Cluster Map
Packages
library(ggplot2)
library(sf)
library(geobr)
library(ggspatial)
library(tidyverse)
library(RColorBrewer)
library(cowplot)
layout(matrix(c(1,2,3,3), 2, 2, byrow = TRUE))
Rio Grande do Sul
RS <- read_state(code_state = "RS", year = 2018, showProgress = F)
ggplot(RS) +
aes(group = code_state) +
geom_sf(size = 1L) +
labs(x = "Longitude", y = "Latitude", title = "Rio Grande do Sul") +
theme_bw()
Brazil
BR <- read_state(code_state = "all", year = 2018, showProgress = F)
BRFINAL2 <- ggplot(BR) +
aes(group = code_region) +
geom_sf(size = 0.5, fill = "white") +
geom_sf(aes(group = code_state), data = RS, fill = "#E9635A") +
labs(x = "Longitude", y = "Latitude", title = "") + theme_void()
BRFINAL2
Cities RS
munRS <- read_municipality(code_muni="RS", year=2018, showProgress = F)
munRS <- munRS[c(-2),]
munRS$Cluster <- c("Não")
munRS$Cluster <- ifelse(munRS$code_muni == 4300802 | munRS$code_muni == 4302105 | munRS$code_muni == 4303673 | munRS$code_muni == 4305108 |
munRS$code_muni == 4305959 | munRS$code_muni == 4307906 | munRS$code_muni == 4308201 | munRS$code_muni == 4308607 |
munRS$code_muni == 4309407 | munRS$code_muni == 4310439 | munRS$code_muni == 4313086 | munRS$code_muni == 4313359 |
munRS$code_muni == 4319000 | munRS$code_muni == 4322806 | munRS$code_muni == 4323309 | munRS$code_muni == 4312385 |
munRS$code_muni == 4314548, "Sim", "Não")
munRS <- munRS %>%
mutate(Cluster = ifelse(Cluster == "Sim", "Yes", "No"))
RSmun2 <- ggplot(munRS) +
aes(fill = Cluster, group = code_state) +
geom_sf(size = 0.5, show.legend = F) +
labs(x = "", y = "", title = "") +
scale_fill_manual(values = c("white", "#E9635A")) + theme_void()
RSmun2
Cluster
ClusterMun <- munRS
ClusterMun <- ClusterMun[c(18,44,70,97,121,164,169,176,190,209,259,280,285,319, 403,483,489),]
Frequencia <- c(8, 20, 0, 34, 2, 12, 71,19,0,0,5,8,4,0,8,2,1)
ClusterMun <- cbind(ClusterMun,Frequencia)
ClusterMun$Categoria <- cut(ClusterMun$Frequencia, breaks = c(-1, 5, 13, 34, 71),
labels = c("0 to 5", "5 to 13", "13 to 34", "34 to 71"))
cluster <- ggplot(ClusterMun) +
aes(group = code_state) +
geom_sf(size = 0.5) +
labs(title = "", x = "", y = "") +
geom_sf_label(aes(label = name_muni), label.padding = unit(0.05, "lines"),
label.r = unit(0.05, "lines"), inherit.aes = F, label.size = 0.1) +
theme_bw()
clusterFinal <- ClusterMun %>% ggplot(aes(fill = Categoria)) + geom_sf(size = 0.5) +
scale_fill_manual(values = c("#F3D4D2", "#E9A8A2", "#E9635A", "#C41617")) +
labs(title = "", x = "", y = "", fill = "Frequency") +
geom_sf_text(aes(label = name_muni), check_overlap = T, size = 3) + theme_bw()
clusterFinal
Final Maps
clusterFinal
BRFINAL2
RSmun2
All in one
ggdraw (clusterFinal) +
draw_plot(BRFINAL2, width = 0.26, height = 0.22,
x = 0.05, y = 0.72) +
draw_plot(RSmun2, width = 0.26, height = 0.22,
x = 0.05, y = 0.15)
Evolution Statistics
Wineries
library(readxl)
library(tidyverse)
library(plotly)
library(reshape2)
empresas <- read_excel("C:/Users/user/Desktop/Vida acadêmica/Submissões/Artigo dissertação/Quanti/Empresas.xlsx")
empresas2 <- melt(empresas, id.vars = "Ano")
str(empresas2)
## 'data.frame': 50 obs. of 3 variables:
## $ Ano : num 1995 1996 1997 1998 1999 ...
## $ variable: Factor w/ 2 levels "Cluster","Brasil": 1 1 1 1 1 1 1 1 1 1 ...
## $ value : num 171 160 172 168 178 186 197 189 192 188 ...
empresas2$variable <- factor(empresas2$variable,
levels=c("Brasil", "Cluster"),
labels=c("Brazil", "Cluster"))
GEE <- ggplot(empresas2, aes(x = Ano, y = value, group = variable)) +
geom_line(aes(color = variable, linetype = variable), size = 1L) +
geom_point(aes(color = variable), size = 3L) +
scale_color_manual(values=c('#1E90FF','#87CEEB')) +
scale_x_continuous(breaks = c(1995, 1998, 2001, 2004, 2007, 2010, 2013, 2016, 2019)) +
labs(title = "Historical Evolution of the Number of Wineries", y = "", x = "",
color = NULL, linetype = NULL) +
theme_bw(base_size = 10) +
theme(legend.position="top", text = element_text(family= "Times New Roman", face="bold"),
plot.title = element_text(hjust = 0.5),
legend.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 8))
GEE
Employees
empregados <- read_excel("C:/Users/user/Desktop/Vida acadêmica/Submissões/Artigo dissertação/Quanti/Empregados.xlsx")
empregados2 <- melt(empregados, id.vars = "Ano")
empregados2$variable <- factor(empregados2$variable,
levels=c("Brasil", "Cluster"),
labels=c("Brazil", "Cluster"))
GEM <- ggplot(empregados2, aes(x = Ano, y = value, group = variable)) +
geom_line(aes(color = variable, linetype = variable), size = 1L) +
geom_point(aes(color = variable), size = 3L) +
scale_color_manual(values=c('#1E90FF','#87CEEB')) +
scale_x_continuous(breaks = c(1995, 1998, 2001, 2004, 2007, 2010, 2013, 2016, 2019)) +
labs(title = "Historical Evolution of the Number of Employees in the Wine Sector", y = "", x = "",
color = NULL, linetype = NULL) +
theme_bw(base_size = 10) +
theme(legend.position="top", text = element_text(family= "Times New Roman", face="bold"),
plot.title = element_text(hjust = 0.5),
legend.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 8))
GEM
All in one
library(gridExtra)
grid.arrange(GEE,GEM, ncol = 1)
Wine Descriptions
Load Data
library(foreign)
dados <- read.spss("C:/Users/user/Desktop/Vida acadêmica/Submissões/Artigo dissertação/Quanti/Dados.sav")
attach(dados)
library(tibble) # Resolve o problema anterior #
dados <- as_tibble(dados)
library(tidyverse)
library(plotly)
Cities frequency table
dados %>%
count(Município)
## # A tibble: 13 x 2
## Município n
## <fct> <int>
## 1 Antônio Prado 8
## 2 Bento Gonçalves 20
## 3 Flores da Cunha 71
## 4 Garibaldi 19
## 5 Caxias do Sul 34
## 6 Nova Pádua 8
## 7 Farroupilha 12
## 8 Monte Belo 5
## 9 São Marcos 8
## 10 Nova Roma do Sul 4
## 11 Cotiporã 2
## 12 Veranópolis 2
## 13 Vila FLores 1
Crisis Impact in the Wineries
tab <- table(dados$Impacto_crise)
Impacto_da_crise <- plot_ly(dados, values = tab,
type = 'pie', textposition = "inside",
textinfo = 'percent+value',
labels = c("Strong", "Moderate", "Weak")) %>% layout(title="Crisis Impact")
Impacto_da_crise
Respondent profile
levels(dados$Cargos)
## [1] "Proprietário" "Sócio"
## [3] "Diretor de departamento" "Gerente"
## [5] "Função administrativa" "Enólogo"
## [7] "Outros"
dados$Cargos <- factor(dados$Cargos,
levels=c("Proprietário", "Sócio", "Diretor de departamento", "Gerente", "Função administrativa", "Enólogo", "Outros"),
labels=c("Owner", "Partner", "Department Director", "Manager", "Administrative", "Oenologist", "Other"))
dados %>% count(Cargos)
## # A tibble: 7 x 2
## Cargos n
## <fct> <int>
## 1 Owner 143
## 2 Partner 2
## 3 Department Director 12
## 4 Manager 11
## 5 Administrative 12
## 6 Oenologist 12
## 7 Other 2
prop.table(table(dados$Cargos)) * 100
##
## Owner Partner Department Director Manager
## 73.711340 1.030928 6.185567 5.670103
## Administrative Oenologist Other
## 6.185567 6.185567 1.030928
SEM MODEL
Packages
library(semTools)
library(lavaan)
library(semPlot)
Model building
Modelo2 <-
'Performance =~ D1 + D2 + D3 + D4 + D5 + D6
Economic Specialization =~ ESP1 + ESP3 + ESP4 + ESP5
Economic Diversification =~ DIV2 + DIV3 + DIV4 + DIV5
Relational Networks =~ RED1 + RED2 + RED3 + RED4 + RED6
International Relations =~ INT1 + INT2 + INT3 + INT4 + INT5 + INT7
Technological Heterogeneity =~ TEC1 + TEC3 + TEC4 + TEC5
Institutional Environment =~ INST1 + INST2 + INST3 + INST4 + INST5 + INST6
Public policies =~ POL3 + POL4 + POL5 +POL7
Performance ~ Economic Specialization + Economic Diversification + Relational Networks + International Relations + Technological Heterogeneity + Institutional Environment + Public policies'
Model Fit
ModeloFit <- cfa(Modelo2, data = noout, estimator = "WLSMV", ordered = c("D1", "D2", "D3", "D4", "D5", "D6",
"ESP1", "ESP3", "ESP4", "ESP5",
"DIV2", "DIV3", "DIV4", "DIV5",
"RED1", "RED2", "RED3", "RED4", "RED6",
"INT1", "INT2", "INT3", "INT4", "INT5", "INT7",
"TEC1", "TEC3", "TEC4", "TEC5",
"INST1", "INST2", "INST3", "INST4", "INST5", "INST6",
"POL3", "POL4", "POL5", "POL7"))
Model Summary
summary(ModeloFit, standardized = T, rsquare = T, fit.measures = T) # Summary
## lavaan 0.6-6 ended normally after 82 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of free parameters 215
##
## Number of observations 191
##
## Model Test User Model:
## Standard Robust
## Test Statistic 928.327 917.894
## Degrees of freedom 674 674
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.909
## Shift parameter 431.637
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 18741.452 6260.754
## Degrees of freedom 741 741
## P-value 0.000 0.000
## Scaling correction factor 3.261
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.986 0.956
## Tucker-Lewis Index (TLI) 0.984 0.951
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.045 0.044
## 90 Percent confidence interval - lower 0.037 0.036
## 90 Percent confidence interval - upper 0.051 0.051
## P-value RMSEA <= 0.05 0.902 0.934
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.080 0.080
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv
