Ajuste do ambiente

library(dplyr)
library(survey)
library(reshape)
library(readr)
library(readxl)
library(gtools)
library(erer)
library(VGAM)
options( survey.lonely.psu = "adjust" )
setwd("W:\\Amostra\\Reponderação\\4_tri\\Workspace")


Reponderação dos resultados

# Trazendo dados trabalhados na Reponderação das Estratégias 2 e 3
coleta_4tri<-readRDS(file="W:/Amostra/Reponderação/4_tri/255/Bases/SIPS_4tri_1744.rds")
coleta_4tri$aux <- 1
#Trazendo dados populacionais
Pos_estratos <- read.csv("W:\\Amostra\\Reponderação\\4_tri\\Workspace\\vetor_calib.csv", sep=";", dec=",", header=TRUE)
x <- subset(coleta_4tri, pos_estrato == "ServiçosIntermediário")
#Concacentando os Estratos com o Tipo de produto
Pos_estratos$pos_estrato <- paste0(Pos_estratos$Estrato,Pos_estratos$Tipo_produto)
# Vetor segundo IPCA nos 3 estratos
Pos_est1 <- Pos_estratos %>%
  select(Pop_Est1,pos_estrato)
# Excluindo industria
Pos_est1 <- subset(Pos_est1,Pop_Est1>0)
coleta_4tri <- subset(coleta_4tri,Estrato != "Industria"& Estrato != "Intermediário")


Declarando o plano amostral

plano_SIPS <- svydesign(ids = ~ 1, strata = ~ amostra_dominio, weights = ~peso_dsn, 
                        data = coleta_4tri, nest = TRUE)
#Calibração
calibra2 = postStratify(plano_SIPS, ~pos_estrato, Pos_est1)
# Trazendo o peso para a base de dados
coleta_4tri$peso_est1 <- weights(calibra2)
# Calculando os fatores de ajuste (gweigths)
coleta_4tri$gweights2 <-coleta_4tri$peso_est1/coleta_4tri$peso_dsn
pesos_agregados<-coleta_4tri %>% 
  select(P1,pos_estrato,peso_est1)
pesos_agregados<-unique(pesos_agregados)
##################################################################################### 
#       Declarando o plano amostral sob a 2ª calibração                             
coleta_4tri$pos_estrato <- ifelse(coleta_4tri$pos_estrato == "ServiçosSPF", "Serviços SPF", coleta_4tri$pos_estrato)
pl_est1 <- svydesign(ids = ~ 1, strata = ~ amostra_dominio, weights = ~peso_est1, 
                     data = coleta_4tri, nest = TRUE)
#Total pós estrato
total <- as.data.frame(svyby(~aux,~pos_estrato,pl_est1, svytotal,pps = "TRUE",na.rm = "TRUE"))
#Total estrato
total2 <- as.data.frame(svyby(~aux,~Estrato,pl_est1, svytotal,pps = "TRUE",na.rm = "TRUE"))
rm(total, total2,x)


Probit considerando todas as observações

#Primeiro cruzamento
probit_cz1=svyolr(P18_probit~P4_v2, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1)
Call:
svyolr(P18_probit ~ P4_v2, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
        Value Std. Error t value
P4_v22 0.3160     0.1117   2.829
P4_v23 0.6368     0.1704   3.738

Intercepts:
    Value  Std. Error t value
1|2 -0.070  0.068     -1.033 
2|3  1.580  0.104     15.206 
(22 observations deleted due to missingness)
#AIC
probit_cz1$deviance+2*length(probit_cz1$coefficients)
[1] 1843
#Segundo cruzamento
probit_cz2=svyolr(P18_probit~P10_v2, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2)
Call:
svyolr(P18_probit ~ P10_v2, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
         Value Std. Error t value
P10_v22 0.1314     0.1126  1.1671
P10_v23 0.1972     0.2033  0.9701

Intercepts:
    Value  Std. Error t value
1|2 -0.157  0.071     -2.213 
2|3  1.427  0.096     14.825 
(25 observations deleted due to missingness)
#AIC
probit_cz2$deviance+2*length(probit_cz2$coefficients)
[1] 1888


