower 0.055
## 90 Percent confidence interval - upper 0.088
## P-value RMSEA <= 0.05 0.017
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.050
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## covid_threat =~
## V60 1.000 0.870 0.618
## V62 -1.182 0.128 -9.200 0.000 -1.028 -0.557
## V63 0.458 0.053 8.706 0.000 0.399 0.504
## V64 -0.683 0.087 -7.822 0.000 -0.594 -0.430
## trust =~
## V66 1.000 1.471 0.903
## V67 0.994 0.104 9.574 0.000 1.462 0.805
## covid_impact =~
## V65 1.000 0.161 0.121
## V61 -2.780 0.871 -3.193 0.001 -0.448 -0.266
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## covid_threat ~~
## trust 0.522 0.079 6.612 0.000 0.408 0.408
## covid_impact 0.196 0.058 3.358 0.001 1.401 1.401
## trust ~~
## covid_impact 0.029 0.036 0.813 0.416 0.122 0.122
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V60 1.223 0.099 12.402 0.000 1.223 0.618
## .V62 2.343 0.168 13.928 0.000 2.343 0.689
## .V63 0.467 0.031 14.954 0.000 0.467 0.746
## .V64 1.558 0.097 15.994 0.000 1.558 0.815
## .V66 0.488 0.217 2.250 0.024 0.488 0.184
## .V67 1.159 0.222 5.216 0.000 1.159 0.352
## .V65 1.760 0.102 17.215 0.000 1.760 0.985
## .V61 2.633 0.275 9.584 0.000 2.633 0.929
## covid_threat 0.757 0.112 6.744 0.000 1.000 1.000
## trust 2.163 0.259 8.337 0.000 1.000 1.000
## covid_impact 0.026 0.034 0.753 0.451 1.000 1.000
coding COVID-19 scale dimensions
covid2 %>%
rowwise %>%
mutate(covid_threat = mean(c(V60, 8-V62, V63, 8-V64))) %>%
mutate(trust = mean(c(V66, V67))) -> covid2
covid2 %>%
rowwise %>%
mutate(covid_impact = mean(c(V65, 8-V61))) ->covid2
alpha COVID-19 global
alpha_covid_threat <- covid2 %>% select(V60, V62, V63, V64, V66, V67, V65, V61)
alpha_covid_threat$V62<-8-alpha_covid_threat$V62
alpha_covid_threat$V64<-8-alpha_covid_threat$V64
alpha_covid_threat$V66<-8-alpha_covid_threat$V66
alpha_covid_threat$V67<-8-alpha_covid_threat$V67
alpha_covid_threat$V61<-8-alpha_covid_threat$V61
cronbach(alpha_covid_threat)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 8
##
## $alpha
## [1] 0.3665474
alpha COVID-19 covid_threat
alpha_covid_threat <- covid2 %>% select(V60, V62, V63, V64)
alpha_covid_threat$V62<-8-alpha_covid_threat$V62
alpha_covid_threat$V64<-8-alpha_covid_threat$V64
cronbach(alpha_covid_threat)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 4
##
## $alpha
## [1] 0.5773531
alpha COVID-19 covid_trust
alpha_covid_trust <- covid2 %>% select(V66, V67)
cronbach(alpha_covid_trust)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 2
##
## $alpha
## [1] 0.8391796
alpha COVID-19 global
alpha_covid_impact <- covid2 %>% select(V65, V61)
alpha_covid_impact$V61<-8-alpha_covid_impact$V61
cronbach(alpha_covid_impact)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 2
##
## $alpha
## [1] 0.06063101
PCA vaccine scale
pca <- PCA(covid2[,68:87], graph=F)
get_eig(pca)
## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 6.3960192 31.9800962 31.98010
## Dim.2 1.8936801 9.4684003 41.44850
## Dim.3 1.4234872 7.1174359 48.56593
## Dim.4 1.3466117 6.7330584 55.29899
## Dim.5 1.0354248 5.1771240 60.47611
## Dim.6 0.9656257 4.8281285 65.30424
## Dim.7 0.8570011 4.2850055 69.58925
## Dim.8 0.7902319 3.9511593 73.54041
## Dim.9 0.7011004 3.5055019 77.04591
## Dim.10 0.6092650 3.0463248 80.09223
## Dim.11 0.5837526 2.9187630 83.01100
## Dim.12 0.5391384 2.6956922 85.70669
## Dim.13 0.4825333 2.4126663 88.11936
## Dim.14 0.4691926 2.3459632 90.46532
## Dim.15 0.4256310 2.1281551 92.59347
## Dim.16 0.4089712 2.0448559 94.63833
## Dim.17 0.3244820 1.6224102 96.26074
## Dim.18 0.2897644 1.4488218 97.70956
## Dim.19 0.2722502 1.3612509 99.07081
## Dim.20 0.1858373 0.9291866 100.00000
fviz_screeplot(pca, addlabels = TRUE, ylim = c(0, 50))
var <- get_pca_var(pca)
fviz_pca_var(pca, col.var="contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE
)
summary(pca,nbelements = 20, ncp = 4)
##
## Call:
## PCA(X = covid2[, 68:87], graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 6.396 1.894 1.423 1.347 1.035 0.966 0.857
## % of var. 31.980 9.468 7.117 6.733 5.177 4.828 4.285
## Cumulative % of var. 31.980 41.448 48.566 55.299 60.476 65.304 69.589
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 0.790 0.701 0.609 0.584 0.539 0.483 0.469
## % of var. 3.951 3.506 3.046 2.919 2.696 2.413 2.346
## Cumulative % of var. 73.540 77.046 80.092 83.011 85.707 88.119 90.465
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20