## Performance =~
## D1 1.000 0.764
## D2 0.909 0.075 12.195 0.000 0.694
## D3 0.953 0.074 12.793 0.000 0.728
## D4 0.990 0.068 14.498 0.000 0.756
## D5 0.791 0.068 11.607 0.000 0.605
## D6 0.802 0.069 11.603 0.000 0.613
## EconomicSpecialization =~
## ESP1 1.000 0.737
## ESP3 1.065 0.103 10.303 0.000 0.785
## ESP4 0.973 0.105 9.288 0.000 0.717
## ESP5 1.214 0.103 11.751 0.000 0.895
## EconomicDiversification =~
## DIV2 1.000 0.588
## DIV3 0.770 0.163 4.738 0.000 0.453
## DIV4 1.090 0.167 6.524 0.000 0.641
## DIV5 1.191 0.190 6.270 0.000 0.701
## RelationalNetworks =~
## RED1 1.000 0.769
## RED2 0.964 0.107 9.033 0.000 0.741
## RED3 0.776 0.088 8.835 0.000 0.597
## RED4 0.750 0.094 8.026 0.000 0.577
## RED6 0.867 0.086 10.060 0.000 0.666
## InternationalRelations =~
## INT1 1.000 0.709
## INT2 1.252 0.075 16.634 0.000 0.887
## INT3 1.239 0.076 16.296 0.000 0.878
## INT4 1.306 0.077 17.050 0.000 0.926
## INT5 1.309 0.076 17.326 0.000 0.928
## INT7 0.950 0.077 12.402 0.000 0.673
## TechnologicalHeterogeneity =~
## TEC1 1.000 0.820
## TEC3 0.695 0.089 7.771 0.000 0.570
## TEC4 0.909 0.094 9.657 0.000 0.745
## TEC5 0.733 0.099 7.384 0.000 0.601
## InstitutionalEnvironment =~
## INST1 1.000 0.597
## INST2 1.032 0.158 6.542 0.000 0.616
## INST3 1.115 0.150 7.458 0.000 0.665
## INST4 0.990 0.151 6.565 0.000 0.591
## INST5 1.056 0.144 7.346 0.000 0.630
## INST6 1.049 0.150 6.988 0.000 0.626
## Publicpolicies =~
## POL3 1.000 0.703
## POL4 0.943 0.103 9.155 0.000 0.663
## POL5 1.068 0.092 11.606 0.000 0.752
## POL7 1.049 0.103 10.179 0.000 0.738
## Std.all
##
## 0.764
## 0.694
## 0.728
## 0.756
## 0.605
## 0.613
##
## 0.737
## 0.785
## 0.717
## 0.895
##
## 0.588
## 0.453
## 0.641
## 0.701
##
## 0.769
## 0.741
## 0.597
## 0.577
## 0.666
##
## 0.709
## 0.887
## 0.878
## 0.926
## 0.928
## 0.673
##
## 0.820
## 0.570
## 0.745
## 0.601
##
## 0.597
## 0.616
## 0.665
## 0.591
## 0.630
## 0.626
##
## 0.703
## 0.663
## 0.752
## 0.738
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Performance ~
## EconomcSpclztn 0.391 0.131 2.994 0.003 0.378 0.378
## EconmcDvrsfctn -0.181 0.180 -1.004 0.316 -0.139 -0.139
## RelatinlNtwrks -0.145 0.103 -1.407 0.159 -0.146 -0.146
## InterntnlRltns 0.602 0.107 5.608 0.000 0.558 0.558
## TchnlgclHtrgnt 0.203 0.096 2.113 0.035 0.218 0.218
## InstttnlEnvrnm -0.316 0.184 -1.717 0.086 -0.247 -0.247
## Publicpolicies 0.514 0.142 3.612 0.000 0.473 0.473
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## EconomicSpecialization ~~
## EconmcDvrsfctn 0.286 0.050 5.678 0.000 0.659
## RelatinlNtwrks -0.015 0.048 -0.316 0.752 -0.027
## InterntnlRltns -0.037 0.043 -0.862 0.389 -0.071
## TchnlgclHtrgnt 0.124 0.052 2.357 0.018 0.205
## InstttnlEnvrnm 0.017 0.037 0.462 0.644 0.039
## Publicpolicies -0.018 0.049 -0.366 0.714 -0.034
## EconomicDiversification ~~
## RelatinlNtwrks 0.125 0.045 2.792 0.005 0.276
## InterntnlRltns 0.016 0.039 0.401 0.689 0.038
## TchnlgclHtrgnt 0.108 0.045 2.433 0.015 0.225
## InstttnlEnvrnm 0.101 0.033 3.069 0.002 0.289
## Publicpolicies 0.055 0.038 1.440 0.150 0.134
## RelationalNetworks ~~
## InterntnlRltns 0.286 0.040 7.125 0.000 0.526
## TchnlgclHtrgnt 0.243 0.051 4.773 0.000 0.386
## InstttnlEnvrnm 0.239 0.042 5.642 0.000 0.521
## Publicpolicies 0.317 0.049 6.506 0.000 0.587
## InternationalRelations ~~
## TchnlgclHtrgnt 0.348 0.045 7.677 0.000 0.598
## InstttnlEnvrnm 0.174 0.034 5.133 0.000 0.411
## Publicpolicies 0.228 0.042 5.463 0.000 0.458
## TechnologicalHeterogeneity ~~
## InstttnlEnvrnm 0.232 0.046 5.050 0.000 0.474
## Publicpolicies 0.254 0.049 5.136 0.000 0.440
## InstitutionalEnvironment ~~
## Publicpolicies 0.319 0.050 6.349 0.000 0.759
## Std.all
##
## 0.659
## -0.027
## -0.071
## 0.205
## 0.039
## -0.034
##
## 0.276
## 0.038
## 0.225
## 0.289
## 0.134
##
## 0.526
## 0.386
## 0.521
## 0.587
##
## 0.598
## 0.411
## 0.458
##
## 0.474
## 0.440
##
## 0.759
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D1 0.000 0.000 0.000
## .D2 0.000 0.000 0.000
## .D3 0.000 0.000 0.000
## .D4 0.000 0.000 0.000
## .D5 0.000 0.000 0.000
## .D6 0.000 0.000 0.000
## .ESP1 0.000 0.000 0.000
## .ESP3 0.000 0.000 0.000
## .ESP4 0.000 0.000 0.000
## .ESP5 0.000 0.000 0.000
## .DIV2 0.000 0.000 0.000
## .DIV3 0.000 0.000 0.000
## .DIV4 0.000 0.000 0.000
## .DIV5 0.000 0.000 0.000
## .RED1 0.000 0.000 0.000
## .RED2 0.000 0.000 0.000
## .RED3 0.000 0.000 0.000
## .RED4 0.000 0.000 0.000
## .RED6 0.000 0.000 0.000
## .INT1 0.000 0.000 0.000
## .INT2 0.000 0.000 0.000
## .INT3 0.000 0.000 0.000
## .INT4 0.000 0.000 0.000
## .INT5 0.000 0.000 0.000
## .INT7 0.000 0.000 0.000
## .TEC1 0.000 0.000 0.000
## .TEC3 0.000 0.000 0.000
## .TEC4 0.000 0.000 0.000
## .TEC5 0.000 0.000 0.000
## .INST1 0.000 0.000 0.000
## .INST2 0.000 0.000 0.000
## .INST3 0.000 0.000 0.000
## .INST4 0.000 0.000 0.000
## .INST5 0.000 0.000 0.000
## .INST6 0.000 0.000 0.000
## .POL3 0.000 0.000 0.000
## .POL4 0.000 0.000 0.000
## .POL5 0.000 0.000 0.000
## .POL7 0.000 0.000 0.000
## .Performance 0.000 0.000 0.000
## EconomcSpclztn 0.000 0.000 0.000
## EconmcDvrsfctn 0.000 0.000 0.000
## RelatinlNtwrks 0.000 0.000 0.000
## InterntnlRltns 0.000 0.000 0.000
## TchnlgclHtrgnt 0.000 0.000 0.000
## InstttnlEnvrnm 0.000 0.000 0.000
## Publicpolicies 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D1|t1 -1.199 0.119 -10.063 0.000 -1.199 -1.199
## D1|t2 -0.721 0.100 -7.202 0.000 -0.721 -0.721
## D1|t3 0.455 0.094 4.817 0.000 0.455 0.455
## D1|t4 1.347 0.128 10.501 0.000 1.347 1.347
## D2|t1 -0.790 0.102 -7.746 0.000 -0.790 -0.790
## D2|t2 -0.205 0.092 -2.236 0.025 -0.205 -0.205
## D2|t3 0.514 0.095 5.385 0.000 0.514 0.514
## D2|t4 0.923 0.107 8.666 0.000 0.923 0.923
## D3|t1 -1.940 0.191 -10.174 0.000 -1.940 -1.940
## D3|t2 -1.147 0.116 -9.856 0.000 -1.147 -1.147
## D3|t3 0.020 0.091 0.217 0.829 0.020 0.020
## D3|t4 1.007 0.110 9.166 0.000 1.007 1.007
## D4|t1 -1.673 0.156 -10.707 0.000 -1.673 -1.673
## D4|t2 -1.098 0.114 -9.636 0.000 -1.098 -1.098
## D4|t3 0.272 0.092 2.955 0.003 0.272 0.272
## D4|t4 1.173 0.118 9.962 0.000 1.173 1.173
## D5|t1 -1.729 0.162 -10.644 0.000 -1.729 -1.729
## D5|t2 -1.147 0.116 -9.856 0.000 -1.147 -1.147
## D5|t3 -0.072 0.091 -0.794 0.427 -0.072 -0.072
## D5|t4 1.051 0.112 9.406 0.000 1.051 1.051
## D6|t1 -1.490 0.139 -10.718 0.000 -1.490 -1.490
## D6|t2 -0.943 0.107 -8.793 0.000 -0.943 -0.943
## D6|t3 0.232 0.092 2.524 0.012 0.232 0.232
## D6|t4 1.452 0.136 10.679 0.000 1.452 1.452
## ESP1|t1 -2.035 0.206 -9.861 0.000 -2.035 -2.035
## ESP1|t2 -1.199 0.119 -10.063 0.000 -1.199 -1.199
## ESP1|t3 -0.773 0.102 -7.611 0.000 -0.773 -0.773
## ESP1|t4 -0.138 0.091 -1.515 0.130 -0.138 -0.138
## ESP3|t1 -2.152 0.229 -9.398 0.000 -2.152 -2.152
## ESP3|t2 -1.531 0.143 -10.743 0.000 -1.531 -1.531
## ESP3|t3 -0.232 0.092 -2.524 0.012 -0.232 -0.232
## ESP4|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## ESP4|t2 -1.791 0.170 -10.542 0.000 -1.791 -1.791
## ESP4|t3 -0.985 0.109 -9.043 0.000 -0.985 -0.985
## ESP4|t4 -0.033 0.091 -0.361 0.718 -0.033 -0.033
## ESP5|t1 -2.152 0.229 -9.398 0.000 -2.152 -2.152
## ESP5|t2 -1.315 0.126 -10.425 0.000 -1.315 -1.315
## ESP5|t3 -0.245 0.092 -2.668 0.008 -0.245 -0.245
## DIV2|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## DIV2|t2 -1.791 0.170 -10.542 0.000 -1.791 -1.791
## DIV2|t3 -1.227 0.121 -10.161 0.000 -1.227 -1.227
## DIV2|t4 0.191 0.092 2.092 0.036 0.191 0.191
## DIV3|t1 -2.309 0.266 -8.675 0.000 -2.309 -2.309
## DIV3|t2 -1.940 0.191 -10.174 0.000 -1.940 -1.940
## DIV3|t3 -1.173 0.118 -9.962 0.000 -1.173 -1.173
## DIV3|t4 0.099 0.091 1.082 0.279 0.099 0.099
## DIV4|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## DIV4|t2 -1.791 0.170 -10.542 0.000 -1.791 -1.791
## DIV4|t3 -1.147 0.116 -9.856 0.000 -1.147 -1.147
## DIV4|t4 0.072 0.091 0.794 0.427 0.072 0.072
## DIV5|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## DIV5|t2 -1.452 0.136 -10.679 0.000 -1.452 -1.452
## DIV5|t3 -0.369 0.093 -3.960 0.000 -0.369 -0.369
## RED1|t1 -1.860 0.179 -10.392 0.000 -1.860 -1.860
## RED1|t2 -1.173 0.118 -9.962 0.000 -1.173 -1.173
## RED1|t3 -0.412 0.094 -4.389 0.000 -0.412 -0.412
## RED1|t4 0.412 0.094 4.389 0.000 0.412 0.412
## RED2|t1 -2.035 0.206 -9.861 0.000 -2.035 -2.035
## RED2|t2 -1.531 0.143 -10.743 0.000 -1.531 -1.531
## RED2|t3 -0.654 0.098 -6.650 0.000 -0.654 -0.654
## RED2|t4 0.440 0.094 4.674 0.000 0.440 0.440
## RED3|t1 -1.860 0.179 -10.392 0.000 -1.860 -1.860
## RED3|t2 -1.622 0.151 -10.741 0.000 -1.622 -1.622
## RED3|t3 -0.738 0.101 -7.339 0.000 -0.738 -0.738
## RED3|t4 0.355 0.093 3.817 0.000 0.355 0.355
## RED4|t1 -2.309 0.266 -8.675 0.000 -2.309 -2.309
## RED4|t2 -1.074 0.113 -9.522 0.000 -1.074 -1.074
## RED4|t3 0.191 0.092 2.092 0.036 0.191 0.191
## RED6|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## RED6|t2 -1.729 0.162 -10.644 0.000 -1.729 -1.729
## RED6|t3 -0.721 0.100 -7.202 0.000 -0.721 -0.721
## RED6|t4 0.575 0.097 5.950 0.000 0.575 0.575
## INT1|t1 -0.773 0.102 -7.611 0.000 -0.773 -0.773
## INT1|t2 -0.341 0.093 -3.673 0.000 -0.341 -0.341
## INT1|t3 0.245 0.092 2.668 0.008 0.245 0.245
## INT1|t4 0.790 0.102 7.746 0.000 0.790 0.790
## INT2|t1 -0.440 0.094 -4.674 0.000 -0.440 -0.440
## INT2|t2 -0.007 0.091 -0.072 0.942 -0.007 -0.007
## INT2|t3 0.286 0.092 3.099 0.002 0.286 0.286
## INT2|t4 1.098 0.114 9.636 0.000 1.098 1.098
## INT3|t1 -0.046 0.091 -0.505 0.613 -0.046 -0.046
## INT3|t2 0.152 0.091 1.659 0.097 0.152 0.152
## INT3|t3 0.529 0.096 5.527 0.000 0.529 0.529
## INT3|t4 0.964 0.108 8.919 0.000 0.964 0.964
## INT4|t1 -0.245 0.092 -2.668 0.008 -0.245 -0.245
## INT4|t2 -0.020 0.091 -0.217 0.829 -0.020 -0.020
## INT4|t3 0.412 0.094 4.389 0.000 0.412 0.412
## INT4|t4 0.985 0.109 9.043 0.000 0.985 0.985
## INT5|t1 -0.397 0.094 -4.246 0.000 -0.397 -0.397
## INT5|t2 -0.099 0.091 -1.082 0.279 -0.099 -0.099
## INT5|t3 0.286 0.092 3.099 0.002 0.286 0.286
## INT5|t4 0.864 0.104 8.278 0.000 0.864 