Probit desagregado por produtos (Cruzamento 1)

probit_cz1_AFD=svyolr(P18_probit~AFD_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_AFD)
Call:
svyolr(P18_probit ~ AFD_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
         Value Std. Error t value
AFD_42 0.03073     0.1806  0.1701
AFD_43 0.37376     0.4277  0.8739

Intercepts:
    Value  Std. Error t value
1|2 -0.354  0.118     -2.987 
2|3  1.970  0.216      9.100 
(860 observations deleted due to missingness)
probit_cz1_AFD$deviance+2*length(probit_cz1_AFD$coefficients)
[1] 285.7
probit_cz1_AND=svyolr(P18_probit~AND_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_AND)
Call:
svyolr(P18_probit ~ AND_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
        Value Std. Error t value
AND_42 0.3433     0.1523   2.254
AND_43 0.9613     0.3662   2.625

Intercepts:
    Value  Std. Error t value
1|2  0.101  0.105      0.962 
2|3  1.589  0.151     10.544 
(788 observations deleted due to missingness)
probit_cz1_AND$deviance+2*length(probit_cz1_AND$coefficients)
[1] 469.1
probit_cz1_COM=svyolr(P18_probit~COM_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_COM)
Call:
svyolr(P18_probit ~ COM_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
         Value Std. Error t value
COM_42 -0.1538     0.4682 -0.3285

Intercepts:
    Value  Std. Error t value
1|2 -1.179  0.291     -4.047 
2|3  0.567  0.325      1.744 
(1021 observations deleted due to missingness)
probit_cz1_COM$deviance+2*length(probit_cz1_COM$coefficients)
[1] 48.22
probit_cz1_DESP=svyolr(P18_probit~DESP_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_DESP)
Call:
svyolr(P18_probit ~ DESP_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
         Value Std. Error t value
DESP_42 0.2997     0.3021  0.9922
DESP_43 0.3366     0.7571  0.4445

Intercepts:
    Value Std. Error t value
1|2 0.107 0.210      0.509  
2|3 2.030 0.225      9.011  
(937 observations deleted due to missingness)
probit_cz1_DESP$deviance+2*length(probit_cz1_DESP$coefficients)
[1] 178.7
probit_cz1_TP=svyolr(P18_probit~TP_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_TP)
Call:
svyolr(P18_probit ~ TP_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
       Value Std. Error t value
TP_42 0.2843     0.1812   1.569
TP_43 0.6037     0.3336   1.810

Intercepts:
    Value  Std. Error t value
1|2 -0.293  0.115     -2.548 
2|3  1.450  0.141     10.257 
(864 observations deleted due to missingness)
probit_cz1_TP$deviance+2*length(probit_cz1_TP$coefficients)
[1] 334
probit_cz1_VT=svyolr(P18_probit~VT_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_VT)
Call:
svyolr(P18_probit ~ VT_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
       Value Std. Error t value
VT_42 0.5776     0.2418   2.389
VT_43 0.7418     0.4952   1.498

Intercepts:
    Value  Std. Error t value
1|2 -0.054  0.164     -0.332 
2|3  2.088  0.266      7.851 
(936 observations deleted due to missingness)
probit_cz1_VT$deviance+2*length(probit_cz1_VT$coefficients)
[1] 176.6
probit_cz1_HB=svyolr(P18_probit~HB_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_HB)
Call:
svyolr(P18_probit ~ HB_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
        Value Std. Error t value
HB_42 0.06332     0.6887 0.09194
HB_43 5.73866     0.9477 6.05563

Intercepts:
    Value  Std. Error t value
1|2  0.703  0.474      1.483 
2|3  6.169  0.481     12.829 
(1011 observations deleted due to missingness)
probit_cz1_HB$deviance+2*length(probit_cz1_HB$coefficients)
[1] 43.24
probit_cz1_DP=svyolr(P18_probit~DP_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_DP)
Call:
svyolr(P18_probit ~ DP_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
        Value Std. Error  t value
DP_42 -0.7731     0.8170  -0.9462
DP_43 -5.5312     0.3585 -15.4281

