GUIA 1 PERIODO 2

##Carga

library(wooldridge)
data("hprice1")
head(force(hprice1),n=5)

##   price assess bdrms lotsize sqrft colonial   lprice  lassess llotsize   lsqrft
## 1   300  349.1     4    6126  2438        1 5.703783 5.855359 8.720297 7.798934
## 2   370  351.5     3    9903  2076        1 5.913503 5.862210 9.200593 7.638198
## 3   191  217.7     3    5200  1374        0 5.252274 5.383118 8.556414 7.225482
## 4   195  231.8     3    4600  1448        1 5.273000 5.445875 8.433811 7.277938
## 5   373  319.1     4    6095  2514        1 5.921578 5.765504 8.715224 7.829630

library(wooldridge)
data("hprice1")
head(force(hprice1),n=5)

##   price assess bdrms lotsize sqrft colonial   lprice  lassess llotsize   lsqrft
## 1   300  349.1     4    6126  2438        1 5.703783 5.855359 8.720297 7.798934
## 2   370  351.5     3    9903  2076        1 5.913503 5.862210 9.200593 7.638198
## 3   191  217.7     3    5200  1374        0 5.252274 5.383118 8.556414 7.225482
## 4   195  231.8     3    4600  1448        1 5.273000 5.445875 8.433811 7.277938
## 5   373  319.1     4    6095  2514        1 5.921578 5.765504 8.715224 7.829630

##ESTIMACION

library(stargazer)
modelo_hrpice <- lm(formula = price ~ lotsize + sqrft + bdrms, data = hprice1)
stargazer(modelo_hrpice, title = "precios", type = "text", digits = 5)

## 
## precios
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                price           
## -----------------------------------------------
## lotsize                     0.00207***         
##                              (0.00064)         
##                                                
## sqrft                       0.12278***         
##                              (0.01324)         
##                                                
## bdrms                        13.85252          
##                              (9.01015)         
##                                                
## Constant                     -21.77031         
##                             (29.47504)         
##                                                
## -----------------------------------------------
## Observations                    88             
## R2                            0.67236          
## Adjusted R2                   0.66066          
## Residual Std. Error     59.83348 (df = 84)     
## F Statistic          57.46023*** (df = 3; 84)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

##PRUEBA JB

library(moments)
residuosJB <- residuals(modelo_hrpice)
nJB <- length(residuosJB)
asimetriaJB <- skewness(residuosJB)
curtosisJB <- kurtosis(residuosJB)-3
JB <- nJB /6 * (asimetriaJB^2 + 1/4*curtosisJB^2)
print(JB)

## [1] 32.27791

#TABLA

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(gt)
library(gtExtras)
residuosJB %>%
  as_tibble() %>%
  mutate(posicion=row_number()) %>% 
  arrange(value) %>% 
  mutate(dist1=row_number()/n()) %>%
  mutate(dist2=(row_number()-1)/n()) %>%
  mutate(zi=as.vector(scale(value,center=TRUE ))) %>%
  mutate(pi=pnorm(zi, lower.tail=TRUE)) %>%
  mutate(dif1=abs(dist1-pi)) %>%
  mutate(dif2=abs(dist2-pi)) %>%
  rename(residuales=value) -> tabla_JB 
#FORMATO
tabla_JB %>%
  gt() %>%
  tab_header("TABLA PARA CALCULAR JB")