0.864
## INT7|t1 -1.622 0.151 -10.741 0.000 -1.622 -1.622
## INT7|t2 -0.606 0.097 -6.231 0.000 -0.606 -0.606
## INT7|t3 0.085 0.091 0.938 0.348 0.085 0.085
## INT7|t4 0.687 0.099 6.927 0.000 0.687 0.687
## TEC1|t1 -2.035 0.206 -9.861 0.000 -2.035 -2.035
## TEC1|t2 -1.380 0.131 -10.569 0.000 -1.380 -1.380
## TEC1|t3 -0.455 0.094 -4.817 0.000 -0.455 -0.455
## TEC1|t4 0.455 0.094 4.817 0.000 0.455 0.455
## TEC3|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## TEC3|t2 -2.035 0.206 -9.861 0.000 -2.035 -2.035
## TEC3|t3 -0.638 0.098 -6.510 0.000 -0.638 -0.638
## TEC3|t4 0.469 0.095 4.959 0.000 0.469 0.469
## TEC4|t1 -2.309 0.266 -8.675 0.000 -2.309 -2.309
## TEC4|t2 -1.791 0.170 -10.542 0.000 -1.791 -1.791
## TEC4|t3 -0.985 0.109 -9.043 0.000 -0.985 -0.985
## TEC4|t4 0.426 0.094 4.532 0.000 0.426 0.426
## TEC5|t1 -1.122 0.115 -9.748 0.000 -1.122 -1.122
## TEC5|t2 0.245 0.092 2.668 0.008 0.245 0.245
## INST1|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## INST1|t2 -1.860 0.179 -10.392 0.000 -1.860 -1.860
## INST1|t3 -1.098 0.114 -9.636 0.000 -1.098 -1.098
## INST1|t4 0.286 0.092 3.099 0.002 0.286 0.286
## INST2|t1 -2.152 0.229 -9.398 0.000 -2.152 -2.152
## INST2|t2 -0.903 0.106 -8.538 0.000 -0.903 -0.903
## INST2|t3 0.514 0.095 5.385 0.000 0.514 0.514
## INST3|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## INST3|t2 -1.791 0.170 -10.542 0.000 -1.791 -1.791
## INST3|t3 -0.773 0.102 -7.611 0.000 -0.773 -0.773
## INST3|t4 0.469 0.095 4.959 0.000 0.469 0.469
## INST4|t1 -1.860 0.179 -10.392 0.000 -1.860 -1.860
## INST4|t2 -1.029 0.111 -9.287 0.000 -1.029 -1.029
## INST4|t3 0.369 0.093 3.960 0.000 0.369 0.369
## INST5|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## INST5|t2 -1.622 0.151 -10.741 0.000 -1.622 -1.622
## INST5|t3 -1.173 0.118 -9.962 0.000 -1.173 -1.173
## INST5|t4 0.383 0.093 4.103 0.000 0.383 0.383
## INST6|t1 -2.560 0.348 -7.366 0.000 -2.560 -2.560
## INST6|t2 -1.940 0.191 -10.174 0.000 -1.940 -1.940
## INST6|t3 -1.173 0.118 -9.962 0.000 -1.173 -1.173
## INST6|t4 0.138 0.091 1.515 0.130 0.138 0.138
## POL3|t1 -1.147 0.116 -9.856 0.000 -1.147 -1.147
## POL3|t2 -0.790 0.102 -7.746 0.000 -0.790 -0.790
## POL3|t3 -0.286 0.092 -3.099 0.002 -0.286 -0.286
## POL3|t4 0.845 0.104 8.146 0.000 0.845 0.845
## POL4|t1 -2.035 0.206 -9.861 0.000 -2.035 -2.035
## POL4|t2 -1.199 0.119 -10.063 0.000 -1.199 -1.199
## POL4|t3 -0.469 0.095 -4.959 0.000 -0.469 -0.469
## POL4|t4 0.544 0.096 5.668 0.000 0.544 0.544
## POL5|t1 -1.673 0.156 -10.707 0.000 -1.673 -1.673
## POL5|t2 -1.285 0.124 -10.342 0.000 -1.285 -1.285
## POL5|t3 -0.341 0.093 -3.673 0.000 -0.341 -0.341
## POL5|t4 0.773 0.102 7.611 0.000 0.773 0.773
## POL7|t1 -1.673 0.156 -10.707 0.000 -1.673 -1.673
## POL7|t2 -1.415 0.133 -10.629 0.000 -1.415 -1.415
## POL7|t3 -0.773 0.102 -7.611 0.000 -0.773 -0.773
## POL7|t4 0.383 0.093 4.103 0.000 0.383 0.383
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D1 0.417 0.417 0.417
## .D2 0.518 0.518 0.518
## .D3 0.471 0.471 0.471
## .D4 0.428 0.428 0.428
## .D5 0.635 0.635 0.635
## .D6 0.625 0.625 0.625
## .ESP1 0.457 0.457 0.457
## .ESP3 0.384 0.384 0.384
## .ESP4 0.486 0.486 0.486
## .ESP5 0.199 0.199 0.199
## .DIV2 0.654 0.654 0.654
## .DIV3 0.795 0.795 0.795
## .DIV4 0.589 0.589 0.589
## .DIV5 0.509 0.509 0.509
## .RED1 0.409 0.409 0.409
## .RED2 0.451 0.451 0.451
## .RED3 0.644 0.644 0.644
## .RED4 0.667 0.667 0.667
## .RED6 0.556 0.556 0.556
## .INT1 0.497 0.497 0.497
## .INT2 0.212 0.212 0.212
## .INT3 0.229 0.229 0.229
## .INT4 0.143 0.143 0.143
## .INT5 0.139 0.139 0.139
## .INT7 0.547 0.547 0.547
## .TEC1 0.328 0.328 0.328
## .TEC3 0.675 0.675 0.675
## .TEC4 0.445 0.445 0.445
## .TEC5 0.639 0.639 0.639
## .INST1 0.644 0.644 0.644
## .INST2 0.620 0.620 0.620
## .INST3 0.557 0.557 0.557
## .INST4 0.651 0.651 0.651
## .INST5 0.603 0.603 0.603
## .INST6 0.608 0.608 0.608
## .POL3 0.505 0.505 0.505
## .POL4 0.560 0.560 0.560
## .POL5 0.435 0.435 0.435
## .POL7 0.456 0.456 0.456
## .Performance 0.163 0.037 4.336 0.000 0.279 0.279
## EconomcSpclztn 0.543 0.081 6.736 0.000 1.000 1.000
## EconmcDvrsfctn 0.346 0.075 4.600 0.000 1.000 1.000
## RelatinlNtwrks 0.591 0.077 7.698 0.000 1.000 1.000
## InterntnlRltns 0.503 0.059 8.557 0.000 1.000 1.000
## TchnlgclHtrgnt 0.672 0.077 8.752 0.000 1.000 1.000
## InstttnlEnvrnm 0.356 0.074 4.794 0.000 1.000 1.000
## Publicpolicies 0.495 0.072 6.887 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D1 1.000 1.000 1.000
## D2 1.000 1.000 1.000
## D3 1.000 1.000 1.000
## D4 1.000 1.000 1.000
## D5 1.000 1.000 1.000
## D6 1.000 1.000 1.000
## ESP1 1.000 1.000 1.000
## ESP3 1.000 1.000 1.000
## ESP4 1.000 1.000 1.000
## ESP5 1.000 1.000 1.000
## DIV2 1.000 1.000 1.000
## DIV3 1.000 1.000 1.000
## DIV4 1.000 1.000 1.000
## DIV5 1.000 1.000 1.000
## RED1 1.000 1.000 1.000
## RED2 1.000 1.000 1.000
## RED3 1.000 1.000 1.000
## RED4 1.000 1.000 1.000
## RED6 1.000 1.000 1.000
## INT1 1.000 1.000 1.000
## INT2 1.000 1.000 1.000
## INT3 1.000 1.000 1.000
## INT4 1.000 1.000 1.000
## INT5 1.000 1.000 1.000
## INT7 1.000 1.000 1.000
## TEC1 1.000 1.000 1.000
## TEC3 1.000 1.000 1.000
## TEC4 1.000 1.000 1.000
## TEC5 1.000 1.000 1.000
## INST1 1.000 1.000 1.000
## INST2 1.000 1.000 1.000
## INST3 1.000 1.000 1.000
## INST4 1.000 1.000 1.000
## INST5 1.000 1.000 1.000
## INST6 1.000 1.000 1.000
## POL3 1.000 1.000 1.000
## POL4 1.000 1.000 1.000
## POL5 1.000 1.000 1.000
## POL7 1.000 1.000 1.000
##
## R-Square:
## Estimate
## D1 0.583
## D2 0.482
## D3 0.529
## D4 0.572
## D5 0.365
## D6 0.375
## ESP1 0.543
## ESP3 0.616
## ESP4 0.514
## ESP5 0.801
## DIV2 0.346
## DIV3 0.205
## DIV4 0.411
## DIV5 0.491
## RED1 0.591
## RED2 0.549
## RED3 0.356
## RED4 0.333
## RED6 0.444
## INT1 0.503
## INT2 0.788
## INT3 0.771
## INT4 0.857
## INT5 0.861
## INT7 0.453
## TEC1 0.672
## TEC3 0.325
## TEC4 0.555
## TEC5 0.361
## INST1 0.356
## INST2 0.380
## INST3 0.443
## INST4 0.349
## INST5 0.397
## INST6 0.392
## POL3 0.495
## POL4 0.440
## POL5 0.565
## POL7 0.544
## Performance 0.721
parameterEstimates(ModeloFit, standardized = T) # Parameter Estimates
## lhs op rhs est se
## 1 Performance =~ D1 1.000 0.000
## 2 Performance =~ D2 0.909 0.075
## 3 Performance =~ D3 0.953 0.074
## 4 Performance =~ D4 0.990 0.068
## 5 Performance =~ D5 0.791 0.068
## 6 Performance =~ D6 0.802 0.069
## 7 EconomicSpecialization =~ ESP1 1.000 0.000
## 8 EconomicSpecialization =~ ESP3 1.065 0.103
## 9 EconomicSpecialization =~ ESP4 0.973 0.105
## 10 EconomicSpecialization =~ ESP5 1.214 0.103
## 11 EconomicDiversification =~ DIV2 1.000 0.000
## 12 EconomicDiversification =~ DIV3 0.770 0.163
## 13 EconomicDiversification =~ DIV4 1.090 0.167
## 14 EconomicDiversification =~ DIV5 1.191 0.190
## 15 RelationalNetworks =~ RED1 1.000 0.000
## 16 RelationalNetworks =~ RED2 0.964 0.107
## 17 RelationalNetworks =~ RED3 0.776 0.088
## 18 RelationalNetworks =~ RED4 0.750 0.094
## 19 RelationalNetworks =~ RED6 0.867 0.086
## 20 InternationalRelations =~ INT1 1.000 0.000
## 21 InternationalRelations =~ INT2 1.252 0.075
## 22 InternationalRelations =~ INT3 1.239 0.076
## 23 InternationalRelations =~ INT4 1.306 0.077
## 24 InternationalRelations =~ INT5 1.309 0.076
## 25 InternationalRelations =~ INT7 0.950 0.077
## 26 TechnologicalHeterogeneity =~ TEC1 1.000 0.000
## 27 TechnologicalHeterogeneity =~ TEC3 0.695 0.089
## 28 TechnologicalHeterogeneity =~ TEC4 0.909 0.094
## 29 TechnologicalHeterogeneity =~ TEC5 0.733 0.099
## 30 InstitutionalEnvironment =~ INST1 1.000 0.000
## 31 InstitutionalEnvironment =~ INST2 1.032 0.158
## 32 InstitutionalEnvironment =~ INST3 1.115 0.150
## 33 InstitutionalEnvironment =~ INST4 0.990 0.151
## 34 InstitutionalEnvironment =~ INST5 1.056 0.144
## 35 InstitutionalEnvironment =~ INST6 1.049 0.150
## 36 Publicpolicies =~ POL3 1.000 0.000
## 37 Publicpolicies =~ POL4 0.943 0.103
## 38 Publicpolicies =~ POL5 1.068 0.092
## 39 Publicpolicies =~ POL7 1.049 0.103
## 40 Performance ~ EconomicSpecialization 0.391 0.131
## 41 Performance ~ EconomicDiversification -0.181 0.180
## 42 Performance ~ RelationalNetworks -0.145 0.103
## 43 Performance ~ InternationalRelations 0.602 0.107
## 44 Performance ~ TechnologicalHeterogeneity 0.203 0.096
## 45 Performance ~ InstitutionalEnvironment -0.316 0.184
## 46 Performance ~ Publicpolicies 0.514 0.142
## 47 D1 | t1 -1.199 0.119
## 48 D1 | t2 -0.721 0.100
## 49 D1 | t3 0.455 0.094
## 50 D1 | t4 1.347 0.128
## 51 D2 | t1 -0.790 0.102
## 52 D2 | t2 -0.205 0.092
## 53 D2 | t3 0.514 0.095
## 54 D2 | t4 0.923 0.107
## 55 D3 | t1 -1.940 0.191
## 56 D3 | t2 -1.147 0.116
## 57 D3 | t3 0.020 0.091
## 58 D3 | t4 1.007 0.110
## 59 D4 | t1 -1.673 0.156
## 60 D4 | t2 -1.098 0.114
## 61 D4 | t3 0.272 0.092
## 62 D4 | t4 1.173 0.118
## 63 D5 | t1 -1.729 0.162
## 64 D5 | t2 -1.147 0.116
## 65 D5 | t3 -0.072 0.091
## 66 D5 | t4 1.051 0.112
## 67 D6 | t1 -1.490 0.139
## 68 D6 | t2 -0.943 0.107
## 69 D6 | t3 0.232 0.092
## 70 D6 | t4 1.452 0.136
## 71 ESP1 | t1 -2.035 0.206
## 72 ESP1 | t2 -1.199 0.119
## 73 ESP1 | t3 -0.773 0.102
## 74 ESP1 | t4 -0.138 0.091
## 75 ESP3 | t1 -2.152 0.229
## 76 ESP3 | t2 -1.531 0.143
## 77 ESP3 | t3 -0.232 0.092
## 78 ESP4 | t1 -2.560 0.348
## 79 ESP4 | t2 -1.791 0.170
## 80 ESP4 | t3 -0.985 0.109
## 81 ESP4 | t4 -0.033 0.091
## 82 ESP5 | t1 -2.152 0.229
## 83 ESP5 | t2 -1.315 0.126
## 84 ESP5 | t3 -0.245 0.092
## 85 DIV2 | t1 -2.560 0.348
## 86 DIV2 | t2 -1.791 0.170
## 87 DIV2 | t3 -1.227 0.121
## 88 