## Variance 0.426 0.409 0.324 0.290 0.272 0.186
## % of var. 2.128 2.045 1.622 1.449 1.361 0.929
## Cumulative % of var. 92.593 94.638 96.261 97.710 99.071 100.000
##
## Individuals (the 20 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 1 | 5.292 | 1.787 0.077 0.114 | 3.377 0.928 0.407 | 2.440 0.644
## 2 | 4.203 | 2.372 0.136 0.319 | -1.446 0.170 0.118 | -0.501 0.027
## 3 | 3.360 | 1.198 0.035 0.127 | 0.110 0.001 0.001 | 0.768 0.064
## 4 | 3.123 | 1.889 0.086 0.366 | 0.531 0.023 0.029 | 0.366 0.015
## 5 | 4.338 | 3.657 0.322 0.711 | -0.270 0.006 0.004 | -0.217 0.005
## 6 | 4.078 | 0.754 0.014 0.034 | -2.206 0.396 0.293 | 0.199 0.004
## 7 | 5.580 | 0.017 0.000 0.000 | 3.688 1.106 0.437 | 0.583 0.037
## 8 | 5.202 | 1.084 0.028 0.043 | -2.256 0.414 0.188 | -0.204 0.005
## 9 | 4.732 | 2.626 0.166 0.308 | -0.896 0.065 0.036 | -1.582 0.271
## 10 | 2.844 | 1.322 0.042 0.216 | -0.412 0.014 0.021 | -0.622 0.042
## 11 | 7.244 | -5.577 0.749 0.593 | -0.302 0.007 0.002 | 0.785 0.067
## 12 | 4.293 | 3.330 0.267 0.602 | -1.004 0.082 0.055 | -0.375 0.015
## 13 | 3.013 | 2.099 0.106 0.486 | -1.691 0.233 0.315 | -0.189 0.004
## 14 | 3.558 | -0.195 0.001 0.003 | -0.502 0.021 0.020 | 0.058 0.000
## 15 | 1.461 | 0.281 0.002 0.037 | -0.127 0.001 0.008 | -0.162 0.003
## 16 | 3.008 | 1.891 0.086 0.395 | 0.397 0.013 0.017 | 1.300 0.183
## 17 | 3.915 | 2.040 0.100 0.272 | -1.635 0.217 0.174 | -0.128 0.002
## 18 | 2.650 | 2.241 0.121 0.716 | -0.042 0.000 0.000 | -0.366 0.014
## 19 | 3.728 | 2.591 0.162 0.483 | 1.159 0.109 0.097 | -0.520 0.029
## 20 | 5.483 | 0.480 0.006 0.008 | -0.435 0.015 0.006 | -0.193 0.004
## cos2 Dim.4 ctr cos2
## 1 0.213 | 1.627 0.303 0.094 |
## 2 0.014 | 0.621 0.044 0.022 |
## 3 0.052 | 0.410 0.019 0.015 |
## 4 0.014 | 1.309 0.196 0.176 |
## 5 0.002 | -0.900 0.093 0.043 |
## 6 0.002 | -1.106 0.140 0.074 |
## 7 0.011 | -1.145 0.150 0.042 |
## 8 0.002 | 0.059 0.000 0.000 |
## 9 0.112 | 0.514 0.030 0.012 |
## 10 0.048 | 0.692 0.055 0.059 |
## 11 0.012 | -0.308 0.011 0.002 |
## 12 0.008 | -0.018 0.000 0.000 |
## 13 0.004 | 0.377 0.016 0.016 |
## 14 0.000 | 0.844 0.082 0.056 |
## 15 0.012 | -0.272 0.008 0.035 |
## 16 0.187 | 0.306 0.011 0.010 |
## 17 0.001 | 1.255 0.180 0.103 |
## 18 0.019 | 0.307 0.011 0.013 |
## 19 0.019 | 0.151 0.003 0.002 |
## 20 0.001 | 0.753 0.065 0.019 |
##
## Variables
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## V68 | 0.547 4.677 0.299 | 0.103 0.564 0.011 | -0.062 0.272 0.004 |
## V69 | 0.482 3.631 0.232 | 0.265 3.713 0.070 | 0.203 2.904 0.041 |
## V70 | 0.645 6.498 0.416 | 0.332 5.831 0.110 | 0.261 4.777 0.068 |
## V71 | 0.633 6.262 0.401 | 0.230 2.801 0.053 | 0.058 0.239 0.003 |
## V72 | 0.743 8.634 0.552 | 0.032 0.055 0.001 | 0.016 0.018 0.000 |
## V73 | -0.489 3.732 0.239 | 0.125 0.826 0.016 | 0.107 0.797 0.011 |
## V74 | -0.568 5.039 0.322 | 0.393 8.173 0.155 | -0.001 0.000 0.000 |
## V75 | -0.585 5.342 0.342 | 0.410 8.863 0.168 | 0.053 0.194 0.003 |
## V76 | -0.798 9.948 0.636 | 0.223 2.624 0.050 | 0.014 0.015 0.000 |
## V77 | 0.279 1.219 0.078 | 0.214 2.414 0.046 | -0.258 4.659 0.066 |
## V78 | 0.455 3.237 0.207 | 0.238 3.000 0.057 | -0.233 3.816 0.054 |
## V79 | -0.101 0.158 0.010 | 0.383 7.741 0.147 | 0.547 21.020 0.299 |
## V80 | 0.382 2.280 0.146 | -0.605 19.314 0.366 | 0.427 12.803 0.182 |
## V81 | 0.477 3.564 0.228 | -0.578 17.632 0.334 | 0.421 12.457 0.177 |
## V82 | 0.649 6.585 0.421 | 0.118 0.736 0.014 | -0.220 3.402 0.048 |
## V83 | 0.637 6.344 0.406 | 0.093 0.454 0.009 | -0.218 3.347 0.048 |
## V84 | 0.616 5.928 0.379 | 0.245 3.183 0.060 | 0.006 0.002 0.000 |
## V85 | 0.780 9.524 0.609 | 0.179 1.687 0.032 | -0.087 0.537 0.008 |
## V86 | 0.655 6.700 0.429 | 0.248 3.235 0.061 | 0.235 3.892 0.055 |
## V87 | -0.211 0.699 0.045 | 0.368 7.155 0.135 | 0.595 24.847 0.354 |
## Dim.4 ctr cos2
## V68 -0.167 2.078 0.028 |
## V69 -0.073 0.392 0.005 |
## V70 -0.143 1.525 0.021 |
## V71 -0.139 1.444 0.019 |
## V72 -0.107 0.849 0.011 |
## V73 0.096 0.686 0.009 |
## V74 0.177 2.315 0.031 |
## V75 0.118 1.029 0.014 |
## V76 0.196 2.862 0.039 |
## V77 0.647 31.064 0.418 |
## V78 0.569 24.040 0.324 |
## V79 0.077 0.444 0.006 |
## V80 0.357 9.488 0.128 |
## V81 0.319 7.557 0.102 |
## V82 0.207 3.182 0.043 |
## V83 0.272 5.501 0.074 |
## V84 -0.121 1.094 0.015 |
## V85 -0.170 2.136 0.029 |
## V86 -0.039 0.113 0.002 |
## V87 0.172 2.201 0.030 |
CFA vaccine scale