residuales	posicion	dist1	dist2	zi	pi	dif1	dif2
TABLA PARA CALCULAR JB
-120.026447	81	0.01136364	0.00000000	-2.041515459	0.02059981	0.0092361731	0.0205998094
-115.508697	77	0.02272727	0.01136364	-1.964673586	0.02472601	0.0019987418	0.0133623781
-107.080889	24	0.03409091	0.02272727	-1.821326006	0.03427866	0.0001877487	0.0115513850
-91.243980	48	0.04545455	0.03409091	-1.551957925	0.06033615	0.0148816002	0.0262452366
-85.461169	12	0.05681818	0.04545455	-1.453598781	0.07302879	0.0162106057	0.0275742421
-77.172687	32	0.06818182	0.05681818	-1.312620980	0.09465535	0.0264735301	0.0378371665
-74.702719	54	0.07954545	0.06818182	-1.270609602	0.10193378	0.0223883300	0.0337519664
-65.502849	39	0.09090909	0.07954545	-1.114130117	0.13261169	0.0417025941	0.0530662305
-63.699108	69	0.10227273	0.09090909	-1.083450505	0.13930425	0.0370315271	0.0483951634
-62.566594	83	0.11363636	0.10227273	-1.064187703	0.14362184	0.0299854747	0.0413491110
-59.845223	36	0.12500000	0.11363636	-1.017900230	0.15436269	0.0293626861	0.0407263225
-54.466158	13	0.13636364	0.12500000	-0.926408352	0.17711690	0.0407532663	0.0521169027
-54.300415	14	0.14772727	0.13636364	-0.923589260	0.17785010	0.0301228311	0.0414864675
-52.129801	15	0.15909091	0.14772727	-0.886669532	0.18762842	0.0285375141	0.0399011505
-51.441108	17	0.17045455	0.15909091	-0.874955638	0.19079902	0.0203444766	0.0317081129
-48.704980	47	0.18181818	0.17045455	-0.828417174	0.20371714	0.0218989601	0.0332625965
-48.350295	29	0.19318182	0.18181818	-0.822384375	0.20542908	0.0122472664	0.0236109028
-47.855859	11	0.20454545	0.19318182	-0.813974573	0.20782976	0.0032843043	0.0146479407
-45.639765	1	0.21590909	0.20454545	-0.776281294	0.21879146	0.0028823668	0.0142460032
-43.142550	9	0.22727273	0.21590909	-0.733806463	0.23153335	0.0042606233	0.0156242596
-41.749618	57	0.23863636	0.22727273	-0.710114247	0.23881665	0.0001802823	0.0115439187
-40.869022	27	0.25000000	0.23863636	-0.695136302	0.24348494	0.0065150566	0.0048485798
-37.749811	34	0.26136364	0.25000000	-0.642082009	0.26040997	0.0009536682	0.0104099682
-36.663785	71	0.27272727	0.26136364	-0.623609925	0.26644190	0.0062853771	0.0050782592
-36.646568	79	0.28409091	0.27272727	-0.623317083	0.26653809	0.0175528221	0.0061891857
-33.801248	37	0.29545455	0.28409091	-0.574921384	0.28267223	0.0127823120	0.0014186757
-29.766931	16	0.30681818	0.29545455	-0.506302171	0.30632227	0.0004959124	0.0108677240
-26.696234	22	0.31818182	0.30681818	-0.454073044	0.32488813	0.0067063089	0.0180699452
-24.271531	23	0.32954545	0.31818182	-0.412831567	0.33986501	0.0103195566	0.0216831929
-23.651448	86	0.34090909	0.32954545	-0.402284648	0.34373728	0.0028281851	0.0141918214
-19.683427	88	0.35227273	0.34090909	-0.334793052	0.36889060	0.0166178738	0.0279815102
-17.817835	10	0.36363636	0.35227273	-0.303061413	0.38092153	0.0172851663	0.0286488027
-16.762094	60	0.37500000	0.36363636	-0.285104441	0.38778206	0.0127820638	0.0241457002
-16.596960	21	0.38636364	0.37500000	-0.282295711	0.38885839	0.0024947507	0.0138583870
-16.271207	58	0.39772727	0.38636364	-0.276755010	0.39098411	0.0067431583	0.0046204781
-13.815798	56	0.40909091	0.39772727	-0.234991254	0.40710776	0.0019831485	0.0093804879
-13.462160	75	0.42045455	0.40909091	-0.228976273	0.40944368	0.0110108666	0.0003527698
-12.081520	4	0.43181818	0.42045455	-0.205493119	0.41859344	0.0132247451	0.0018611087
-11.629207	51	0.44318182	0.43181818	-0.197799788	0.42160086	0.0215809622	0.0102173258
-11.312669	74	0.45454545	0.44318182	-0.192415834	0.42370825	0.0308372092	0.0194735728
-8.236558	3	0.46590909	0.45454545	-0.140094626	0.44429261	0.0216164775	0.0102528411
-7.662789	70	0.47727273	0.46590909	-0.130335452	0.44815052	0.0291222111	0.0177585748
-6.752801	67	0.48863636	0.47727273	-0.114857588	0.45427900	0.0343573625	0.0229937262