DIV2 | t4 0.191 0.092
## 89 DIV3 | t1 -2.309 0.266
## 90 DIV3 | t2 -1.940 0.191
## 91 DIV3 | t3 -1.173 0.118
## 92 DIV3 | t4 0.099 0.091
## 93 DIV4 | t1 -2.560 0.348
## 94 DIV4 | t2 -1.791 0.170
## 95 DIV4 | t3 -1.147 0.116
## 96 DIV4 | t4 0.072 0.091
## 97 DIV5 | t1 -2.560 0.348
## 98 DIV5 | t2 -1.452 0.136
## 99 DIV5 | t3 -0.369 0.093
## 100 RED1 | t1 -1.860 0.179
## 101 RED1 | t2 -1.173 0.118
## 102 RED1 | t3 -0.412 0.094
## 103 RED1 | t4 0.412 0.094
## 104 RED2 | t1 -2.035 0.206
## 105 RED2 | t2 -1.531 0.143
## 106 RED2 | t3 -0.654 0.098
## 107 RED2 | t4 0.440 0.094
## 108 RED3 | t1 -1.860 0.179
## 109 RED3 | t2 -1.622 0.151
## 110 RED3 | t3 -0.738 0.101
## 111 RED3 | t4 0.355 0.093
## 112 RED4 | t1 -2.309 0.266
## 113 RED4 | t2 -1.074 0.113
## 114 RED4 | t3 0.191 0.092
## 115 RED6 | t1 -2.560 0.348
## 116 RED6 | t2 -1.729 0.162
## 117 RED6 | t3 -0.721 0.100
## 118 RED6 | t4 0.575 0.097
## 119 INT1 | t1 -0.773 0.102
## 120 INT1 | t2 -0.341 0.093
## 121 INT1 | t3 0.245 0.092
## 122 INT1 | t4 0.790 0.102
## 123 INT2 | t1 -0.440 0.094
## 124 INT2 | t2 -0.007 0.091
## 125 INT2 | t3 0.286 0.092
## 126 INT2 | t4 1.098 0.114
## 127 INT3 | t1 -0.046 0.091
## 128 INT3 | t2 0.152 0.091
## 129 INT3 | t3 0.529 0.096
## 130 INT3 | t4 0.964 0.108
## 131 INT4 | t1 -0.245 0.092
## 132 INT4 | t2 -0.020 0.091
## 133 INT4 | t3 0.412 0.094
## 134 INT4 | t4 0.985 0.109
## 135 INT5 | t1 -0.397 0.094
## 136 INT5 | t2 -0.099 0.091
## 137 INT5 | t3 0.286 0.092
## 138 INT5 | t4 0.864 0.104
## 139 INT7 | t1 -1.622 0.151
## 140 INT7 | t2 -0.606 0.097
## 141 INT7 | t3 0.085 0.091
## 142 INT7 | t4 0.687 0.099
## 143 TEC1 | t1 -2.035 0.206
## 144 TEC1 | t2 -1.380 0.131
## 145 TEC1 | t3 -0.455 0.094
## 146 TEC1 | t4 0.455 0.094
## 147 TEC3 | t1 -2.560 0.348
## 148 TEC3 | t2 -2.035 0.206
## 149 TEC3 | t3 -0.638 0.098
## 150 TEC3 | t4 0.469 0.095
## 151 TEC4 | t1 -2.309 0.266
## 152 TEC4 | t2 -1.791 0.170
## 153 TEC4 | t3 -0.985 0.109
## 154 TEC4 | t4 0.426 0.094
## 155 TEC5 | t1 -1.122 0.115
## 156 TEC5 | t2 0.245 0.092
## 157 INST1 | t1 -2.560 0.348
## 158 INST1 | t2 -1.860 0.179
## 159 INST1 | t3 -1.098 0.114
## 160 INST1 | t4 0.286 0.092
## 161 INST2 | t1 -2.152 0.229
## 162 INST2 | t2 -0.903 0.106
## 163 INST2 | t3 0.514 0.095
## 164 INST3 | t1 -2.560 0.348
## 165 INST3 | t2 -1.791 0.170
## 166 INST3 | t3 -0.773 0.102
## 167 INST3 | t4 0.469 0.095
## 168 INST4 | t1 -1.860 0.179
## 169 INST4 | t2 -1.029 0.111
## 170 INST4 | t3 0.369 0.093
## 171 INST5 | t1 -2.560 0.348
## 172 INST5 | t2 -1.622 0.151
## 173 INST5 | t3 -1.173 0.118
## 174 INST5 | t4 0.383 0.093
## 175 INST6 | t1 -2.560 0.348
## 176 INST6 | t2 -1.940 0.191
## 177 INST6 | t3 -1.173 0.118
## 178 INST6 | t4 0.138 0.091
## 179 POL3 | t1 -1.147 0.116
## 180 POL3 | t2 -0.790 0.102
## 181 POL3 | t3 -0.286 0.092
## 182 POL3 | t4 0.845 0.104
## 183 POL4 | t1 -2.035 0.206
## 184 POL4 | t2 -1.199 0.119
## 185 POL4 | t3 -0.469 0.095
## 186 POL4 | t4 0.544 0.096
## 187 POL5 | t1 -1.673 0.156
## 188 POL5 | t2 -1.285 0.124
## 189 POL5 | t3 -0.341 0.093
## 190 POL5 | t4 0.773 0.102
## 191 POL7 | t1 -1.673 0.156
## 192 POL7 | t2 -1.415 0.133
## 193 POL7 | t3 -0.773 0.102
## 194 POL7 | t4 0.383 0.093
## 195 D1 ~~ D1 0.417 0.000
## 196 D2 ~~ D2 0.518 0.000
## 197 D3 ~~ D3 0.471 0.000
## 198 D4 ~~ D4 0.428 0.000
## 199 D5 ~~ D5 0.635 0.000
## 200 D6 ~~ D6 0.625 0.000
## 201 ESP1 ~~ ESP1 0.457 0.000
## 202 ESP3 ~~ ESP3 0.384 0.000
## 203 ESP4 ~~ ESP4 0.486 0.000
## 204 ESP5 ~~ ESP5 0.199 0.000
## 205 DIV2 ~~ DIV2 0.654 0.000
## 206 DIV3 ~~ DIV3 0.795 0.000
## 207 DIV4 ~~ DIV4 0.589 0.000
## 208 DIV5 ~~ DIV5 0.509 0.000
## 209 RED1 ~~ RED1 0.409 0.000
## 210 RED2 ~~ RED2 0.451 0.000
## 211 RED3 ~~ RED3 0.644 0.000
## 212 RED4 ~~ RED4 0.667 0.000
## 213 RED6 ~~ RED6 0.556 0.000
## 214 INT1 ~~ INT1 0.497 0.000
## 215 INT2 ~~ INT2 0.212 0.000
## 216 INT3 ~~ INT3 0.229 0.000
## 217 INT4 ~~ INT4 0.143 0.000
## 218 INT5 ~~ INT5 0.139 0.000
## 219 INT7 ~~ INT7 0.547 0.000
## 220 TEC1 ~~ TEC1 0.328 0.000
## 221 TEC3 ~~ TEC3 0.675 0.000
## 222 TEC4 ~~ TEC4 0.445 0.000
## 223 TEC5 ~~ TEC5 0.639 0.000
## 224 INST1 ~~ INST1 0.644 0.000
## 225 INST2 ~~ INST2 0.620 0.000
## 226 INST3 ~~ INST3 0.557 0.000
## 227 INST4 ~~ INST4 0.651 0.000
## 228 INST5 ~~ INST5 0.603 0.000
## 229 INST6 ~~ INST6 0.608 0.000
## 230 POL3 ~~ POL3 0.505 0.000
## 231 POL4 ~~ POL4 0.560 0.000
## 232 POL5 ~~ POL5 0.435 0.000
## 233 POL7 ~~ POL7 0.456 0.000
## 234 Performance ~~ Performance 0.163 0.037
## 235 EconomicSpecialization ~~ EconomicSpecialization 0.543 0.081
## 236 EconomicDiversification ~~ EconomicDiversification 0.346 0.075
## 237 RelationalNetworks ~~ RelationalNetworks 0.591 0.077
## 238 InternationalRelations ~~ InternationalRelations 0.503 0.059
## 239 TechnologicalHeterogeneity ~~ TechnologicalHeterogeneity 0.672 0.077
## 240 InstitutionalEnvironment ~~ InstitutionalEnvironment 0.356 0.074
## 241 Publicpolicies ~~ Publicpolicies 0.495 0.072
## 242 EconomicSpecialization ~~ EconomicDiversification 0.286 0.050
## 243 EconomicSpecialization ~~ RelationalNetworks -0.015 0.048
## 244 EconomicSpecialization ~~ InternationalRelations -0.037 0.043
## 245 EconomicSpecialization ~~ TechnologicalHeterogeneity 0.124 0.052
## 246 EconomicSpecialization ~~ InstitutionalEnvironment 0.017 0.037
## 247 EconomicSpecialization ~~ Publicpolicies -0.018 0.049
## 248 EconomicDiversification ~~ RelationalNetworks 0.125 0.045
## 249 EconomicDiversification ~~ InternationalRelations 0.016 0.039
## 250 EconomicDiversification ~~ TechnologicalHeterogeneity 0.108 0.045
## 251 EconomicDiversification ~~ InstitutionalEnvironment 0.101 0.033
## 252 EconomicDiversification ~~ Publicpolicies 0.055 0.038
## 253 RelationalNetworks ~~ InternationalRelations 0.286 0.040
## 254 RelationalNetworks ~~ TechnologicalHeterogeneity 0.243 0.051
## 255 RelationalNetworks ~~ InstitutionalEnvironment 0.239 0.042
## 256 RelationalNetworks ~~ Publicpolicies 0.317 0.049
## 257 InternationalRelations ~~ TechnologicalHeterogeneity 0.348 0.045
## 258 InternationalRelations ~~ InstitutionalEnvironment 0.174 0.034
## 259 InternationalRelations ~~ Publicpolicies 0.228 0.042
## 260 TechnologicalHeterogeneity ~~ InstitutionalEnvironment 0.232 0.046
## 261 TechnologicalHeterogeneity ~~ Publicpolicies 0.254 0.049
## 262 InstitutionalEnvironment ~~ Publicpolicies 0.319 0.050
## 263 D1 ~*~ D1 1.000 0.000
## 264 D2 ~*~ D2 1.000 0.000
## 265 D3 ~*~ D3 1.000 0.000
## 266 D4 ~*~ D4 1.000 0.000
## 267 D5 ~*~ D5 1.000 0.000
## 268 D6 ~*~ D6 1.000 0.000
## 269 ESP1 ~*~ ESP1 1.000 0.000
## 270 ESP3 ~*~ ESP3 1.000 0.000
## 271 ESP4 ~*~ ESP4 1.000 0.000
## 272 ESP5 ~*~ ESP5 1.000 0.000
## 273 DIV2 ~*~ DIV2 1.000 0.000
## 274 DIV3 ~*~ DIV3 1.000 0.000
## 275 DIV4 ~*~ DIV4 1.000 0.000
## 276 DIV5 ~*~ DIV5 1.000 0.000
## 277 RED1 ~*~ RED1 1.000 0.000
## 278 RED2 ~*~ RED2 1.000 0.000
## 279 RED3 ~*~ RED3 1.000 0.000
## 280 RED4 ~*~ RED4 1.000 0.000
## 281 RED6 ~*~ RED6 1.000 0.000
## 282 INT1 ~*~ INT1 1.000 0.000
## 283 INT2 ~*~ INT2 1.000 0.000
## 284 INT3 ~*~ INT3 1.000 0.000
## 285 INT4 ~*~ INT4 1.000 0.000
## 286 INT5 ~*~ INT5 1.000 0.000
## 287 INT7 ~*~ INT7 1.000 0.000
## 288 TEC1 ~*~ TEC1 1.000 0.000
## 289 TEC3 ~*~ TEC3 1.000 0.000
## 290 TEC4 ~*~ TEC4 1.000 0.000
## 291 TEC5 ~*~ TEC5 1.000 0.000
## 292 INST1 ~*~ INST1 1.000 0.000
## 293 INST2 ~*~ INST2 1.000 0.000
## 294 INST3 ~*~ INST3 1.000 0.000
## 295 INST4 ~*~ INST4 1.000 0.000
## 296 INST5 ~*~ INST5 1.000 0.000
## 297 INST6 ~*~ INST6 1.000 0.000
## 298 POL3 ~*~ POL3 1.000 0.000
## 299 POL4 ~*~ POL4 1.000 0.000
## 300 POL5 ~*~ POL5 1.000 0.000
## 301 POL7 ~*~ POL7 1.000 0.000
## 302 D1 ~1 0.000 0.000
## 303 D2 ~1 0.000 0.000
## 304 D3 ~1 0.000 0.000
## 305 D4 ~1 0.000 0.000
## 306 D5 ~1 0.000 0.000
## 307 D6 ~1 0.000 0.000
## 308 ESP1 ~1 0.000 0.000
## 309 ESP3 ~1 0.000 0.000
## 310 ESP4 ~1 0.000 0.000
## 311 ESP5 ~1 0.000 0.000
## 312 DIV2 ~1 0.000 0.000
## 313 DIV3 ~1 0.000 0.000
## 314 DIV4 ~1 0.000 0.000
## 315 DIV5 ~1 0.000 0.000
## 316 RED1 ~1 0.000 0.000
## 317 RED2 ~1 0.000 0.000
## 318 RED3 ~1 0.000 0.000
## 319 RED4 ~1 0.000 0.000
## 320 RED6 ~1 0.000 0.000
## 321 INT1 ~1 0.000 0.000
## 322 INT2 ~1 0.000 0.000
## 323 INT3 ~1 0.000 0.000
## 324 INT4 ~1 0.000 0.000
## 325 INT5 ~1 0.000 0.000
## 326 INT7 ~1 0.000 0.000
## 327 TEC1 ~1 0.000 0.000
## 328 TEC3 ~1 0.000 0.000
## 329 TEC4 ~1 0.000 0.000
## 330 TEC5 ~1 0.000 0.000
## 331 INST1 ~1 0.000 0.000
## 332 INST2 ~1 0.000 0.000
## 333 INST3 ~1 0.000 0.000
## 334 INST4 ~1 0.000 0.000
## 335 INST5 ~1 0.000 0.000
## 336 INST6 ~1 0.000 0.000
## 337 POL3 ~1 0.000 0.000
## 338 POL4 ~1 0.000 0.000
## 339 POL5 ~1 0.000 0.000
## 340 POL7 ~1 0.000 0.000
## 341 Performance ~1 0.000 0.000
## 342 EconomicSpecialization ~1 0.000 0.000
## 343 EconomicDiversification ~1 0.000 0.000
## 344 RelationalNetworks ~1 0.000 0.000
## 345 InternationalRelations ~1 0.000 0.000
## 346 TechnologicalHeterogeneity ~1 0.000 0.000
## 347 InstitutionalEnvironment ~1 0.000 0.000
## 348 Publicpolicies ~1 0.000 0.000
## z pvalue ci.lower ci.upper std.lv std.all std.nox
## 1 NA NA 1.000 1.000 0.764 0.764 0.764
## 2 12.195 0.000 0.763 1.055 0.694 0.694 0.694
## 3 12.793 0.000 0.807 1.098 0.728 0.728 0.728
## 4 14.498 0.000 0.856 1.124 0.756 0.756 0.756
## 5 11.607 0.000 0.658 0.925 0.605 0.605 0.605
## 6 11.603 0.000 0.667 0.938 0.613 0.613 0.613
## 7 NA NA 1.000 1.000 0.737 0.737 0.737
## 8 10.303 0.000 0.862 1.267 0.785 0.785 0.785
## 9 9.288 0.000 0.768 1.178 0.717 