model<-'
beliefs_attitudes=~V68+V69+V70+V71+V72+V73+V74+V75+V76+V82+V83+V84+V85+V86
others_intention=~V77+V78
knowledge=~V80+V81
return_to_normal=~V79+V87'
fit<- cfa(model, data=covid2)
summary(fit, fit.measures=T,standardized=T)
## lavaan 0.6-8 ended normally after 80 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 46
##
## Number of observations 649
##
## Model Test User Model:
##
## Test statistic 1055.864
## Degrees of freedom 164
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 5083.974
## Degrees of freedom 190
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.818
## Tucker-Lewis Index (TLI) 0.789
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -22394.985
## Loglikelihood unrestricted model (H1) -21867.054
##
## Akaike (AIC) 44881.971
## Bayesian (BIC) 45087.841
## Sample-size adjusted Bayesian (BIC) 44941.792
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.092
## 90 Percent confidence interval - lower 0.086
## 90 Percent confidence interval - upper 0.097
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.066
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## beliefs_attitudes =~
## V68 1.000 1.071 0.517
## V69 0.648 0.070 9.253 0.000 0.694 0.442
## V70 0.892 0.077 11.619 0.000 0.955 0.621
## V71 1.067 0.093 11.465 0.000 1.143 0.607
## V72 1.041 0.082 12.618 0.000 1.115 0.720
## V73 -0.725 0.077 -9.427 0.000 -0.777 -0.453
## V74 -0.969 0.092 -10.534 0.000 -1.038 -0.531
## V75 -0.959 0.090 -10.625 0.000 -1.027 -0.538
## V76 -1.394 0.106 -13.164 0.000 -1.493 -0.785
## V82 0.637 0.055 11.527 0.000 0.682 0.612
## V83 0.611 0.054 11.353 0.000 0.655 0.597
## V84 1.022 0.090 11.413 0.000 1.095 0.602
## V85 1.167 0.089 13.182 0.000 1.250 0.787
## V86 0.901 0.076 11.812 0.000 0.965 0.638
## others_intention =~
## V77 1.000 0.617 0.504
## V78 1.899 0.326 5.830 0.000 1.172 0.947
## knowledge =~
## V80 1.000 1.292 0.762
## V81 1.368 0.121 11.345 0.000 1.769 1.032
## return_to_normal =~
## V79 1.000 0.470 0.346
## V87 3.178 1.570 2.024 0.043 1.493 0.824
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## beliefs_attitudes ~~
## others_intentn 0.277 0.058 4.766 0.000 0.420 0.420
## knowledge 0.526 0.085 6.166 0.000 0.380 0.380
## return_to_nrml -0.117 0.060 -1.935 0.053 -0.232 -0.232
## others_intention ~~
## knowledge 0.111 0.039 2.879 0.004 0.139 0.139
## return_to_nrml -0.018 0.017 -1.082 0.279 -0.064 -0.064
## knowledge ~~
## return_to_nrml -0.051 0.037 -1.383 0.167 -0.084 -0.084
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V68 3.139 0.181 17.356 0.000 3.139 0.732
## .V69 1.981 0.113 17.577 0.000 1.981 0.805
## .V70 1.456 0.086 16.883 0.000 1.456 0.615
## .V71 2.239 0.132 16.962 0.000 2.239 0.632
## .V72 1.153 0.072 16.066 0.000 1.153 0.481
## .V73 2.332 0.133 17.548 0.000 2.332 0.795
## .V74 2.739 0.158 17.306 0.000 2.739 0.718
## .V75 2.589 0.150 17.279 0.000 2.589 0.710
## .V76 1.391 0.092 15.113 0.000 1.391 0.384
## .V82 0.775 0.046 16.931 0.000 0.775 0.625
## .V83 0.773 0.045 17.014 0.000 0.773 0.643
## .V84 2.105 0.124 16.987 0.000 2.105 0.637
## .V85 0.961 0.064 15.068 0.000 0.961 0.381
## .V86 1.354 0.081 16.773 0.000 1.354 0.592
## .V77 1.119 0.087 12.909 0.000 1.119 0.746
## .V78 0.157 0.218 0.718 0.473 0.157 0.102
## .V80 1.207 0.152 7.932 0.000 1.207 0.419
## .V81 -0.193 0.256 -0.755 0.450 -0.193 -0.066
## .V79 1.620 0.140 11.562 0.000 1.620 0.880
## .V87 1.053 1.087 0.969 0.333 1.053 0.321
## beliefs_atttds 1.147 0.169 6.785 0.000 1.000 1.000
## others_intentn 0.381 0.082 4.641 0.000 1.000 1.000
## knowledge 1.670 0.199 8.387 0.000 1.000 1.000
## return_to_nrml 0.221 0.118 1.871 0.061 1.000 1.000
coding vaccine scale dimensions
covid2 %>%
rowwise %>%
mutate(beliefs_attitudes = mean(c(V68, V69, V70, V71, V72, 8-V73, 8-V74, 8-V75, 8-V76, V82, V83, V84, V85, V86))) %>%
mutate(others_intention = mean(c(V77, V78))) %>%
mutate(knowledge = mean(c(V80, V81))) %>%
mutate(return_to_normal = mean(c(V79, V87))) -> covid2
alpha COVID-19 vaccine global
alpha_covid_beliefs <- covid2 %>% select(V68, V69, V70, V71, V72, V73, V74, V75, V76, V82, V83, V84, V85, V86, V77, V78, V80, V81, V79, V87)
alpha_covid_beliefs$V73<-8-alpha_covid_beliefs$V73
alpha_covid_beliefs$V74<-8-alpha_covid_beliefs$V74
alpha_covid_beliefs$V75<-8-alpha_covid_beliefs$V75
alpha_covid_beliefs$V76<-8-alpha_covid_beliefs$V76