-6.707262	31	0.50000000	0.48863636	-0.114083016	0.45458599	0.0454140074	0.0340503710
-6.402439	85	0.51136364	0.50000000	-0.108898313	0.45664157	0.0547220642	0.0433584278
-5.446904	82	0.52272727	0.51136364	-0.092645733	0.46309251	0.0596347676	0.0482711313
-3.537785	43	0.53409091	0.52272727	-0.060173762	0.47600862	0.0580822876	0.0467186512
-2.824941	61	0.54545455	0.53409091	-0.048049090	0.48083856	0.0646159857	0.0532523493
-2.745208	68	0.55681818	0.54545455	-0.046692922	0.48137899	0.0754391961	0.0640755598
-0.195089	65	0.56818182	0.55681818	-0.003318245	0.49867621	0.0695056040	0.0581419676
1.399296	55	0.57954545	0.56818182	0.023800450	0.50949411	0.0700513452	0.0586877088
5.363331	26	0.59090909	0.57954545	0.091224254	0.53634280	0.0545662924	0.0432026561
6.700640	53	0.60227273	0.59090909	0.113970383	0.54536936	0.0569033628	0.0455397265
7.386314	80	0.61363636	0.60227273	0.125632935	0.54998875	0.0636476093	0.0522839730
9.099900	41	0.62500000	0.61363636	0.154779103	0.56150227	0.0634977329	0.0521340965
12.433611	46	0.63636364	0.62500000	0.211481796	0.58374433	0.0526193043	0.0412556680
16.718018	62	0.64772727	0.63636364	0.284354766	0.61193074	0.0357965328	0.0244328965
18.093192	5	0.65909091	0.64772727	0.307744934	0.62086179	0.0382291219	0.0268654856
18.801816	38	0.67045455	0.65909091	0.319797835	0.62543921	0.0450153400	0.0336517036
19.168108	33	0.68181818	0.67045455	0.326028052	0.62779843	0.0540197476	0.0426561112
19.219211	72	0.69318182	0.68181818	0.326897255	0.62812720	0.0650546167	0.0536909803
20.334434	59	0.70454545	0.69318182	0.345865960	0.63527827	0.0692671805	0.0579035442
24.909926	78	0.71590909	0.70454545	0.423689939	0.66410402	0.0518050676	0.0404414312
26.236229	40	0.72727273	0.71590909	0.446248874	0.67229126	0.0549814685	0.0436178321
30.924022	25	0.73863636	0.72727273	0.525982978	0.70054998	0.0380863808	0.0267227444
32.253952	45	0.75000000	0.73863636	0.548603608	0.70836125	0.0416387548	0.0302751184
32.529367	49	0.76136364	0.75000000	0.553288104	0.70996693	0.0513967091	0.0400330727
32.675968	18	0.77272727	0.76136364	0.555781630	0.71081993	0.0619073452	0.0505437088
33.275839	20	0.78409091	0.77272727	0.565984762	0.71429793	0.0697929786	0.0584293423
36.031430	52	0.79545455	0.78409091	0.612854281	0.73001365	0.0654408934	0.0540772571
37.147186	84	0.80681818	0.79545455	0.631832029	0.73625168	0.0705665028	0.0592028664
40.320875	7	0.81818182	0.80681818	0.685812928	0.75358446	0.0645973596	0.0532337232
44.334467	30	0.82954545	0.81818182	0.754079634	0.77459930	0.0549461574	0.0435825211
46.907165	28	0.84090909	0.82954545	0.797838357	0.78751785	0.0533912405	0.0420276041
54.418366	87	0.85227273	0.84090909	0.925595465	0.82267187	0.0296008528	0.0182372164
55.091131	35	0.86363636	0.85227273	0.937038450	0.82563061	0.0380057535	0.0266421172
55.470305	44	0.87500000	0.86363636	0.943487765	0.82728426	0.0477157353	0.0363520989
62.939597	6	0.88636364	0.87500000	1.070532059	0.85781006	0.0285535797	0.0171899433
66.478628	50	0.89772727	0.88636364	1.130727018	0.87091500	0.0268122757	0.0154486394
67.426518	63	0.90909091	0.89772727	1.146849569	0.87427810	0.0348128083	0.0234491719
67.603959	19	0.92045455	0.90909091	1.149867648	0.87490081	0.0455537393	0.0341901029
69.707122	64	0.93181818	0.92045455	1.185640095	0.88211777	0.0497004123	0.0383367759
69.843246	8	0.94318182	0.93181818	1.187955411	0.88257451	0.0606073068	0.0492436705
74.848732	2	0.95454545	0.94318182	1.273093116	0.89850750	0.0560379553	0.0446743189
112.729191	66	0.96590909	0.95454545	1.917397313	0.97240626	0.0064971714	0.0178608078
163.795081	73	0.97727273	0.96590909	2.785970904	0.99733162	0.0200588896	0.0314225260
198.660139	42	0.98863636	0.97727273	3.378986513	0.99963623	0.0109998685	0.0223635048
209.375830	76	1.00000000	0.98863636	3.561248407	0.99981545	0.0001845478	0.0111790885