0.717 0.717
## 10 11.751 0.000 1.011 1.416 0.895 0.895 0.895
## 11 NA NA 1.000 1.000 0.588 0.588 0.588
## 12 4.738 0.000 0.452 1.089 0.453 0.453 0.453
## 13 6.524 0.000 0.762 1.417 0.641 0.641 0.641
## 14 6.270 0.000 0.819 1.563 0.701 0.701 0.701
## 15 NA NA 1.000 1.000 0.769 0.769 0.769
## 16 9.033 0.000 0.755 1.173 0.741 0.741 0.741
## 17 8.835 0.000 0.604 0.949 0.597 0.597 0.597
## 18 8.026 0.000 0.567 0.934 0.577 0.577 0.577
## 19 10.060 0.000 0.698 1.035 0.666 0.666 0.666
## 20 NA NA 1.000 1.000 0.709 0.709 0.709
## 21 16.634 0.000 1.104 1.399 0.887 0.887 0.887
## 22 16.296 0.000 1.090 1.388 0.878 0.878 0.878
## 23 17.050 0.000 1.156 1.456 0.926 0.926 0.926
## 24 17.326 0.000 1.161 1.457 0.928 0.928 0.928
## 25 12.402 0.000 0.800 1.100 0.673 0.673 0.673
## 26 NA NA 1.000 1.000 0.820 0.820 0.820
## 27 7.771 0.000 0.520 0.870 0.570 0.570 0.570
## 28 9.657 0.000 0.724 1.093 0.745 0.745 0.745
## 29 7.384 0.000 0.539 0.928 0.601 0.601 0.601
## 30 NA NA 1.000 1.000 0.597 0.597 0.597
## 31 6.542 0.000 0.723 1.342 0.616 0.616 0.616
## 32 7.458 0.000 0.822 1.408 0.665 0.665 0.665
## 33 6.565 0.000 0.694 1.286 0.591 0.591 0.591
## 34 7.346 0.000 0.774 1.338 0.630 0.630 0.630
## 35 6.988 0.000 0.755 1.343 0.626 0.626 0.626
## 36 NA NA 1.000 1.000 0.703 0.703 0.703
## 37 9.155 0.000 0.741 1.145 0.663 0.663 0.663
## 38 11.606 0.000 0.888 1.249 0.752 0.752 0.752
## 39 10.179 0.000 0.847 1.250 0.738 0.738 0.738
## 40 2.994 0.003 0.135 0.648 0.378 0.378 0.378
## 41 -1.004 0.316 -0.533 0.172 -0.139 -0.139 -0.139
## 42 -1.407 0.159 -0.347 0.057 -0.146 -0.146 -0.146
## 43 5.608 0.000 0.391 0.812 0.558 0.558 0.558
## 44 2.113 0.035 0.015 0.391 0.218 0.218 0.218
## 45 -1.717 0.086 -0.677 0.045 -0.247 -0.247 -0.247
## 46 3.612 0.000 0.235 0.792 0.473 0.473 0.473
## 47 -10.063 0.000 -1.433 -0.966 -1.199 -1.199 -1.199
## 48 -7.202 0.000 -0.917 -0.524 -0.721 -0.721 -0.721
## 49 4.817 0.000 0.270 0.640 0.455 0.455 0.455
## 50 10.501 0.000 1.096 1.598 1.347 1.347 1.347
## 51 -7.746 0.000 -0.990 -0.590 -0.790 -0.790 -0.790
## 52 -2.236 0.025 -0.384 -0.025 -0.205 -0.205 -0.205
## 53 5.385 0.000 0.327 0.701 0.514 0.514 0.514
## 54 8.666 0.000 0.714 1.132 0.923 0.923 0.923
## 55 -10.174 0.000 -2.314 -1.566 -1.940 -1.940 -1.940
## 56 -9.856 0.000 -1.375 -0.919 -1.147 -1.147 -1.147
## 57 0.217 0.829 -0.159 0.198 0.020 0.020 0.020
## 58 9.166 0.000 0.791 1.222 1.007 1.007 1.007
## 59 -10.707 0.000 -1.980 -1.367 -1.673 -1.673 -1.673
## 60 -9.636 0.000 -1.321 -0.875 -1.098 -1.098 -1.098
## 61 2.955 0.003 0.092 0.453 0.272 0.272 0.272
## 62 9.962 0.000 0.942 1.404 1.173 1.173 1.173
## 63 -10.644 0.000 -2.048 -1.411 -1.729 -1.729 -1.729
## 64 -9.856 0.000 -1.375 -0.919 -1.147 -1.147 -1.147
## 65 -0.794 0.427 -0.251 0.106 -0.072 -0.072 -0.072
## 66 9.406 0.000 0.832 1.270 1.051 1.051 1.051
## 67 -10.718 0.000 -1.763 -1.218 -1.490 -1.490 -1.490
## 68 -8.793 0.000 -1.153 -0.733 -0.943 -0.943 -0.943
## 69 2.524 0.012 0.052 0.412 0.232 0.232 0.232
## 70 10.679 0.000 1.185 1.718 1.452 1.452 1.452
## 71 -9.861 0.000 -2.439 -1.630 -2.035 -2.035 -2.035
## 72 -10.063 0.000 -1.433 -0.966 -1.199 -1.199 -1.199
## 73 -7.611 0.000 -0.971 -0.574 -0.773 -0.773 -0.773
## 74 -1.515 0.130 -0.317 0.041 -0.138 -0.138 -0.138
## 75 -9.398 0.000 -2.601 -1.703 -2.152 -2.152 -2.152
## 76 -10.743 0.000 -1.811 -1.252 -1.531 -1.531 -1.531
## 77 -2.524 0.012 -0.412 -0.052 -0.232 -0.232 -0.232
## 78 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 79 -10.542 0.000 -2.124 -1.458 -1.791 -1.791 -1.791
## 80 -9.043 0.000 -1.199 -0.772 -0.985 -0.985 -0.985
## 81 -0.361 0.718 -0.211 0.145 -0.033 -0.033 -0.033
## 82 -9.398 0.000 -2.601 -1.703 -2.152 -2.152 -2.152
## 83 -10.425 0.000 -1.562 -1.068 -1.315 -1.315 -1.315
## 84 -2.668 0.008 -0.425 -0.065 -0.245 -0.245 -0.245
## 85 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 86 -10.542 0.000 -2.124 -1.458 -1.791 -1.791 -1.791
## 87 -10.161 0.000 -1.463 -0.990 -1.227 -1.227 -1.227
## 88 2.092 0.036 0.012 0.371 0.191 0.191 0.191
## 89 -8.675 0.000 -2.831 -1.787 -2.309 -2.309 -2.309
## 90 -10.174 0.000 -2.314 -1.566 -1.940 -1.940 -1.940
## 91 -9.962 0.000 -1.404 -0.942 -1.173 -1.173 -1.173
## 92 1.082 0.279 -0.080 0.277 0.099 0.099 0.099
## 93 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 94 -10.542 0.000 -2.124 -1.458 -1.791 -1.791 -1.791
## 95 -9.856 0.000 -1.375 -0.919 -1.147 -1.147 -1.147
## 96 0.794 0.427 -0.106 0.251 0.072 0.072 0.072
## 97 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 98 -10.679 0.000 -1.718 -1.185 -1.452 -1.452 -1.452
## 99 -3.960 0.000 -0.552 -0.186 -0.369 -0.369 -0.369
## 100 -10.392 0.000 -2.211 -1.510 -1.860 -1.860 -1.860
## 101 -9.962 0.000 -1.404 -0.942 -1.173 -1.173 -1.173
## 102 -4.389 0.000 -0.595 -0.228 -0.412 -0.412 -0.412
## 103 4.389 0.000 0.228 0.595 0.412 0.412 0.412
## 104 -9.861 0.000 -2.439 -1.630 -2.035 -2.035 -2.035
## 105 -10.743 0.000 -1.811 -1.252 -1.531 -1.531 -1.531
## 106 -6.650 0.000 -0.847 -0.461 -0.654 -0.654 -0.654
## 107 4.674 0.000 0.256 0.625 0.440 0.440 0.440
## 108 -10.392 0.000 -2.211 -1.510 -1.860 -1.860 -1.860
## 109 -10.741 0.000 -1.918 -1.326 -1.622 -1.622 -1.622
## 110 -7.339 0.000 -0.935 -0.541 -0.738 -0.738 -0.738
## 111 3.817 0.000 0.173 0.537 0.355 0.355 0.355
## 112 -8.675 0.000 -2.831 -1.787 -2.309 -2.309 -2.309
## 113 -9.522 0.000 -1.295 -0.853 -1.074 -1.074 -1.074
## 114 2.092 0.036 0.012 0.371 0.191 0.191 0.191
## 115 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 116 -10.644 0.000 -2.048 -1.411 -1.729 -1.729 -1.729
## 117 -7.202 0.000 -0.917 -0.524 -0.721 -0.721 -0.721
## 118 5.950 0.000 0.385 0.764 0.575 0.575 0.575
## 119 -7.611 0.000 -0.971 -0.574 -0.773 -0.773 -0.773
## 120 -3.673 0.000 -0.523 -0.159 -0.341 -0.341 -0.341
## 121 2.668 0.008 0.065 0.425 0.245 0.245 0.245
## 122 7.746 0.000 0.590 0.990 0.790 0.790 0.790
## 123 -4.674 0.000 -0.625 -0.256 -0.440 -0.440 -0.440
## 124 -0.072 0.942 -0.185 0.172 -0.007 -0.007 -0.007
## 125 3.099 0.002 0.105 0.467 0.286 0.286 0.286
## 126 9.636 0.000 0.875 1.321 1.098 1.098 1.098
## 127 -0.505 0.613 -0.224 0.132 -0.046 -0.046 -0.046
## 128 1.659 0.097 -0.027 0.330 0.152 0.152 0.152
## 129 5.527 0.000 0.341 0.716 0.529 0.529 0.529
## 130 8.919 0.000 0.752 1.176 0.964 0.964 0.964
## 131 -2.668 0.008 -0.425 -0.065 -0.245 -0.245 -0.245
## 132 -0.217 0.829 -0.198 0.159 -0.020 -0.020 -0.020
## 133 4.389 0.000 0.228 0.595 0.412 0.412 0.412
## 134 9.043 0.000 0.772 1.199 0.985 0.985 0.985
## 135 -4.246 0.000 -0.581 -0.214 -0.397 -0.397 -0.397
## 136 -1.082 0.279 -0.277 0.080 -0.099 -0.099 -0.099
## 137 3.099 0.002 0.105 0.467 0.286 0.286 0.286
## 138 8.278 0.000 0.660 1.069 0.864 0.864 0.864
## 139 -10.741 0.000 -1.918 -1.326 -1.622 -1.622 -1.622
## 140 -6.231 0.000 -0.797 -0.415 -0.606 -0.606 -0.606
## 141 0.938 0.348 -0.093 0.264 0.085 0.085 0.085
## 142 6.927 0.000 0.493 0.881 0.687 0.687 0.687
## 143 -9.861 0.000 -2.439 -1.630 -2.035 -2.035 -2.035
## 144 -10.569 0.000 -1.636 -1.124 -1.380 -1.380 -1.380
## 145 -4.817 0.000 -0.640 -0.270 -0.455 -0.455 -0.455
## 146 4.817 0.000 0.270 0.640 0.455 0.455 0.455
## 147 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 148 -9.861 0.000 -2.439 -1.630 -2.035 -2.035 -2.035
## 149 -6.510 0.000 -0.830 -0.446 -0.638 -0.638 -0.638
## 150 4.959 0.000 0.284 0.655 0.469 0.469 0.469
## 151 -8.675 0.000 -2.831 -1.787 -2.309 -2.309 -2.309
## 152 -10.542 0.000 -2.124 -1.458 -1.791 -1.791 -1.791
## 153 -9.043 0.000 -1.199 -0.772 -0.985 -0.985 -0.985
## 154 4.532 0.000 0.242 0.610 0.426 0.426 0.426
## 155 -9.748 0.000 -1.348 -0.897 -1.122 -1.122 -1.122
## 156 2.668 0.008 0.065 0.425 0.245 0.245 0.245
## 157 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 158 -10.392 0.000 -2.211 -1.510 -1.860 -1.860 -1.860
## 159 -9.636 0.000 -1.321 -0.875 -1.098 -1.098 -1.098
## 160 3.099 0.002 0.105 0.467 0.286 0.286 0.286
## 161 -9.398 0.000 -2.601 -1.703 -2.152 -2.152 -2.152
## 162 -8.538 0.000 -1.110 -0.696 -0.903 -0.903 -0.903
## 163 5.385 0.000 0.327 0.701 0.514 0.514 0.514
## 164 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 165 -10.542 0.000 -2.124 -1.458 -1.791 -1.791 -1.791
## 166 -7.611 0.000 -0.971 -0.574 -0.773 -0.773 -0.773
## 167 4.959 0.000 0.284 0.655 0.469 0.469 0.469
## 168 -10.392 0.000 -2.211 -1.510 -1.860 -1.860 -1.860
## 169 -9.287 0.000 -1.246 -0.812 -1.029 -1.029 -1.029
## 170 3.960 0.000 0.186 0.552 0.369 0.369 0.369
## 171 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 172 -10.741 0.000 -1.918 -1.326 -1.622 -1.622 -1.622
## 173 -9.962 0.000 -1.404 -0.942 -1.173 -1.173 -1.173
## 174 4.103 0.000 0.200 0.566 0.383 0.383 0.383
## 175 -7.366 0.000 -3.241 -1.879 -2.560 -2.560 -2.560
## 176 -10.174 0.000 -2.314 -1.566 -1.940 -1.940 -1.940
## 177 -9.962 0.000 -1.404 -0.942 -1.173 -1.173 -1.173
## 178 1.515 0.130 -0.041 0.317 0.138 0.138 0.138
## 179 -9.856 0.000 -1.375 -0.919 -1.147 -1.147 -1.147
## 180 -7.746 0.000 -0.990 -0.590 -0.790 -0.790 -0.790
## 181 -3.099 0.002 -0.467 -0.105 -0.286 -0.286 -0.286
## 182 8.146 0.000 0.642 1.049 0.845 0.845 0.845
## 183 -9.861 0.000 -2.439 -1.630 -2.035 -2.035 -2.035
## 184 -10.063 0.000 -1.433 -0.966 -1.199 -1.199 -1.199
## 185 -4.959 0.000 -0.655 -0.284 -0.469 -0.469 -0.469
## 186 5.668 0.000 0.356 0.732 0.544 0.544 0.544
## 187 -10.707 0.000 -1.980 -1.367 -1.673 -1.673 -1.673
## 188 -10.342 0.000 -1.528 -1.041 -1.285 -1.285 -1.285
## 189 -3.673 0.000 -0.523 -0.159 -0.341 -0.341 -0.341
## 190 7.611 0.000 0.574 0.971 0.773 0.773 0.773
## 191 -10.707 0.000 -1.980 -1.367 -1.673 -1.673 -1.673
## 192 -10.629 0.000 -1.676 -1.154 -1.415 -1.415 -1.415
## 193 -7.611 0.000 -0.971 -0.574 -0.773 -0.773 -0.773
## 194 4.103 0.000 0.200 0.566 0.383 0.383 0.383
## 195 NA NA 0.417 0.417 0.417 0.417 0.417
## 196 NA NA 0.518 0.518 0.518 0.518 0.518
## 197 NA NA 0.471 0.471 0.471 0.471 0.471
## 198 NA NA 0.428 0.428 0.428 0.428 0.428
## 199 NA NA 0.635 0.635 0.635 0.635 0.635
## 200 NA NA 0.625 0.625 0.625 0.625 0.625
## 201 NA NA 0.457 0.457 0.457 0.457 0.457
## 202 NA NA 0.384 0.384 0.384 0.384 0.384
## 203 NA NA 0.486 0.486 0.486 0.486 0.486
## 204 NA NA 0.199 0.199 0.199 0.199 0.199
## 205 NA NA 0.654 0.654 0.654 0.654 0.654
## 206 NA NA 0.795 0.795 0.795 0.795 0.795
## 207 NA NA 0.589 0.589 0.589 0.589 0.589
## 208 NA NA 0.509 0.509 0.509 0.509 0.509
## 209 NA NA 0.409 0.409 0.409 0.409 0.409
## 210 NA NA 0.451 0.451 0.451 0.451 0.451
## 211 NA NA 0.644 0.644 0.644 0.644 0.644
## 212 NA NA 0.667 0.667 0.667 0.667 0.667
## 213 NA NA 0.556 0.556 0.556 0.556 0.556
## 214 NA NA 0.497 0.497 0.497 0.497 0.497
## 215 NA NA 0.212 0.212 0.212 0.212 0.212
## 216 NA NA 0.229 0.229 0.229 0.229 0.229
## 217 NA NA 0.143 0.143 0.143 0.143 0.143
## 218 NA NA 0.139 0.139 0.139 0.139 0.139
## 219 NA NA 0.547 0.547 0.547 0.547 0.547
## 220 NA NA 0.328 0.328 0.328 0.328 0.328
## 221 NA NA 0.675 0.675 0.675 0.675 0.675
## 222 NA NA 0.445 0.445 0.445 0.445 0.445
## 223 NA NA 0.639 0.639 0.639 0.639 0.639
## 224 NA NA 0.644 0.644 0.644 0.644 0.644
## 225 NA NA 0.620 0.620 0.620 0.620 0.620
## 226 NA NA 0.557 0.557 0.557 0.557 0.557
## 227 NA NA 0.651 0.651 0.651 0.651 0.651
## 228 NA NA 0.603 0.603 0.603 0.603 0.603
## 229 NA NA 0.608 0.608 0.608 0.608 0.608
## 230 NA NA 0.505 0.505 0.505 0.505 0.505
## 231 NA NA 0.560 0.560 0.560 0.560 0.560
## 232 NA NA 0.435 0.435 0.435 0.435 0.435
## 233 NA NA 0.456 0.456 0.456 0.456 0.456
## 234 4.336 0.000 0.089 0.236 0.279 0.279 0.279
## 235 6.736 0.000 0.385 0.702 1.000 1.000 1.000
## 236 4.600 0.000 0.199 0.494 1.000 1.000 1.000
## 237 7.698 0.000 0.440 0.741 1.000 1.000 1.000
## 238 8.557 0.000 0.387 0.618 1.000 1.000 1.000
## 239 8.752 0.000 0.521 0.822 1.000 1.000 1.000
## 240 4.794 0.000 0.211 0.502 1.000 1.000 1.000
## 241 6.887 0.000 0.354 0.636 1.000 1.000 1.000
## 242 5.678 0.000 0.187 0.385 0.659 0.659 0.659
## 243 -0.316 0.752 -0.110 0.079 -0.027 -0.027 -0.027
## 244 -0.862 0.389 -0.122 0.047 -0.071 -0.071 -0.071
## 245 2.357 0.018 0.021 0.227 0.205 0.205 0.205
## 246 0.462 0.644 -0.056 0.091 0.039 0.039 0.039
## 247 -0.366 0.714 -0.113 0.077 -0.034 -0.034 -0.034
## 248 2.792 0.005 0.037 0.212 0.276 0.276 0.276
## 249 0.401 0.689 -0.061 0.093 0.038 0.038 0.038
## 250 2.433 0.015 0.021 0.196 0.225 0.225 0.225
## 251 3.069 0.002 0.037 0.166 0.289 0.289 0.289
## 252 1.440 0.150 -0.020 0.131 0.134 0.134 0.134
## 253 7.125 0.000 0.208 0.365 0.526 0.526 0.526
## 254 4.773 0.000 0.143 0.343 0.386 0.386 0.386
## 255 5.642 0.000 0.156 0.322 0.521 0.521 0.521
## 256 6.506 0.000 0.222 0.413 0.587 0.587 0.587
## 257 7.677 0.000 0.259 0.436 0.598 0.598 0.598
## 258 5.133 0.000 0.108 0.241 0.411 0.411 0.411
## 259 5.463 0.000 0.146 0.310 0.458 0.458 0.458
## 260 5.050 0.000 0.142 0.322 0.474 0.474 0.474
## 261 5.136 0.000 0.157 0.351 0.440 0.440 0.440
## 262 6.349 0.000 0.220 0.417 0.759 0.759 0.759
## 263 NA NA 1.000 1.000 1.000 1.000 1.000
## 264 NA NA 1.000 1.000 1.000 1.000 1.000
## 265 NA NA 1.000 1.000 1.000 1.000 1.000
## 266 NA NA 1.000 1.000 1.000 1.000 1.000
## 267 NA NA 1.000 1.000 1.000 1.000 1.000
## 268 NA NA 1.000 1.000 1.000 1.000 1.000
## 269 NA NA 1.000 1.000 1.000 1.000 1.000
## 270 NA NA 1.000 1.000 1.000 1.000 1.000
## 271 NA NA 1.000 1.000 1.000 1.000 1.000
## 272 NA NA 1.000 1.000 1.000 1.000 1.000
## 273 NA NA 1.000 1.000 1.000 1.000 1.000
## 274 NA NA 1.000 1.000 1.000 1.000 1.000
## 275 NA NA 1.000 1.000 1.000 1.000 1.000
## 276 NA NA 1.000 1.000 1.000 1.000 1.000
## 277 NA NA 1.000 1.000 1.000 1.000 1.000
## 278 NA NA 1.000 1.000 1.000 1.000 1.000
## 279 NA NA 1.000 1.000 1.000 1.000 1.000
## 280 NA NA 1.000 1.000 1.000 1.000 1.000
## 281 NA NA 1.000 1.000 1.000 1.000 1.000
## 282 NA NA 1.000 1.000 1.000 1.000 1.000
## 283 NA NA 1.000 1.000 1.000 1.000 1.000
## 284 NA NA 1.000 1.000 1.000 1.000 1.000
## 285 NA NA 1.000 1.000 1.000 1.000 1.000
## 286 NA NA 1.000 1.000 1.000 1.000 1.000
## 287 NA NA 1.000 1.000 1.000 1.000 1.000
## 288 NA NA 1.000 1.000 1.000 1.000 1.000
## 289 NA NA 1.000 1.000 1.000 1.000 1.000
## 290 NA NA 1.000 1.000 1.000 1.000 1.000
## 291 NA NA 1.000 1.000 1.000 1.000 1.000
## 292 NA NA 1.000 1.000 1.000 1.000 1.000
## 293 NA NA 1.000 1.000 1.000 1.000 1.000
## 294 NA NA 1.000 1.000 1.000 1.000 1.000
## 295 NA NA 1.000 1.000 1.000 1.000 1.000
## 296 NA NA 1.000 1.000 1.000 1.000 1.000
## 297 NA NA 1.000 1.000 1.000 1.000 1.000
## 298 NA NA 1.000 1.000 1.000 1.000 1.000
## 299 NA NA 1.000 1.000 1.000 1.000 1.000
## 300 NA NA 1.000 1.000 1.000 1.000 1.000
## 301 NA NA 1.000 1.000 1.000 1.000 1.000
## 302 NA NA 0.000 0.000 0.000 0.000 0.000
## 303 NA NA 0.000 0.000 0.000 0.000 0.000
## 304 NA NA 0.000 0.000 0.000 0.000 0.000
## 305 NA NA 0.000 0.000 0.000 0.000 0.000
## 306 NA NA 0.000 0.000 0.000 0.000 0.000
## 307 NA NA 0.000 0.000 0.000 0.000 0.000
## 308 NA NA 0.000 0.000 0.000 0.000 0.000
## 309 NA NA 0.000 0.000 0.000 0.000 0.000
## 310 NA NA 0.000 0.000 0.000 0.000 0.000
## 311 NA NA 0.000 0.000 0.000 0.000 0.000
## 312 NA NA 0.000 0.000 0.000 0.000 0.000
## 313 NA NA 0.000 0.000 0.000 0.000 0.000
## 314 NA NA 0.000 0.000 0.000 0.000 0.000
## 315 NA NA 0.000 0.000 0.000 0.000 0.000
## 316 NA NA 0.000 0.000 0.000 0.000 0.000
## 317 NA NA 0.000 0.000 0.000 0.000 0.000
## 318 NA NA 0.000 0.000 0.000 0.000 0.000
## 319 NA NA 0.000 0.000 0.000 0.000 0.000
## 320 NA NA 0.000 0.000 0.000 0.000 0.000
## 321 NA NA 0.000 0.000 0.000 0.000 0.000
## 322 NA NA 0.000 0.000 0.000 0.000 0.000
## 323 NA NA 0.000 0.000 0.000 0.000 0.000
## 324 NA NA 0.000 0.000 0.000 0.000 0.000
## 325 NA NA 0.000 0.000 0.000 0.000 0.000
## 326 NA NA 0.000 0.000 0.000 0.000 0.000
## 327 NA NA 0.000 0.000 0.000 0.000 0.000
## 328 NA NA 0.000 0.000 0.000 0.000 0.000
## 329 NA NA 0.000 0.000 0.000 0.000 0.000
## 330 NA NA 0.000 0.000 0.000 0.000 0.000
## 331 NA NA 0.000 0.000 0.000 0.000 0.000
## 332 NA NA 0.000 0.000 0.000 0.000 0.000
## 333 NA NA 0.000 0.000 0.000 0.000 0.000
## 334 NA NA 0.000 0.000 0.000 0.000 0.000
## 335 NA NA 0.000 0.000 0.000 0.000 0.000
## 336 NA NA 0.000 0.000 0.000 0.000 0.000
## 337 NA NA 0.000 0.000 0.000 0.000 0.000
## 338 NA NA 0.000 0.000 0.000 0.000 0.000
## 339 NA NA 0.000 0.000 0.000 0.000 0.000
## 340 NA NA 0.000 0.000 0.000 0.000 0.000
## 341 NA NA 0.000 0.000 0.000 0.000 0.000
## 342 NA NA 0.000 0.000 0.000 0.000 0.000
## 343 NA NA 0.000 0.000 0.000 0.000 0.000
## 344 NA NA 0.000 0.000 0.000 0.000 0.000
## 345 NA NA 0.000 0.000 0.000 0.000 0.000
## 346 NA NA 0.000 0.000 0.000 0.000 0.000
## 347 NA NA 0.000 0.000 0.000 0.000 0.000
## 348 NA NA 0.000 0.000 0.000 0.000 0.000
inspect(ModeloFit, "r2") # R²
## D1 D2 D3 D4 D5 D6
## 0.583 0.482 0.529 0.572 0.365 0.375
## ESP1 ESP3 ESP4 ESP5 DIV2 DIV3
## 0.543 0.616 0.514 0.801 0.346 0.205
## DIV4 DIV5 RED1 RED2 RED3 RED4
## 0.411 0.491 0.591 0.549 0.356 0.333
## RED6 INT1 INT2 INT3 INT4 INT5
## 0.444 0.503 0.788 0.771 0.857 0.861
## INT7 TEC1 TEC3 TEC4 TEC5 INST1
## 0.453 0.672 0.325 0.555 0.361 0.356
## INST2 INST3 INST4 INST5 INST6 POL3
## 0.380 0.443 0.349 0.397 0.392 0.495
## POL4 POL5 POL7 Performance
## 0.440 0.565 0.544 0.721
inspect(ModeloFit,what="std")$lambda # Loadings
## Prfrmn EcnmcS EcnmcD RltnlN IntrnR TchnlH InsttE Pblcpl
## D1 0.764 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## D2 0.694 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## D3 0.728 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## D4 0.756 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## D5 0.605 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## D6 0.613 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## ESP1 0.000 0.737 0.000 0.000 0.000 0.000 0.000 0.000
## ESP3 0.000 0.785 0.000 0.000 0.000 0.000 0.000 0.000
## ESP4 0.000 0.717 0.000 0.000 0.000 0.000 0.000 0.000
## ESP5 0.000 0.895 0.000 0.000 0.000 0.000 0.000 0.000
## DIV2 0.000 0.000 0.588 0.000 0.000 0.000 0.000 0.000
## DIV3 0.000 0.000 0.453 0.000 0.000 0.000 0.000 0.000
## DIV4 0.000 0.000 0.641 0.000 0.000 0.000 0.000 0.000
## DIV5 0.000 0.000 0.701 0.000 0.000 0.000 0.000 0.000
## RED1 0.000 0.000 0.000 0.769 0.000 0.000 0.000 0.000
## RED2 0.000 0.000 0.000 0.741 0.000 0.000 0.000 0.000
## RED3 0.000 0.000 0.000 0.597 0.000 0.000 0.000 0.000
## RED4 0.000 0.000 0.000 0.577 0.000 0.000 0.000 0.000
## RED6 0.000 0.000 0.000 0.666 0.000 0.000 0.000 0.000
## INT1 0.000 0.000 0.000 0.000 0.709 0.000 0.000 0.000
## INT2 0.000 0.000 0.000 0.000 0.887 0.000 0.000 0.000
## INT3 0.000 0.000 0.000 0.000 0.878 0.000 0.000 0.000
## INT4 0.000 0.000 0.000 0.000 0.926 0.000 0.000 0.000
## INT5 0.000 0.000 0.000 0.000 0.928 0.000 0.000 0.000
## INT7 0.000 0.000 0.000 0.000 0.673 0.000 0.000 0.000
## TEC1 0.000 0.000 0.000 0.000 0.000 0.820 0.000 0.000
## TEC3 0.000 0.000 0.000 0.000 0.000 0.570 0.000 0.000
## TEC4 0.000 0.000 0.000 0.000 0.000 0.745 0.000 0.000
## TEC5 0.000 0.000 0.000 0.000 0.000 0.601 0.000 0.000
## INST1 0.000 0.000 0.000 0.000 0.000 0.000 0.597 0.000
## INST2 0.000 0.000 0.000 0.000 0.000 0.000 0.616 0.000
## INST3 0.000 0.000 0.000 0.000 0.000 0.000 0.665 0.000
## INST4 0.000 0.000 0.000 0.000 0.000 0.000 0.591 0.000
## INST5 0.000 0.000 0.000 0.000 0.000 0.000 0.630 0.000
## INST6 0.000 0.000 0.000 0.000 0.000 0.000 0.626 0.000
## POL3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.703
## POL4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.663
## POL5 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.752
## POL7 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.738
Get the scores for each observation