cronbach(alpha_covid_beliefs)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 20
##
## $alpha
## [1] 0.8527662
alpha COVID-19 vaccine beliefs
alpha_covid_beliefs <- covid2 %>% select(V68, V69, V70, V71, V72, V73, V74, V75, V76, V82, V83, V84, V85, V86)
alpha_covid_beliefs$V73<-8-alpha_covid_beliefs$V73
alpha_covid_beliefs$V74<-8-alpha_covid_beliefs$V74
alpha_covid_beliefs$V75<-8-alpha_covid_beliefs$V75
alpha_covid_beliefs$V76<-8-alpha_covid_beliefs$V76
cronbach(alpha_covid_beliefs)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 14
##
## $alpha
## [1] 0.8824257
alpha COVID-19 others_intention
alpha_covid_other <- covid2 %>% select(V77, V78)
cronbach(alpha_covid_other)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 2
##
## $alpha
## [1] 0.6461679
alpha COVID-19 others_intention
alpha_covid_knowledge <- covid2 %>% select(V80, V81)
cronbach(alpha_covid_knowledge)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 2
##
## $alpha
## [1] 0.8805898
alpha COVID-19 others_intention
alpha_covid_return_normal <- covid2 %>% select(V79, V87)
cronbach(alpha_covid_return_normal)
## $sample.size
## [1] 649
##
## $number.of.items
## [1] 2
##
## $alpha
## [1] 0.429814
coding risk aversion scale
covid2 %>%
rowwise %>%
mutate(risk_aversion = mean(c(V88, V89, V90, V91, V92, V93, V94, V95, V96, V97, V98, V99, V100, V101, V102,V103, V104,V105, V106, V107,V108,V109,V110, V111,V112, V113,V114,V115, V116,V117)))->covid2
rename col
names(covid2)[2] <- "days_lockdown"
names(covid2)[21] <- "gender"
names(covid2)[23] <- "age"
names(covid2)[24] <- "nationality"
names(covid2)[25] <- "literacy"
names(covid2)[26] <- "age"
names(covid2)[27] <- "residency"
names(covid2)[28] <- "relationship"
names(covid2)[29] <- "socioeconomic"
names(covid2)[30] <- "professional_status"
names(covid2)[32] <- "have_children"
names(covid2)[48] <- "how_much_life_changed"
names(covid2)[49] <- "religious"
names(covid2)[50] <- "religion"
names(covid2)[51] <- "economic_changes"
names(covid2)[54] <- "covid-19_risk"
names(covid2)[55] <- "had_covid-19"
names(covid2)[56] <- "vaccination_intention"
covid2$socioeconomic[covid2$socioeconomic==1]<-"low"
covid2$socioeconomic[covid2$socioeconomic==2]<-"low"
covid2$socioeconomic[covid2$socioeconomic==3]<-"1medium"
covid2$socioeconomic[covid2$socioeconomic==4]<-"high"
covid2$socioeconomic[covid2$socioeconomic==5]<-"high"
covid2$socioeconomic<-as.factor(covid2$socioeconomic)
covid2 <- covid2[!is.na(covid2$risk_aversion), ]
Python
multiprocessing
sys <- import("sys")
exe <- file.path(sys$exec_prefix, "pythonw.exe")
sys$executable <- exe
sys$`_base_executable` <- exe
multiprocessing <- import("multiprocessing")
multiprocessing$set_executable(exe)
library
import pandas as pd
import seaborn as sns
import matplotlib as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn import svm
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
from sklearn import decomposition
from matplotlib import pyplot as plt
import plotly.express as px
import numpy as np
from sklearn.model_selection import cross_val_score, StratifiedKFold, KFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
import statsmodels.formula.api as smf
import textwrap
read & cleaning
vac = pd.read_csv(r"C:\Users\nunok\Documents\2021\Vacinação\Data\EXCEL\covid_15_03.csv",sep=',')
#col to numeric
vac_2 = vac.apply(pd.to_numeric, errors='coerce')
#drop NA
vac_2 = vac_2.dropna(subset=['Q70_30'])
vac_2 = vac_2.dropna(subset=['Q62_20'])
vac_2 = vac_2.dropna(subset=['Q56_1'])
vac_2 = vac_2[vac_2["Q4"]>17]
vac_2 = vac_2.dropna(subset=['Q54'])
Glm own&children~general beliefs and attitudes
# children vaccination
vac_2[['Q55','Q66','Q75']]=vac_2[['Q55','Q66','Q75']].fillna(0)
vac_2["children"] = vac_2["Q55"] + vac_2["Q66"] + vac_2["Q75"]
# select variables and target
vac_2= vac_2[['Q62_1','Q62_2','Q62_3','Q62_4','Q62_5','Q62_6','Q62_7','Q62_8','Q62_9','Q62_15','Q62_16','Q62_17','Q62_18','Q62_19','Q54',"children"]]
#global dataset
mod_own = smf.ols(formula='Q54 ~ Q62_1 + Q62_2 + Q62_3 + Q62_4 + Q62_5 + Q62_6 + Q62_7 + Q62_8 + Q62_9 + Q62_15 + Q62_16 + Q62_17 + Q62_18 + Q62_19', data=vac_2)
mod_children = smf.ols(formula='children ~ Q62_1 + Q62_2 + Q62_3 + Q62_4 + Q62_5 + Q62_6 + Q62_7 + Q62_8 + Q62_9 + Q62_15 + Q62_16 + Q62_17 + Q62_18 + Q62_19', data=vac_2)
res_own = mod_own.fit()
res_children = mod_children.fit()
res_own.summary()