#GRAFICO

library(fastGraph)
residuosJB_graph <- scale(modelo_hrpice$residuals)
hist(residuosJB_graph, fred= FALSE, main= "distribucion de los residuos estandarizados" ,xlab= "residuos estandarizados")

curve(dnorm (x, mean = mean(residuosJB_graph), sd=sd(residuosJB_graph)), add = TRUE, col="blue",lwd=2)

#prueba ks

residuosks <- residuals(modelo_hrpice)
n_ks <-length(residuosks)
residuosoredenadosks <- sort(residuosks)
d_plus <- max((1:n_ks )/n_ks - pnorm(residuosoredenadosks))
d_minus <- max(pnorm(residuosoredenadosks)-(0:(n_ks-1))/n_ks)
D_KS <- max(d_plus, d_minus)
print(D_KS)

## [1] 0.5537946

##TABLA

library(dplyr)
library(gt)
library(gtExtras)
residuosks %>%
  as_tibble() %>%
  mutate(posicion=row_number()) %>%
  arrange(value) %>%
  mutate(dist1=row_number()/n()) %>%
  mutate(dist2=(row_number()-1/n())) %>%
           mutate(zi=as.vector(scale(value,center=TRUE))) %>%
           mutate(pi=pnorm(zi,lower.tail=TRUE)) %>%
           mutate(dif1=abs(dist1-pi)) %>%
  mutate(dif2=abs(dist2-pi)) %>%
  rename(residuales=value) -> tabla_ks

tabla_ks %>%
  gt() %>%
  tab_header("tabla para calsular KS")