Intercepts:
    Value   Std. Error t value
1|2  -0.071   0.308     -0.232
2|3   0.791   0.327      2.415
(1024 observations deleted due to missingness)
probit_cz1_DP$deviance+2*length(probit_cz1_DP$coefficients)
[1] 45.01
probit_cz1_CP=svyolr(P18_probit~CP_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_CP)
Call:
svyolr(P18_probit ~ CP_4, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
       Value Std. Error t value
CP_42 0.6539     0.3724  1.7558
CP_43 0.1771     0.6196  0.2857

Intercepts:
    Value  Std. Error t value
1|2 -0.098  0.285     -0.344 
2|3  1.764  0.393      4.485 
(999 observations deleted due to missingness)
probit_cz1_CP$deviance+2*length(probit_cz1_CP$coefficients)
[1] 84.5


Probit desagregado por produtos (Cruzamento 1)

probit_cz2_AFD=svyolr(P18_probit~AFD_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_AFD)
Call:
svyolr(P18_probit ~ AFD_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
         Value Std. Error t value
AFD_102 0.3791     0.1831   2.071
AFD_103 0.6509     0.5905   1.102

Intercepts:
    Value  Std. Error t value
1|2 -0.191  0.132     -1.442 
2|3  2.174  0.238      9.144 
(859 observations deleted due to missingness)
probit_cz2_AFD$deviance+2*length(probit_cz2_AFD$coefficients)
[1] 282.3
probit_cz2_AND=svyolr(P18_probit~AND_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_AND)
Call:
svyolr(P18_probit ~ AND_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
           Value Std. Error t value
AND_102  0.05472     0.1530  0.3578
AND_103 -1.00633     0.6095 -1.6512

Intercepts:
    Value  Std. Error t value
1|2 -0.064  0.106     -0.602 
2|3  1.377  0.137     10.072 
(789 observations deleted due to missingness)
probit_cz2_AND$deviance+2*length(probit_cz2_AND$coefficients)
[1] 477
probit_cz2_COM=svyolr(P18_probit~COM_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_COM)
Call:
svyolr(P18_probit ~ COM_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
             Value Std. Error    t value
COM_102  0.0823027     0.5460  0.1507330
COM_103 -0.0003125     0.6005 -0.0005204

Intercepts:
    Value  Std. Error t value
1|2 -1.100  0.537     -2.049 
2|3  0.686  0.491      1.397 
(1020 observations deleted due to missingness)
probit_cz2_COM$deviance+2*length(probit_cz2_COM$coefficients)
[1] 51.43
probit_cz2_DESP=svyolr(P18_probit~DESP_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_DESP)
Call:
svyolr(P18_probit ~ DESP_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
           Value Std. Error t value
DESP_102  0.7418     0.3042   2.438
DESP_103 -1.1156     0.6471  -1.724

Intercepts:
    Value  Std. Error t value
1|2  0.143  0.212      0.675 
2|3  2.204  0.261      8.429 
(938 observations deleted due to missingness)
probit_cz2_DESP$deviance+2*length(probit_cz2_DESP$coefficients)
[1] 165.8
probit_cz2_TP=svyolr(P18_probit~TP_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_TP)
Call:
svyolr(P18_probit ~ TP_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
        Value Std. Error t value
TP_102 0.3618     0.1758   2.058
TP_103 0.6233     0.4032   1.546

Intercepts:
    Value  Std. Error t value
1|2 -0.267  0.125     -2.138 
2|3  1.484  0.166      8.954 
(865 observations deleted due to missingness)
probit_cz2_TP$deviance+2*length(probit_cz2_TP$coefficients)
[1] 330.7
probit_cz2_VT=svyolr(P18_probit~VT_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_VT)
Call:
svyolr(P18_probit ~ VT_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
         Value Std. Error t value
VT_102 0.06228     0.2235  0.2787
VT_103 0.41865     0.5951  0.7035

Intercepts:
    Value  Std. Error t value
1|2 -0.254  0.167     -1.516 
2|3  1.769  0.198      8.933 
(939 observations deleted due to missingness)
probit_cz2_VT$deviance+2*length(probit_cz2_VT$coefficients)
[1] 179.7
probit_cz2_HB=svyolr(P18_probit~HB_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_HB)
Call:
svyolr(P18_probit ~ HB_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
         Value Std. Error t value
HB_102 -0.9879     0.6025  -1.640
HB_103 -4.9780     0.5340  -9.322