## <class 'statsmodels.iolib.summary.Summary'>
## """
## OLS Regression Results
## ==============================================================================
## Dep. Variable: Q54 R-squared: 0.692
## Model: OLS Adj. R-squared: 0.686
## Method: Least Squares F-statistic: 101.9
## Date: Sat, 09 Oct 2021 Prob (F-statistic): 8.05e-152
## Time: 23:12:16 Log-Likelihood: -856.54
## No. Observations: 649 AIC: 1743.
## Df Residuals: 634 BIC: 1810.
## Df Model: 14
## Covariance Type: nonrobust
## ==============================================================================
## coef std err t P>|t| [0.025 0.975]
## ------------------------------------------------------------------------------
## Intercept 1.4101 0.360 3.922 0.000 0.704 2.116
## Q62_1 0.0889 0.021 4.295 0.000 0.048 0.130
## Q62_2 0.0114 0.027 0.423 0.672 -0.041 0.064
## Q62_3 0.0486 0.032 1.517 0.130 -0.014 0.112
## Q62_4 0.1188 0.024 4.861 0.000 0.071 0.167
## Q62_5 0.2562 0.032 7.941 0.000 0.193 0.320
## Q62_6 0.0119 0.024 0.499 0.618 -0.035 0.059
## Q62_7 0.0229 0.025 0.911 0.363 -0.027 0.072
## Q62_8 -0.0654 0.024 -2.702 0.007 -0.113 -0.018
## Q62_9 -0.1257 0.032 -3.949 0.000 -0.188 -0.063
## Q62_15 0.0016 0.046 0.035 0.972 -0.089 0.092
## Q62_16 0.0570 0.046 1.236 0.217 -0.034 0.148
## Q62_17 0.0419 0.025 1.645 0.100 -0.008 0.092
## Q62_18 0.2541 0.036 6.972 0.000 0.183 0.326
## Q62_19 0.0686 0.032 2.176 0.030 0.007 0.131
## ==============================================================================
## Omnibus: 70.546 Durbin-Watson: 1.951
## Prob(Omnibus): 0.000 Jarque-Bera (JB): 191.836
## Skew: -0.549 Prob(JB): 2.20e-42
## Kurtosis: 5.427 Cond. No. 185.
## ==============================================================================
##
## Notes:
## [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
## """
res_children.summary()
# predict & test subset
## <class 'statsmodels.iolib.summary.Summary'>
## """
## OLS Regression Results
## ==============================================================================
## Dep. Variable: children R-squared: 0.605
## Model: OLS Adj. R-squared: 0.597
## Method: Least Squares F-statistic: 69.44
## Date: Sat, 09 Oct 2021 Prob (F-statistic): 8.06e-118
## Time: 23:12:16 Log-Likelihood: -978.79
## No. Observations: 649 AIC: 1988.
## Df Residuals: 634 BIC: 2055.
## Df Model: 14
## Covariance Type: nonrobust
## ==============================================================================
## coef std err t P>|t| [0.025 0.975]
## ------------------------------------------------------------------------------
## Intercept 1.3309 0.434 3.066 0.002 0.479 2.183
## Q62_1 0.1424 0.025 5.699 0.000 0.093 0.191
## Q62_2 0.0079 0.032 0.242 0.809 -0.056 0.072
## Q62_3 0.0432 0.039 1.118 0.264 -0.033 0.119
## Q62_4 0.1284 0.029 4.353 0.000 0.070 0.186
## Q62_5 0.2153 0.039 5.527 0.000 0.139 0.292
## Q62_6 0.0103 0.029 0.358 0.720 -0.046 0.067
## Q62_7 0.0380 0.030 1.249 0.212 -0.022 0.098
## Q62_8 -0.1003 0.029 -3.431 0.001 -0.158 -0.043
## Q62_9 -0.1271 0.038 -3.309 0.001 -0.203 -0.052
## Q62_15 -0.0571 0.056 -1.023 0.307 -0.167 0.052
## Q62_16 0.1876 0.056 3.368 0.001 0.078 0.297
## Q62_17 -0.0012 0.031 -0.038 0.970 -0.062 0.059
## Q62_18 0.2242 0.044 5.094 0.000 0.138 0.311
## Q62_19 0.0555 0.038 1.458 0.145 -0.019 0.130
## ==============================================================================
## Omnibus: 98.186 Durbin-Watson: 2.035
## Prob(Omnibus): 0.000 Jarque-Bera (JB): 255.505
## Skew: -0.776 Prob(JB): 3.29e-56
## Kurtosis: 5.654 Cond. No. 185.
## ==============================================================================
##
## Notes:
## [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
## """
predict, validate = train_test_split(vac_2, test_size=0.2, random_state=42)
# predict
mod_own_discovery = smf.ols(formula='Q54 ~ Q62_1 + Q62_2 + Q62_3 + Q62_4 + Q62_5 + Q62_6 + Q62_7 + Q62_8 + Q62_9 + Q62_15 + Q62_16 + Q62_17 + Q62_18 + Q62_19', data=predict)
mod_children_discovery = smf.ols(formula='children ~ Q62_1 + Q62_2 + Q62_3 + Q62_4 + Q62_5 + Q62_6 + Q62_7 + Q62_8 + Q62_9 + Q62_15 + Q62_16 + Q62_17 + Q62_18 + Q62_19', data=predict)
res_own_discovery = mod_own_discovery.fit()
res_children_discovery = mod_children_discovery.fit()
res_own_discovery.summary()
## <class 'statsmodels.iolib.summary.Summary'>
## """
## OLS Regression Results
## ==============================================================================
## Dep. Variable: Q54 R-squared: 0.668
## Model: OLS Adj. R-squared: 0.658
## Method: Least Squares F-statistic: 72.34
## Date: Sat, 09 Oct 2021 Prob (F-statistic): 9.22e-111
## Time: 23:12:16 Log-Likelihood: -702.70
## No. Observations: 519 AIC: 1435.
## Df Residuals: 504 BIC: 1499.
## Df Model: 14
## Covariance Type: nonrobust
## ==============================================================================
## coef std err t P>|t| [0.025 0.975]
## ------------------------------------------------------------------------------
## Intercept 1.5191 0.416 3.655 0.000 0.703 2.336
## Q62_1 0.0878 0.024 3.661 0.000 0.041 0.135
## Q62_2 0.0151 0.032 0.472 0.637 -0.048 0.078
## Q62_3 0.0361 0.037 0.971 0.332 -0.037 0.109
## Q62_4 0.1211 0.028 4.335 0.000 0.066 0.176
## Q62_5 0.2287 0.037 6.202 0.000 0.156 0.301
## Q62_6 0.0008 0.027 0.029 0.977 -0.053 0.054
## Q62_7 0.0376 0.030 1.271 0.204 -0.020 0.096
## Q62_8 -0.0882 0.028 -3.096 0.002 -0.144 -0.032
## Q62_9 -0.1192 0.037 -3.229 0.001 -0.192 -0.047
## Q62_15 -0.0138 0.055 -0.253 0.800 -0.121 0.093
## Q62_16 0.0834 0.053 1.581 0.115 -0.020 0.187
## Q62_17 0.0379 0.029 1.287 0.199 -0.020 0.096
## Q62_18 0.2516 0.041 6.069 0.000 0.170 0.333
## Q62_19 0.0859 0.035 2.421 0.016 0.016 0.156
## ==============================================================================
## Omnibus: 63.963 Durbin-Watson: 2.012
## Prob(Omnibus): 0.000 Jarque-Bera (JB): 155.205
## Skew: -0.644 Prob(JB): 1.99e-34
## Kurtosis: 5.349 Cond. No. 185.
## ==============================================================================
##
## Notes:
## [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
## """
res_children_discovery.summary()