residuales	posicion	dist1	dist2	zi	pi	dif1	dif2
tabla para calsular KS
-120.026447	81	0.01136364	0.9886364	-2.041515459	0.02059981	0.0092361731	0.9680366
-115.508697	77	0.02272727	1.9886364	-1.964673586	0.02472601	0.0019987418	1.9639103
-107.080889	24	0.03409091	2.9886364	-1.821326006	0.03427866	0.0001877487	2.9543577
-91.243980	48	0.04545455	3.9886364	-1.551957925	0.06033615	0.0148816002	3.9283002
-85.461169	12	0.05681818	4.9886364	-1.453598781	0.07302879	0.0162106057	4.9156076
-77.172687	32	0.06818182	5.9886364	-1.312620980	0.09465535	0.0264735301	5.8939810
-74.702719	54	0.07954545	6.9886364	-1.270609602	0.10193378	0.0223883300	6.8867026
-65.502849	39	0.09090909	7.9886364	-1.114130117	0.13261169	0.0417025941	7.8560247
-63.699108	69	0.10227273	8.9886364	-1.083450505	0.13930425	0.0370315271	8.8493321
-62.566594	83	0.11363636	9.9886364	-1.064187703	0.14362184	0.0299854747	9.8450145
-59.845223	36	0.12500000	10.9886364	-1.017900230	0.15436269	0.0293626861	10.8342737
-54.466158	13	0.13636364	11.9886364	-0.926408352	0.17711690	0.0407532663	11.8115195
-54.300415	14	0.14772727	12.9886364	-0.923589260	0.17785010	0.0301228311	12.8107863
-52.129801	15	0.15909091	13.9886364	-0.886669532	0.18762842	0.0285375141	13.8010079
-51.441108	17	0.17045455	14.9886364	-0.874955638	0.19079902	0.0203444766	14.7978373
-48.704980	47	0.18181818	15.9886364	-0.828417174	0.20371714	0.0218989601	15.7849192
-48.350295	29	0.19318182	16.9886364	-0.822384375	0.20542908	0.0122472664	16.7832073
-47.855859	11	0.20454545	17.9886364	-0.813974573	0.20782976	0.0032843043	17.7808066
-45.639765	1	0.21590909	18.9886364	-0.776281294	0.21879146	0.0028823668	18.7698449
-43.142550	9	0.22727273	19.9886364	-0.733806463	0.23153335	0.0042606233	19.7571030
-41.749618	57	0.23863636	20.9886364	-0.710114247	0.23881665	0.0001802823	20.7498197
-40.869022	27	0.25000000	21.9886364	-0.695136302	0.24348494	0.0065150566	21.7451514
-37.749811	34	0.26136364	22.9886364	-0.642082009	0.26040997	0.0009536682	22.7282264
-36.663785	71	0.27272727	23.9886364	-0.623609925	0.26644190	0.0062853771	23.7221945
-36.646568	79	0.28409091	24.9886364	-0.623317083	0.26653809	0.0175528221	24.7220983
-33.801248	37	0.29545455	25.9886364	-0.574921384	0.28267223	0.0127823120	25.7059641
-29.766931	16	0.30681818	26.9886364	-0.506302171	0.30632227	0.0004959124	26.6823141
-26.696234	22	0.31818182	27.9886364	-0.454073044	0.32488813	0.0067063089	27.6637482
-24.271531	23	0.32954545	28.9886364	-0.412831567	0.33986501	0.0103195566	28.6487714
-23.651448	86	0.34090909	29.9886364	-0.402284648	0.34373728	0.0028281851	29.6448991
-19.683427	88	0.35227273	30.9886364	-0.334793052	0.36889060	0.0166178738	30.6197458
-17.817835	10	0.36363636	31.9886364	-0.303061413	0.38092153	0.0172851663	31.6077148
-16.762094	60	0.37500000	32.9886364	-0.285104441	0.38778206	0.0127820638	32.6008543
-16.596960	21	0.38636364	33.9886364	-0.282295711	0.38885839	0.0024947507	33.5997780