dados2 <- lavPredict(ModeloFit) # Get the scores for each observation
Modificatio Indices
modindices(ModeloFit, sort. = T, minimum.value = 10.82) # MI, we dind't made any change in the model
## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 510 InternationalRelations =~ INST2 46.151 0.438 0.310 0.310 0.310
## 459 RelationalNetworks =~ ESP3 40.881 -0.359 -0.276 -0.276 -0.276
## 493 InternationalRelations =~ ESP3 36.551 -0.310 -0.220 -0.220 -0.220
## 561 InstitutionalEnvironment =~ ESP3 36.013 -0.435 -0.260 -0.260 -0.260
## 526 TechnologicalHeterogeneity =~ ESP3 35.704 -0.320 -0.262 -0.262 -0.262
## 373 Performance =~ INST2 35.080 0.369 0.282 0.282 0.282
## 516 InternationalRelations =~ POL4 34.597 0.440 0.312 0.312 0.312
## 594 Publicpolicies =~ ESP3 34.180 -0.342 -0.241 -0.241 -0.241
## 545 TechnologicalHeterogeneity =~ INST2 30.406 0.410 0.336 0.336 0.336
## 350 Performance =~ ESP3 30.071 -0.290 -0.222 -0.222 -0.222
## 497 InternationalRelations =~ DIV3 28.489 0.245 0.174 0.174 0.174
## 463 RelationalNetworks =~ DIV3 28.481 0.293 0.226 0.226 0.226
## 424 EconomicDiversification =~ ESP3 28.387 -0.965 -0.568 -0.568 -0.568
## 477 RelationalNetworks =~ INST2 27.661 0.518 0.398 0.398 0.398
## 598 Publicpolicies =~ DIV3 26.232 0.272 0.191 0.191 0.191
## 494 InternationalRelations =~ ESP4 25.418 0.242 0.171 0.171 0.171
## 565 InstitutionalEnvironment =~ DIV3 24.715 0.345 0.206 0.206 0.206
## 527 TechnologicalHeterogeneity =~ ESP4 24.658 0.244 0.200 0.200 0.200
## 588 Publicpolicies =~ D2 24.073 -0.381 -0.268 -0.268 -0.268
## 572 InstitutionalEnvironment =~ RED6 23.378 0.583 0.348 0.348 0.348
## 442 EconomicDiversification =~ INST1 22.133 0.446 0.262 0.262 0.262
## 460 RelationalNetworks =~ ESP4 21.847 0.247 0.190 0.190 0.190
## 351 Performance =~ ESP4 21.482 0.228 0.174 0.174 0.174
## 530 TechnologicalHeterogeneity =~ DIV3 20.696 0.223 0.183 0.183 0.183
## 407 EconomicSpecialization =~ INST1 20.503 0.301 0.222 0.222 0.222
## 354 Performance =~ DIV3 20.153 0.205 0.157 0.157 0.157
## 403 EconomicSpecialization =~ TEC1 19.340 -0.352 -0.259 -0.259 -0.259
## 597 Publicpolicies =~ DIV2 18.386 -0.238 -0.168 -0.168 -0.168
## 578 InstitutionalEnvironment =~ INT7 18.286 0.339 0.202 0.202 0.202
## 605 Publicpolicies =~ RED6 17.938 0.469 0.330 0.330 0.330
## 353 Performance =~ DIV2 17.779 -0.207 -0.158 -0.158 -0.158
## 379 Performance =~ POL4 17.683 0.300 0.230 0.230 0.230
## 831 D6 ~~ POL3 17.025 0.264 0.264 0.470 0.470
## 543 TechnologicalHeterogeneity =~ INT7 16.929 0.349 0.286 0.286 0.286
## 472 RelationalNetworks =~ TEC1 16.767 0.384 0.295 0.295 0.295
## 595 Publicpolicies =~ ESP4 16.751 0.225 0.158 0.158 0.158
## 509 InternationalRelations =~ INST1 16.476 -0.262 -0.186 -0.186 -0.186
## 612 Publicpolicies =~ TEC1 16.072 0.428 0.301 0.301 0.301
## 397 EconomicSpecialization =~ INT1 15.995 0.264 0.195 0.195 0.195
## 555 InstitutionalEnvironment =~ D2 15.967 -0.318 -0.190 -0.190 -0.190
## 562 InstitutionalEnvironment =~ ESP4 15.410 0.268 0.160 0.160 0.160
## 409 EconomicSpecialization =~ INST3 15.267 -0.267 -0.197 -0.197 -0.197
## 548 TechnologicalHeterogeneity =~ INST5 14.479 -0.284 -0.233 -0.233 -0.233
## 564 InstitutionalEnvironment =~ DIV2 13.773 -0.264 -0.157 -0.157 -0.157
## 617 Publicpolicies =~ INST2 13.631 0.689 0.485 0.485 0.485
## 611 Publicpolicies =~ INT7 12.569 0.254 0.179 0.179 0.179
## 613 Publicpolicies =~ TEC3 12.335 -0.325 -0.228 -0.228 -0.228
## 425 EconomicDiversification =~ ESP4 12.264 0.582 0.343 0.343 0.343
## 580 InstitutionalEnvironment =~ TEC3 12.221 -0.368 -0.220 -0.220 -0.220
## 399 EconomicSpecialization =~ INT3 11.973 -0.247 -0.182 -0.182 -0.182
## 453 RelationalNetworks =~ D2 11.901 -0.237 -0.182 -0.182 -0.182
## 412 EconomicSpecialization =~ INST6 11.550 0.222 0.164 0.164 0.164
## 518 InternationalRelations =~ POL7 11.402 -0.273 -0.193 -0.193 -0.193
## 496 InternationalRelations =~ DIV2 11.082 -0.158 -0.112 -0.112 -0.112
## 389 EconomicSpecialization =~ DIV3 10.972 -0.406 -0.300 -0.300 -0.300
Reliability
round(reliability(ModeloFit),3) # Reliability
## Performance EconomicSpecialization EconomicDiversification
## alpha 0.844 0.855 0.681
## omega 0.829 0.790 0.602
## omega2 0.829 0.790 0.602
## omega3 0.839 0.807 0.606
## avevar 0.485 0.619 0.363
## RelationalNetworks InternationalRelations TechnologicalHeterogeneity
## alpha 0.804 0.927 0.785
## omega 0.768 0.918 0.742
## omega2 0.768 0.918 0.742
## omega3 0.759 0.931 0.736
## avevar 0.454 0.705 0.478
## InstitutionalEnvironment Publicpolicies
## alpha 0.787 0.795
## omega 0.736 0.761
## omega2 0.736 0.761
## omega3 0.737 0.773
## avevar 0.386 0.511
Sem Path
semPaths(ModeloFit,
what = "std",
style = "lisrel",
residScale = 8,
theme = "colorblind",
nCharNodes = 3,
reorder = FALSE,
rotation = 2,
cardinal = "lat cov",
curvePivot = TRUE,
sizeMan = 4,
sizeLat = 10,
intercepts = T,
exoCov = F,
residuals = F,
sizeInt = 0.00001,
mar =c(1, 1, 1, 1),
edge.label.cex = 1.25) # Color blind friendly
Hierarchical Cluster
Packages and data
library(factoextra)
library(FactoMineR)
library(dendextend)
dados2 <- as.data.frame(scale(dados2))
dados3 <- as.data.frame(dados2$Performance)
Optimal Number of Clusters
fviz_nbclust(dados3, hcut, method = c("wss")) + geom_vline(xintercept = 3, linetype = 2)+
labs(subtitle = "Elbow method")
Hierarchical
grupo <- hclust(dist(dados3)^2, method = "ward.D")
grupo$height
## [1] 3.127822e-10 2.227517e-08 5.164966e-08 7.016311e-08 7.971826e-08
## [6] 1.258375e-07 3.586025e-07 3.695404e-07 6.669422e-07 8.683602e-07
## [11] 8.703940e-07 1.074269e-06 1.096574e-06 1.449315e-06 1.656943e-06
## [16] 1.857978e-06 2.559847e-06 3.115978e-06 3.915546e-06 4.105866e-06
## [21] 5.634775e-06 5.924604e-06 6.378956e-06 6.410650e-06 7.136282e-06
## [26] 7.290991e-06 7.777024e-06 8.034634e-06 8.183839e-06 9.382538e-06
## [31] 9.724593e-06 1.019551e-05 1.192950e-05 1.214759e-05 1.260623e-05
## [36] 1.295864e-05 1.330452e-05 1.563257e-05 1.653986e-05 1.715111e-05
## [41] 2.350652e-05 2.368983e-05 2.636847e-05 2.683802e-05 2.793327e-05
## [46] 2.830700e-05 2.831520e-05 3.228098e-05 3.346064e-05 3.747643e-05
## [51] 4.074928e-05 4.098824e-05 4.270026e-05 4.825938e-05 5.086358e-05
## [56] 5.333882e-05 5.517821e-05 5.602854e-05 6.777848e-05 8.416598e-05
## [61] 8.821066e-05 9.572124e-05 1.103014e-04 1.116876e-04 1.331575e-04
## [66] 1.445833e-04 1.565401e-04 1.610125e-04 1.711716e-04 2.012101e-04
## [71] 2.121658e-04 2.200005e-04 2.297451e-04 2.352952e-04 2.634297e-04
## [76] 2.693394e-04 2.702981e-04 2.751778e-04 2.933721e-04 3.361452e-04
## [81] 3.374903e-04 3.382463e-04 3.424925e-04 3.555438e-04 4.182287e-04
## [86] 4.350782e-04 4.510974e-04 4.747876e-04 4.849112e-04 5.154310e-04
## [91] 5.298838e-04 5.299006e-04 5.394746e-04 5.453972e-04 5.763007e-04
## [96] 6.915917e-04 7.019899e-04 7.034156e-04 7.399326e-04 7.612072e-04
## [101] 7.666660e-04 8.330550e-04 8.989984e-04 9.333645e-04 9.354080e-04
## [106] 9.733413e-04 9.744037e-04 1.001845e-03 1.168046e-03 1.231846e-03
## [111] 1.583351e-03 1.584778e-03 1.605938e-03 1.742711e-03 1.819081e-03
## [116] 2.012213e-03 2.223493e-03 2.300156e-03 2.366864e-03 2.462772e-03
## [121] 2.578641e-03 2.968785e-03 2.999206e-03 3.023064e-03 3.115124e-03
## [126] 3.213885e-03 3.406789e-03 3.873030e-03 3.948942e-03 4.050188e-03
## [131] 4.326049e-03 4.426708e-03 4.509107e-03 4.516007e-03 5.293936e-03
## [136] 5.346775e-03 5.672438e-03 6.809058e-03 7.780628e-03 7.850265e-03
## [141] 8.182162e-03 8.681556e-03 8.943277e-03 9.105645e-03 9.210013e-03
## [146] 9.782583e-03 1.032521e-02 1.046060e-02 1.437375e-02 1.528969e-02
## [151] 2.103274e-02 2.128399e-02 2.412682e-02 2.506311e-02 2.635123e-02
## [156] 3.109745e-02 3.378595e-02 4.126473e-02 4.904238e-02 5.466119e-02
## [161] 5.534453e-02 6.313075e-02 7.077463e-02 7.158704e-02 7.350380e-02
## [166] 1.102016e-01 1.450924e-01 1.553461e-01 1.773950e-01 1.806835e-01
## [171] 1.961422e-01 2.059137e-01 2.206881e-01 2.778861e-01 3.670341e-01
## [176] 3.713209e-01 5.116507e-01 5.534698e-01 6.016814e-01 1.082568e+00
## [181] 1.499895e+00 1.901261e+00 2.052569e+00 3.824857e+00 7.048173e+00
## [186] 1.163409e+01 1.165363e+01 3.317653e+01 1.072082e+02 1.939427e+02
plot(grupo, labels = FALSE)
grps <- cutree(grupo, k=3)
grps
## [1] 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 3 3 1 1 2 2 1 1 1 1 2 1 1 1 3 3 1 3 1 3 2 1
## [38] 3 1 3 1 2 1 2 1 3 1 1 1 3 1 1 3 3 1 3 1 1 3 1 1 2 1 1 3 1 1 3 3 1 3 3 3 1
## [75] 1 1 1 1 1 3 3 1 2 1 1 2 1 1 1 1 3 1 1 1 3 1 1 3 3 3 3 1 3 3 3 3 3 3 1 3 1
## [112] 3 3 2 1 1 1 1 1 2 1 2 1 1 3 1 1 1 2 1 1 1 3 1 1 2 1 1 2 2 1 1 2 1 3 1 2 1
## [149] 1 1 1 1 2 1 1 2 2 2 1 2 1 2 2 2 1 1 2 1 1 1 1 1 2 1 1 3 2 1 1 1 2 1 1 1 1
## [186] 2 1 1 1 1 1
rect.hclust(grupo, k = 3, border = 2:5)
Rename
dados2 <- cbind(dados2, grps)
names(dados2)[names(dados2) == "grps"] <- "Cluster"
Cluster Descriptive
dados2 %>%
count(Cluster)
## Cluster n
## 1 1 115
## 2 2 35
## 3 3 41
dados2 %>%
group_by(Cluster) %>%
summarise(mean = mean(Performance))