# validate
## <class 'statsmodels.iolib.summary.Summary'>
## """
## OLS Regression Results
## ==============================================================================
## Dep. Variable: children R-squared: 0.592
## Model: OLS Adj. R-squared: 0.580
## Method: Least Squares F-statistic: 52.18
## Date: Sat, 09 Oct 2021 Prob (F-statistic): 1.52e-88
## Time: 23:12:16 Log-Likelihood: -781.24
## No. Observations: 519 AIC: 1592.
## Df Residuals: 504 BIC: 1656.
## Df Model: 14
## Covariance Type: nonrobust
## ==============================================================================
## coef std err t P>|t| [0.025 0.975]
## ------------------------------------------------------------------------------
## Intercept 1.5483 0.483 3.202 0.001 0.598 2.498
## Q62_1 0.1323 0.028 4.746 0.000 0.078 0.187
## Q62_2 0.0326 0.037 0.875 0.382 -0.041 0.106
## Q62_3 0.0403 0.043 0.931 0.353 -0.045 0.125
## Q62_4 0.1197 0.033 3.682 0.000 0.056 0.184
## Q62_5 0.1665 0.043 3.880 0.000 0.082 0.251
## Q62_6 -0.0074 0.032 -0.235 0.814 -0.070 0.055
## Q62_7 0.0455 0.034 1.325 0.186 -0.022 0.113
## Q62_8 -0.0986 0.033 -2.977 0.003 -0.164 -0.034
## Q62_9 -0.1512 0.043 -3.519 0.000 -0.236 -0.067
## Q62_15 -0.0357 0.063 -0.561 0.575 -0.160 0.089
## Q62_16 0.2045 0.061 3.331 0.001 0.084 0.325
## Q62_17 -0.0174 0.034 -0.507 0.613 -0.085 0.050
## Q62_18 0.1952 0.048 4.046 0.000 0.100 0.290
## Q62_19 0.0864 0.041 2.093 0.037 0.005 0.167
## ==============================================================================
## Omnibus: 73.318 Durbin-Watson: 2.164
## Prob(Omnibus): 0.000 Jarque-Bera (JB): 173.041
## Skew: -0.743 Prob(JB): 2.66e-38
## Kurtosis: 5.407 Cond. No. 185.
## ==============================================================================
##
## Notes:
## [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
## """
mod_own_validation = smf.ols(formula='Q54 ~ Q62_1 + Q62_2 + Q62_3 + Q62_4 + Q62_5 + Q62_6 + Q62_7 + Q62_8 + Q62_9 + Q62_15 + Q62_16 + Q62_17 + Q62_18 + Q62_19', data=validate)
mod_children_validation = smf.ols(formula='children ~ Q62_1 + Q62_2 + Q62_3 + Q62_4 + Q62_5 + Q62_6 + Q62_7 + Q62_8 + Q62_9 + Q62_15 + Q62_16 + Q62_17 + Q62_18 + Q62_19', data=validate)
res_own_validation = mod_own_validation.fit()
res_children_validation = mod_children_validation.fit()
res_own_validation.summary()
## <class 'statsmodels.iolib.summary.Summary'>
## """
## OLS Regression Results
## ==============================================================================
## Dep. Variable: Q54 R-squared: 0.816
## Model: OLS Adj. R-squared: 0.793
## Method: Least Squares F-statistic: 36.39
## Date: Sat, 09 Oct 2021 Prob (F-statistic): 1.21e-35
## Time: 23:12:16 Log-Likelihood: -140.50
## No. Observations: 130 AIC: 311.0
## Df Residuals: 115 BIC: 354.0
## Df Model: 14
## Covariance Type: nonrobust
## ==============================================================================
## coef std err t P>|t| [0.025 0.975]
## ------------------------------------------------------------------------------
## Intercept 0.6612 0.710 0.932 0.353 -0.745 2.067
## Q62_1 0.0880 0.041 2.147 0.034 0.007 0.169
## Q62_2 0.0460 0.050 0.925 0.357 -0.053 0.145
## Q62_3 0.0908 0.062 1.466 0.145 -0.032 0.213
## Q62_4 0.0927 0.050 1.836 0.069 -0.007 0.193
## Q62_5 0.3559 0.069 5.159 0.000 0.219 0.493
## Q62_6 0.0864 0.049 1.751 0.083 -0.011 0.184
## Q62_7 -0.0526 0.049 -1.081 0.282 -0.149 0.044
## Q62_8 0.0267 0.045 0.594 0.554 -0.062 0.116
## Q62_9 -0.1399 0.062 -2.257 0.026 -0.263 -0.017
## Q62_15 0.0944 0.085 1.112 0.269 -0.074 0.263
## Q62_16 -0.0740 0.095 -0.776 0.439 -0.263 0.115
## Q62_17 0.0699 0.049 1.425 0.157 -0.027 0.167
## Q62_18 0.2914 0.079 3.708 0.000 0.136 0.447
## Q62_19 0.0065 0.071 0.091 0.928 -0.134 0.147
## ==============================================================================
## Omnibus: 6.765 Durbin-Watson: 2.094
## Prob(Omnibus): 0.034 Jarque-Bera (JB): 7.715
## Skew: 0.341 Prob(JB): 0.0211
## Kurtosis: 3.979 Cond. No. 197.
## ==============================================================================
##
## Notes:
## [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
## """
res_children_validation.summary()