Intercepts:
    Value  Std. Error t value
1|2  0.193  0.498      0.388 
2|3  1.967  0.430      4.578 
(1011 observations deleted due to missingness)
probit_cz2_HB$deviance+2*length(probit_cz2_HB$coefficients)
[1] 44.56
probit_cz2_DP=svyolr(P18_probit~DP_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_DP)
Call:
svyolr(P18_probit ~ DP_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
        Value Std. Error t value
DP_102 0.1159     0.5369  0.2159
DP_103 0.6309     0.4220  1.4952

Intercepts:
    Value Std. Error t value
1|2 0.217 0.402      0.540  
2|3 1.044 0.492      2.124  
(1024 observations deleted due to missingness)
probit_cz2_DP$deviance+2*length(probit_cz2_DP$coefficients)
[1] 46.98
probit_cz2_CP=svyolr(P18_probit~CP_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_CP)
Call:
svyolr(P18_probit ~ CP_10, design = pl_est1, method = "probit", 
    na.action = na.omit)

Coefficients:
          Value Std. Error t value
CP_102 -0.07865     0.3576 -0.2199
CP_103  0.44452     0.3238  1.3729

Intercepts:
    Value  Std. Error t value
1|2 -0.453  0.313     -1.448 
2|3  1.342  0.389      3.453 
(999 observations deleted due to missingness)
probit_cz2_CP$deviance+2*length(probit_cz2_CP$coefficients)
[1] 87.84
---
title: "Modelo Probit sob a ótica da primeira estratégia"
author: "Rafael Cabral Fernandez"
output: html_notebook
---

<br>
<br>

##Ajuste do ambiente
```{r,warning=FALSE}
library(dplyr)
library(survey)
library(reshape)
library(readr)
library(readxl)
library(gtools)
library(erer)
library(VGAM)
library(kableExtra)
options( survey.lonely.psu = "adjust" )
setwd("W:\\Amostra\\Reponderação\\4_tri\\Workspace")
```
<br>

##Reponderação dos resultados
```{r}
# Trazendo dados trabalhados na Reponderação das Estratégias 2 e 3
coleta_4tri<-readRDS(file="W:/Amostra/Reponderação/4_tri/255/Bases/SIPS_4tri_1744.rds")
coleta_4tri$aux <- 1

#Trazendo dados populacionais
Pos_estratos <- read.csv("W:\\Amostra\\Reponderação\\4_tri\\Workspace\\vetor_calib.csv", sep=";", dec=",", header=TRUE)

x <- subset(coleta_4tri, pos_estrato == "ServiçosIntermediário")

#Concacentando os Estratos com o Tipo de produto
Pos_estratos$pos_estrato <- paste0(Pos_estratos$Estrato,Pos_estratos$Tipo_produto)

# Vetor segundo IPCA nos 3 estratos
Pos_est1 <- Pos_estratos %>%
  select(Pop_Est1,pos_estrato)

# Excluindo industria
Pos_est1 <- subset(Pos_est1,Pop_Est1>0)
coleta_4tri <- subset(coleta_4tri,Estrato != "Industria"& Estrato != "Intermediário")


```
<br>

##Declarando o plano amostral
```{r}
plano_SIPS <- svydesign(ids = ~ 1, strata = ~ amostra_dominio, weights = ~peso_dsn, 
                        data = coleta_4tri, nest = TRUE)

#Calibração
calibra2 = postStratify(plano_SIPS, ~pos_estrato, Pos_est1)

# Trazendo o peso para a base de dados
coleta_4tri$peso_est1 <- weights(calibra2)

# Calculando os fatores de ajuste (gweigths)
coleta_4tri$gweights2 <-coleta_4tri$peso_est1/coleta_4tri$peso_dsn

pesos_agregados<-coleta_4tri %>% 
  select(P1,pos_estrato,peso_est1)

pesos_agregados<-unique(pesos_agregados)


##################################################################################### 
#       Declarando o plano amostral sob a 2ª calibração                             

coleta_4tri$pos_estrato <- ifelse(coleta_4tri$pos_estrato == "ServiçosSPF", "Serviços SPF", coleta_4tri$pos_estrato)

pl_est1 <- svydesign(ids = ~ 1, strata = ~ amostra_dominio, weights = ~peso_est1, 
                     data = coleta_4tri, nest = TRUE)

#Total pós estrato
total <- as.data.frame(svyby(~aux,~pos_estrato,pl_est1, svytotal,pps = "TRUE",na.rm = "TRUE"))

#Total estrato
total2 <- as.data.frame(svyby(~aux,~Estrato,pl_est1, svytotal,pps = "TRUE",na.rm = "TRUE"))
rm(total, total2,x)
```
<br>