## # A tibble: 3 x 2
## Cluster mean
## <int> <dbl>
## 1 1 0.0427
## 2 2 1.46
## 3 3 -1.36
Dendogram
dados2$Cluster <- factor(dados2$Cluster, label = c("Médio", "Alto", "Baixo"), levels = 1:3)
par(mar=c(2,6,2,2))
geno <- as.dendrogram(hclust(dist(dados3)^2, method = "ward.D"), leaflab = "none")
cols_branches <- c(2:4)
d5gr <- color_branches(geno, k=3, groupLabels = F)
fviz_dend(d5gr, k = 3, color_labels_by_k = TRUE,
show_labels = F, rect = T, ggtheme = theme_bw(),
main = "", ylab = "Height") # Final Dendogram
Anova
Normality test with Shapiro Wilk
shapiro.test(dados2$Performance) # Normal at alpha of 5%
##
## Shapiro-Wilk normality test
##
## data: dados2$Performance
## W = 0.99737, p-value = 0.9873
shapiro.test(dados2$EconomicSpecialization) # Normal at alpha of 5%
##
## Shapiro-Wilk normality test
##
## data: dados2$EconomicSpecialization
## W = 0.98575, p-value = 0.05054
shapiro.test(dados2$EconomicDiversification) # Not normal at alpha of 5%
##
## Shapiro-Wilk normality test
##
## data: dados2$EconomicDiversification
## W = 0.98323, p-value = 0.02207
shapiro.test(dados2$RelationalNetworks) # Not normal at alpha of 5%
##
## Shapiro-Wilk normality test
##
## data: dados2$RelationalNetworks
## W = 0.97895, p-value = 0.005629
shapiro.test(dados2$InternationalRelations) # Not normal at alpha of 5%
##
## Shapiro-Wilk normality test
##
## data: dados2$InternationalRelations
## W = 0.9736, p-value = 0.001125
shapiro.test(dados2$TechnologicalHeterogeneity) # Normal at alpha of 5%
##
## Shapiro-Wilk normality test
##
## data: dados2$TechnologicalHeterogeneity
## W = 0.98995, p-value = 0.2008
shapiro.test(dados2$InstitutionalEnvironment) # Normal at alpha of 5%
##
## Shapiro-Wilk normality test
##
## data: dados2$InstitutionalEnvironment
## W = 0.99243, p-value = 0.426
shapiro.test(dados2$Publicpolicies) # Normal at alpha of 5%
##
## Shapiro-Wilk normality test
##
## data: dados2$Publicpolicies
## W = 0.99002, p-value = 0.2056
Radar Chart
library(foreign)
dados2 <- read.spss("C:/Users/user/Desktop/Vida acadêmica/Submissões/Artigo dissertação/Quanti/dados2.sav")
attach(dados2)
library(tibble) # Resolve o problema anterior #
dados2 <- as_tibble(dados2)
library(tidyverse)
library(magrittr)
library(plotly)
dados2 %>%
group_by(Cluster) %>%
summarise(mean = mean(DiversificaçãoEconômica))
## # A tibble: 3 x 2
## Cluster mean
## <fct> <dbl>
## 1 Médio -0.0451
## 2 Alto 0.356
## 3 Baixo -0.177
g <- plot_ly(type = 'scatterpolar', fill = 'tonext', mode = "markers") %>%
add_trace(
r = c(-1.15, -0.701, -0.660, -0.275, -1.06, -0.889, -0.177),
theta = c('International Relations','Institutional Environment','Relational Networks', 'Economic Specialization', 'Technological Heterogeneity', 'Public policies', 'Economic Diversification'),
name = 'Low Performance'
) %>%
add_trace(
r = c(0.0775, 0.0510, 0.0344, -0.0577, 0.0451, 0.0566, -0.0451),
theta = c('International Relations','Institutional Environment','Relational Networks', 'Economic Specialization', 'Technological Heterogeneity', 'Public policies', 'Economic Diversification'),
name = 'Intermediate Performance'
) %>%
add_trace(
r = c(1.09, 0.654, 0.660, 0.512, 1.10, 0.856, 0.356),
theta = c('International Relations','Institutional Environment','Relational Networks', 'Economic Specialization', 'Technological Heterogeneity', 'Public policies', 'Economic Diversification'),
name = 'High Performance'
) %>% layout(polar = list(radialaxis = list(visible = T, range = c(-0,7,1,5))))
g
Kruskal-Wallis
kruskal.test(dados2$EspecializaçãoEconômica ~ dados2$Cluster) # Economic Specialization
##
## Kruskal-Wallis rank sum test
##
## data: dados2$EspecializaçãoEconômica by dados2$Cluster
## Kruskal-Wallis chi-squared = 11.554, df = 2, p-value = 0.003098
kruskal.test(dados2$DiversificaçãoEconômica ~ dados2$Cluster) # Economic Diversification
##
## Kruskal-Wallis rank sum test
##
## data: dados2$DiversificaçãoEconômica by dados2$Cluster
## Kruskal-Wallis chi-squared = 5.8251, df = 2, p-value = 0.05434
kruskal.test(dados2$RedesRelacionais ~ dados2$Cluster) # Relational Networks
##
## Kruskal-Wallis rank sum test
##
## data: dados2$RedesRelacionais by dados2$Cluster
## Kruskal-Wallis chi-squared = 33.582, df = 2, p-value = 5.102e-08
kruskal.test(dados2$RelaçõesInternacionais ~ dados2$Cluster) # International Relations
##
## Kruskal-Wallis rank sum test
##
## data: dados2$RelaçõesInternacionais by dados2$Cluster
## Kruskal-Wallis chi-squared = 95.223, df = 2, p-value < 2.2e-16
kruskal.test(dados2$HeterogeneidadeTecnológica ~ dados2$Cluster) # Technological Heterogeneity
##
## Kruskal-Wallis rank sum test
##
## data: dados2$HeterogeneidadeTecnológica by dados2$Cluster
## Kruskal-Wallis chi-squared = 87.335, df = 2, p-value < 2.2e-16
kruskal.test(dados2$AmbienteInstitucional ~ dados2$Cluster) # Institutional Environment
##
## Kruskal-Wallis rank sum test
##
## data: dados2$AmbienteInstitucional by dados2$Cluster
## Kruskal-Wallis chi-squared = 39.402, df = 2, p-value = 2.78e-09
kruskal.test(dados2$Políticaspúblicas ~ dados2$Cluster) # Public Policies
##
## Kruskal-Wallis rank sum test
##
## data: dados2$Políticaspúblicas by dados2$Cluster
## Kruskal-Wallis chi-squared = 56.699, df = 2, p-value = 4.876e-13
Effect Statistic Eta2[H]
library(rstatix)
kruskal_effsize(dados2, dados2$EspecializaçãoEconômica ~ dados2$Cluster, ci = F, conf.level = 0.95, ci.type = "perc", nboot = 1000) # Economic Specialization
## # A tibble: 1 x 5
## .y. n effsize method magnitude
## * <chr> <int> <dbl> <chr> <ord>
## 1 dados2$EspecializaçãoEconômica 191 0.0508 eta2[H] small
kruskal_effsize(dados2, dados2$DiversificaçãoEconômica ~ dados2$Cluster, ci = F, conf.level = 0.95, ci.type = "perc", nboot = 1000) # Economic Diversification
## # A tibble: 1 x 5
## .y. n effsize method magnitude
## * <chr> <int> <dbl> <chr> <ord>
## 1 dados2$DiversificaçãoEconômica 191 0.0203 eta2[H] small
kruskal_effsize(dados2, dados2$RedesRelacionais ~ dados2$Cluster, ci = F, conf.level = 0.95, ci.type = "perc", nboot = 1000) # Relational Networks
## # A tibble: 1 x 5
## .y. n effsize method magnitude
## * <chr> <int> <dbl> <chr> <ord>
## 1 dados2$RedesRelacionais 191 0.168 eta2[H] large
kruskal_effsize(dados2, dados2$RelaçõesInternacionais ~ dados2$Cluster, ci = F, conf.level = 0.95, ci.type = "perc", nboot = 1000) # International Relations
## # A tibble: 1 x 5
## .y. n effsize method magnitude
## * <chr> <int> <dbl> <chr> <ord>
## 1 dados2$RelaçõesInternacionais 191 0.496 eta2[H] large
kruskal_effsize(dados2, dados2$HeterogeneidadeTecnológica ~ dados2$Cluster, ci = F, conf.level = 0.95, ci.type = "perc", nboot = 1000) # Technological Heterogeneity
## # A tibble: 1 x 5
## .y. n effsize method magnitude
## * <chr> <int> <dbl> <chr> <ord>
## 1 dados2$HeterogeneidadeTecnológica 191 0.454 eta2[H] large
kruskal_effsize(dados2, dados2$AmbienteInstitucional ~ dados2$Cluster, ci = F, conf.level = 0.95, ci.type = "perc", nboot = 1000) # Institutional Environment
## # A tibble: 1 x 5
## .y. n effsize method magnitude
## * <chr> <int> <dbl> <chr> <ord>
## 1 dados2$AmbienteInstitucional 191 0.199 eta2[H] large
kruskal_effsize(dados2, dados2$Políticaspúblicas ~ dados2$Cluster, ci = F, conf.level = 0.95, ci.type = "perc", nboot = 1000) # Public Policies
## # A tibble: 1 x 5
## .y. n effsize method magnitude
## * <chr> <int> <dbl> <chr> <ord>
## 1 dados2$Políticaspúblicas 191 0.291 eta2[H] large
Dunn Test
library(FSA)
dunnTest(dados2$EspecializaçãoEconômica ~ dados2$Cluster, method = "bonferroni") # Economic Specialization
## Comparison Z P.unadj P.adj
## 1 Alto - Baixo 3.1686840 0.001531308 0.004593924
## 2 Alto - Médio 2.9798044 0.002884325 0.008652975
## 3 Baixo - Médio -0.8465321 0.397255960 1.000000000
dunnTest(dados2$DiversificaçãoEconômica ~ dados2$Cluster, method = "bonferroni") # Economic Diversification
## Comparison Z P.unadj P.adj
## 1 Alto - Baixo 2.228015 0.02587950 0.07763851
## 2 Alto - Médio 2.143448 0.03207716 0.09623149
## 3 Baixo - Médio -0.544029 0.58642153 1.00000000
dunnTest(dados2$RedesRelacionais ~ dados2$Cluster, method = "bonferroni") # Relational Networks
## Comparison Z P.unadj P.adj
## 1 Alto - Baixo 5.764794 8.175761e-09 2.452728e-08
## 2 Alto - Médio 3.254865 1.134463e-03 3.403388e-03
## 3 Baixo - Médio -3.839208 1.234317e-04 3.702951e-04
dunnTest(dados2$RelaçõesInternacionais ~ dados2$Cluster, method = "bonferroni") # International Relations
## Comparison Z P.unadj P.adj
## 1 Alto - Baixo 9.653947 4.729925e-22 1.418978e-21
## 2 Alto - Médio 5.119178 3.068702e-07 9.206106e-07
## 3 Baixo - Médio -6.781158 1.192165e-11 3.576495e-11
dunnTest(dados2$HeterogeneidadeTecnológica ~ dados2$Cluster, method = "bonferroni") # Technological Heterogeneity
## Comparison Z P.unadj P.adj
## 1 Alto - Baixo 9.311980 1.254706e-20 3.764119e-20
## 2 Alto - Médio 5.384577 7.261513e-08 2.178454e-07
## 3 Baixo - Médio -6.066831 1.304586e-09 3.913757e-09
dunnTest(dados2$AmbienteInstitucional ~ dados2$Cluster, method = "bonferroni") # Institutional Environment
## Comparison Z P.unadj P.adj
## 1 Alto - Baixo 6.204865 5.474397e-10 1.642319e-09
## 2 Alto - Médio 3.263479 1.100534e-03 3.301601e-03
## 3 Baixo - Médio -4.386844 1.150072e-05 3.450216e-05
dunnTest(dados2$Políticaspúblicas ~ dados2$Cluster, method = "bonferroni") # Public Policies
## Comparison Z P.unadj P.adj
## 1 Alto - Baixo 7.451951 9.197012e-14 2.759104e-13
## 2 Alto - Médio 3.965157 7.334762e-05 2.200428e-04
## 3 Baixo - Médio -5.219962 1.789603e-07 5.368808e-07