## <class 'statsmodels.iolib.summary.Summary'>
## """
## OLS Regression Results
## ==============================================================================
## Dep. Variable: children R-squared: 0.702
## Model: OLS Adj. R-squared: 0.665
## Method: Least Squares F-statistic: 19.31
## Date: Sat, 09 Oct 2021 Prob (F-statistic): 5.70e-24
## Time: 23:12:16 Log-Likelihood: -187.10
## No. Observations: 130 AIC: 404.2
## Df Residuals: 115 BIC: 447.2
## Df Model: 14
## Covariance Type: nonrobust
## ==============================================================================
## coef std err t P>|t| [0.025 0.975]
## ------------------------------------------------------------------------------
## Intercept 0.3342 1.016 0.329 0.743 -1.678 2.346
## Q62_1 0.1779 0.059 3.033 0.003 0.062 0.294
## Q62_2 -0.0339 0.071 -0.476 0.635 -0.175 0.107
## Q62_3 0.0428 0.089 0.483 0.630 -0.133 0.218
## Q62_4 0.1388 0.072 1.922 0.057 -0.004 0.282
## Q62_5 0.3967 0.099 4.019 0.000 0.201 0.592
## Q62_6 0.0824 0.071 1.166 0.246 -0.058 0.222
## Q62_7 0.0272 0.070 0.391 0.697 -0.111 0.165
## Q62_8 -0.1132 0.064 -1.762 0.081 -0.240 0.014
## Q62_9 -0.0515 0.089 -0.581 0.563 -0.227 0.124
## Q62_15 -0.1023 0.122 -0.841 0.402 -0.343 0.139
## Q62_16 0.1248 0.137 0.914 0.363 -0.146 0.395
## Q62_17 0.0645 0.070 0.919 0.360 -0.075 0.204
## Q62_18 0.3423 0.112 3.044 0.003 0.120 0.565
## Q62_19 -0.0945 0.102 -0.930 0.354 -0.296 0.107
## ==============================================================================
## Omnibus: 23.808 Durbin-Watson: 1.983
## Prob(Omnibus): 0.000 Jarque-Bera (JB): 74.383
## Skew: -0.591 Prob(JB): 7.05e-17
## Kurtosis: 6.512 Cond. No. 197.
## ==============================================================================
##
## Notes:
## [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
## """
Plot own&children~general beliefs and attitudes
#thanks to https://github.com/tobywise/covid19-risk-perception
# labels
names = ['A coronavirus vaccination should be mandatory for everyone who is able to have it','Without a coronavirus vaccine, I am likely to catch coronavirus','If I get a coronavirus vaccination, I will be protected against coronavirus','If I don’t get a coronavirus vaccination and end up getting coronavirus, I would regret not getting the vaccination','It would be very easy for me to have a coronavirus vaccination','A coronavirus vaccination could give me coronavirus','I would be worried about experiencing side effects from a coronavirus vaccination','I might regret getting a coronavirus vaccination if I later experienced side effects from the vaccination','A coronavirus vaccination will be too new for me to be confident about getting vaccinated','My family would approve of my having a coronavirus vaccination','My friends would approve of my having a coronavirus vaccination','If a coronavirus vaccination were recommended by the Government, I would get vaccinated','If a coronavirus vaccination were recommended by a health care professional (e.g. GP or nurse), I would get vaccinated','A coronavirus vaccine will allow us to get back to ‘normal’']
# font type & size
plt.rcParams.update({'font.size': 12})
plt.rcParams["font.family"] = "Times New Roman"
# plot
f, ax = plt.subplots(1, 2, figsize=(14, 10))
# DISCOVERY
ax[0].axvline(0, linestyle=':', color='gray', linewidth=2)
# Points and error bars
ax[0].errorbar(y=np.arange(len(res_own_discovery.params[1:])) - 0.1, x=res_own_discovery.params[1:],
xerr=np.abs(res_own_discovery.conf_int().values[1:, :] - res_own_discovery.params[1:, np.newaxis]).T,
color='gray', linewidth=2, fmt='^', label='own')
## <ErrorbarContainer object of 3 artists>
##
## <string>:2: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
ax[0].errorbar(y=np.arange(len(res_children_discovery.params[1:])) + 0.1, x=res_children_discovery.params[1:],
xerr=np.abs(res_children_discovery.conf_int().values[1:, :] - res_children_discovery.params[1:, np.newaxis]).T,
color='black', linewidth=2, fmt='s', label='children')
# Labels
## <ErrorbarContainer object of 3 artists>
##
## <string>:2: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
ax[0].set_yticks(range(len(names)))
## [<matplotlib.axis.YTick object at 0x0000000064F96FD0>, <matplotlib.axis.YTick object at 0x0000000064F96850>, <matplotlib.axis.YTick object at 0x0000000064F836A0>, <matplotlib.axis.YTick object at 0x0000000064D445B0>, <matplotlib.axis.YTick object at 0x0000000064D44AC0>, <matplotlib.axis.YTick object at 0x000000006505BEE0>, <matplotlib.axis.YTick object at 0x0000000064D44A90>, <matplotlib.axis.YTick object at 0x000000006505B790>, <matplotlib.axis.YTick object at 0x00000000650860D0>, <matplotlib.axis.YTick object at 0x0000000065086760>, <matplotlib.axis.YTick object at 0x0000000065086EB0>, <matplotlib.axis.YTick object at 0x000000006508F760>, <matplotlib.axis.YTick object at 0x0000000065086E80>, <matplotlib.axis.YTick object at 0x000000006505B3D0>]
ax[0].set_yticklabels(['\n'.join(textwrap.wrap(q, 60, break_long_words=False)) for q in names])
## Titles
## [Text(0, 0, 'A coronavirus vaccination should be mandatory for everyone\nwho is able to have it'), Text(0, 1, 'Without a coronavirus vaccine, I am likely to catch\ncoronavirus'), Text(0, 2, 'If I get a coronavirus vaccination, I will be protected\nagainst coronavirus'), Text(0, 3, 'If I don’t get a coronavirus vaccination and end up getting\ncoronavirus, I would regret not getting the vaccination'), Text(0, 4, 'It would be very easy for me to have a coronavirus\nvaccination'), Text(0, 5, 'A coronavirus vaccination could give me coronavirus'), Text(0, 6, 'I would be worried about experiencing side effects from a\ncoronavirus vaccination'), Text(0, 7, 'I might regret getting a coronavirus vaccination if I later\nexperienced side effects from the vaccination'), Text(0, 8, 'A coronavirus vaccination will be too new for me to be\nconfident about getting vaccinated'), Text(0, 9, 'My family would approve of my having a coronavirus\nvaccination'), Text(0, 10, 'My friends would approve of my having a coronavirus\nvaccination'), Text(0, 11, 'If a coronavirus vaccination were recommended by the\nGovernment, I would get vaccinated'), Text(0, 12, 'If a coronavirus vaccination were recommended by a health\ncare professional (e.g. GP or nurse), I would get vaccinated'), Text(0, 13, 'A coronavirus vaccine will allow us to get back to ‘normal’')]
ax[0].set_title("Attitudes and beliefs on vaccination intention\nestimation set (${n}$ = 519)")