##Probit considerando todas as observações
```{r}
#Primeiro cruzamento
probit_cz1=svyolr(P18_probit~P4_v2, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1)
#AIC
probit_cz1$deviance+2*length(probit_cz1$coefficients)

#Segundo cruzamento
probit_cz2=svyolr(P18_probit~P10_v2, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2)
#AIC
probit_cz2$deviance+2*length(probit_cz2$coefficients)

```
<br>

##Probit desagregado por produtos (Cruzamento 1)
```{r}

probit_cz1_AFD=svyolr(P18_probit~AFD_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_AFD)
probit_cz1_AFD$deviance+2*length(probit_cz1_AFD$coefficients)

probit_cz1_AND=svyolr(P18_probit~AND_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_AND)
probit_cz1_AND$deviance+2*length(probit_cz1_AND$coefficients)

probit_cz1_COM=svyolr(P18_probit~COM_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_COM)
probit_cz1_COM$deviance+2*length(probit_cz1_COM$coefficients)

probit_cz1_DESP=svyolr(P18_probit~DESP_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_DESP)
probit_cz1_DESP$deviance+2*length(probit_cz1_DESP$coefficients)

probit_cz1_TP=svyolr(P18_probit~TP_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_TP)
probit_cz1_TP$deviance+2*length(probit_cz1_TP$coefficients)

probit_cz1_VT=svyolr(P18_probit~VT_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_VT)
probit_cz1_VT$deviance+2*length(probit_cz1_VT$coefficients)

probit_cz1_HB=svyolr(P18_probit~HB_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_HB)
probit_cz1_HB$deviance+2*length(probit_cz1_HB$coefficients)

probit_cz1_DP=svyolr(P18_probit~DP_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_DP)
probit_cz1_DP$deviance+2*length(probit_cz1_DP$coefficients)

probit_cz1_CP=svyolr(P18_probit~CP_4, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz1_CP)
probit_cz1_CP$deviance+2*length(probit_cz1_CP$coefficients)
```
<br>

##Probit desagregado por produtos (Cruzamento 1)
```{r}
probit_cz2_AFD=svyolr(P18_probit~AFD_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_AFD)
probit_cz2_AFD$deviance+2*length(probit_cz2_AFD$coefficients)

probit_cz2_AND=svyolr(P18_probit~AND_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_AND)
probit_cz2_AND$deviance+2*length(probit_cz2_AND$coefficients)

probit_cz2_COM=svyolr(P18_probit~COM_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_COM)
probit_cz2_COM$deviance+2*length(probit_cz2_COM$coefficients)

probit_cz2_DESP=svyolr(P18_probit~DESP_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_DESP)
probit_cz2_DESP$deviance+2*length(probit_cz2_DESP$coefficients)

probit_cz2_TP=svyolr(P18_probit~TP_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_TP)
probit_cz2_TP$deviance+2*length(probit_cz2_TP$coefficients)

probit_cz2_VT=svyolr(P18_probit~VT_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_VT)
probit_cz2_VT$deviance+2*length(probit_cz2_VT$coefficients)

probit_cz2_HB=svyolr(P18_probit~HB_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_HB)
probit_cz2_HB$deviance+2*length(probit_cz2_HB$coefficients)

probit_cz2_DP=svyolr(P18_probit~DP_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_DP)
probit_cz2_DP$deviance+2*length(probit_cz2_DP$coefficients)

probit_cz2_CP=svyolr(P18_probit~CP_10, design = pl_est1,method="probit",na.action = na.omit)
summary(probit_cz2_CP)
probit_cz2_CP$deviance+2*length(probit_cz2_CP$coefficients)
```