ax[0].set_xlabel('Regression coefficient (+/- 95% CI)')
ax[0].legend()
# Validation
ax[1].axvline(0, linestyle=':', color='gray', linewidth=2)
ax[1].errorbar(y=np.arange(len(res_own_validation.params[1:])) - 0.1, x=res_own_validation.params[1:],
xerr=np.abs(res_own_validation.conf_int().values[1:, :] - res_own_validation.params[1:, np.newaxis]).T,
color='gray', linewidth=2, fmt='^', label='own')
## <ErrorbarContainer object of 3 artists>
##
## <string>:2: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
ax[1].errorbar(y=np.arange(len(res_children_validation.params[1:])) + 0.1, x=res_children_validation.params[1:],
xerr=np.abs(res_children_validation.conf_int().values[1:, :] - res_children_validation.params[1:, np.newaxis]).T,
color='black', linewidth=2, fmt='s', label='children')
# Labels
## <ErrorbarContainer object of 3 artists>
##
## <string>:2: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
ax[1].set_yticks([])
# Titles
## []
ax[1].set_title("Attitudes and beliefs on vaccination intention\nvalidation set (${n}$ = 130)")
ax[1].set_xlabel('Regression coefficient (+/- 95% CI)')
ax[1].legend()
plt.tight_layout()
plt.gcf().subplots_adjust(left=0.35)
plt.show()
preprocessing ML model
# select features and target
vac_ml = vac_2[['Q62_1','Q62_4','Q62_5','Q62_8','Q62_9','Q62_18','Q54']]
# rename col
vac_ml = vac_ml.rename(columns = {'Q62_1': 'vaccine should be mandatory','Q62_8' : 'regret of having the vaccine in case of side effects', 'Q62_4': 'likely to catch COVID-19 without the vaccine', 'Q62_9' : 'vaccine is too recent', 'Q62_18':'vaccine recommended by health care workers','Q62_5' : 'easy to have the vaccine', 'Q54' : 'Q54'}, inplace = False)
# target distribution
vac_ml['Q54'] = pd.cut(vac_ml.Q54,bins=[0,3,6,7],labels=['1','2','3'])
#sns.countplot(x=vac_ml['Q54'])
# features
X = vac_ml.drop('Q54' , axis = 1)
# target
y = vac_ml['Q54']
# train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# feature scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
hyperparameter tuning
# classifier
mlpc = MLPClassifier()
# parameter space
parameter_space = {
'max_iter': [1000, 1200],
'hidden_layer_sizes': [(3), (3,2), (4)],
'activation': ['tanh', 'relu'],
'solver': ['sgd', 'adam'],
'alpha': [0.0001, 0.05],
}
# grid search
clf = GridSearchCV(mlpc, parameter_space, n_jobs=-1, cv=3, scoring='accuracy')
clf.fit(X_train, y_train)
## GridSearchCV(cv=3, estimator=MLPClassifier(), n_jobs=-1,
## param_grid={'activation': ['tanh', 'relu'],
## 'alpha': [0.0001, 0.05],
## 'hidden_layer_sizes': [3, (3, 2), 4],
## 'max_iter': [1000, 1200], 'solver': ['sgd', 'adam']},
## scoring='accuracy')
ANN
# classifier
mlpc = MLPClassifier(hidden_layer_sizes=3, alpha= 0.0001, max_iter=1000, activation='relu', solver = 'adam', random_state=7)
mlpc.fit(X_train, y_train)
## MLPClassifier(hidden_layer_sizes=3, max_iter=1000, random_state=7)
pred_mlpc=mlpc.predict(X_test)
#metrics
print(classification_report(y_test, pred_mlpc))
# confusion matrix
## precision recall f1-score support
##
## 1 1.00 0.79 0.88 14
## 2 0.71 0.71 0.71 34
## 3 0.88 0.91 0.90 82
##
## accuracy 0.85 130
## macro avg 0.86 0.80 0.83 130
## weighted avg 0.85 0.85 0.85 130
y_true, y_pred = y_test , clf.predict(X_test)
ax = plt.axes()
sns.heatmap(
confusion_matrix(y_test, mlpc.predict(X_test)),
cmap="YlGnBu",
ax=ax,
center=0,
fmt=".0f",
square=True,
linewidths=0.5,
annot=True,
)
ax.set(xlabel='predicted', ylabel='actual', title='confusion matrix')
## [Text(0.5, 113.31319444444445, 'predicted'), Text(458.7222222222222, 0.5, 'actual'), Text(0.5, 1.0, 'confusion matrix')]
ax.set_yticklabels(('low', 'moderate', 'high'),
rotation=0, va="center")
## [Text(0, 0.5, 'low'), Text(0, 1.5, 'moderate'), Text(0, 2.5, 'high')]
ax.set_xticklabels(('low', 'moderate', 'high'),
rotation=0, va="center")
## [Text(0.5, 0, 'low'), Text(1.5, 0, 'moderate'), Text(2.5, 0, 'high')]
plt.show()
feature importance
## <BarContainer object of 6 artists>
## ([<matplotlib.axis.YTick object at 0x0000000065993DF0>, <matplotlib.axis.YTick object at 0x0000000065993670>, <matplotlib.axis.YTick object at 0x000000006577CB50>, <matplotlib.axis.YTick object at 0x00000000659CD490>, <matplotlib.axis.YTick object at 0x00000000659CDBE0>, <matplotlib.axis.YTick object at 0x00000000659D4370>], [Text(0, 0, '1'), Text(0, 1, '2'), Text(0, 2, '3'), Text(0, 3, '4'), Text(0, 4, '5'), Text(0, 5, '6')])
10-fold validation: comaprison with other ML models
# models
rfc = RandomForestClassifier(n_estimators=200, max_features=1)
clf = svm.SVC()
lgr = LogisticRegression()
models = []
models.append(('RFC', rfc))
models.append(('SCV',clf))
models.append(('LGR', lgr))
models.append(('MLPC', mlpc))
print(models)
# compare model results
## [('RFC', RandomForestClassifier(max_features=1, n_estimators=200)), ('SCV', SVC()), ('LGR', LogisticRegression()), ('MLPC', MLPClassifier(hidden_layer_sizes=3, max_iter=1000, random_state=7))]
results = dict()
for name, model in models:
kfold = KFold(n_splits=10, random_state=10, shuffle=True)
cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='accuracy')
results[name]=(cv_results.mean(), cv_results.std())
print()
print("name results.mean results.std")
for key, value in results.items():
print(key, value)
##
## name results.mean results.std
## RFC (0.7822775263951735, 0.051606032322000985)
##
## name results.mean results.std
## RFC (0.7822775263951735, 0.051606032322000985)
## SCV (0.7900075414781297, 0.04492830453137671)
##
## name results.mean results.std
## RFC (0.7822775263951735, 0.051606032322000985)
## SCV (0.7900075414781297, 0.04492830453137671)
## LGR (0.7899321266968325, 0.054211492089524624)
##
## name results.mean results.std
## RFC (0.7822775263951735, 0.051606032322000985)
## SCV (0.7900075414781297, 0.04492830453137671)
## LGR (0.7899321266968325, 0.054211492089524624)
## MLPC (0.795814479638009, 0.04545273644099338)