-16.271207	58	0.39772727	34.9886364	-0.276755010	0.39098411	0.0067431583	34.5976522
-13.815798	56	0.40909091	35.9886364	-0.234991254	0.40710776	0.0019831485	35.5815286
-13.462160	75	0.42045455	36.9886364	-0.228976273	0.40944368	0.0110108666	36.5791927
-12.081520	4	0.43181818	37.9886364	-0.205493119	0.41859344	0.0132247451	37.5700429
-11.629207	51	0.44318182	38.9886364	-0.197799788	0.42160086	0.0215809622	38.5670355
-11.312669	74	0.45454545	39.9886364	-0.192415834	0.42370825	0.0308372092	39.5649281
-8.236558	3	0.46590909	40.9886364	-0.140094626	0.44429261	0.0216164775	40.5443438
-7.662789	70	0.47727273	41.9886364	-0.130335452	0.44815052	0.0291222111	41.5404858
-6.752801	67	0.48863636	42.9886364	-0.114857588	0.45427900	0.0343573625	42.5343574
-6.707262	31	0.50000000	43.9886364	-0.114083016	0.45458599	0.0454140074	43.5340504
-6.402439	85	0.51136364	44.9886364	-0.108898313	0.45664157	0.0547220642	44.5319948
-5.446904	82	0.52272727	45.9886364	-0.092645733	0.46309251	0.0596347676	45.5255439
-3.537785	43	0.53409091	46.9886364	-0.060173762	0.47600862	0.0580822876	46.5126277
-2.824941	61	0.54545455	47.9886364	-0.048049090	0.48083856	0.0646159857	47.5077978
-2.745208	68	0.55681818	48.9886364	-0.046692922	0.48137899	0.0754391961	48.5072574
-0.195089	65	0.56818182	49.9886364	-0.003318245	0.49867621	0.0695056040	49.4899601
1.399296	55	0.57954545	50.9886364	0.023800450	0.50949411	0.0700513452	50.4791423
5.363331	26	0.59090909	51.9886364	0.091224254	0.53634280	0.0545662924	51.4522936
6.700640	53	0.60227273	52.9886364	0.113970383	0.54536936	0.0569033628	52.4432670
7.386314	80	0.61363636	53.9886364	0.125632935	0.54998875	0.0636476093	53.4386476
9.099900	41	0.62500000	54.9886364	0.154779103	0.56150227	0.0634977329	54.4271341
12.433611	46	0.63636364	55.9886364	0.211481796	0.58374433	0.0526193043	55.4048920
16.718018	62	0.64772727	56.9886364	0.284354766	0.61193074	0.0357965328	56.3767056
18.093192	5	0.65909091	57.9886364	0.307744934	0.62086179	0.0382291219	57.3677746
18.801816	38	0.67045455	58.9886364	0.319797835	0.62543921	0.0450153400	58.3631972
19.168108	33	0.68181818	59.9886364	0.326028052	0.62779843	0.0540197476	59.3608379
19.219211	72	0.69318182	60.9886364	0.326897255	0.62812720	0.0650546167	60.3605092
20.334434	59	0.70454545	61.9886364	0.345865960	0.63527827	0.0692671805	61.3533581
24.909926	78	0.71590909	62.9886364	0.423689939	0.66410402	0.0518050676	62.3245323
26.236229	40	0.72727273	63.9886364	0.446248874	0.67229126	0.0549814685	63.3163451
30.924022	25	0.73863636	64.9886364	0.525982978	0.70054998	0.0380863808	64.2880864
32.253952	45	0.75000000	65.9886364	0.548603608	0.70836125	0.0416387548	65.2802751
32.529367	49	0.76136364	66.9886364	0.553288104	0.70996693	0.0513967091	66.2786694
32.675968	18	0.77272727	67.9886364	0.555781630	0.71081993	0.0619073452	67.2778164
33.275839	20	0.78409091	68.9886364	0.565984762	0.71429793	0.0697929786	68.2743384
36.031430	52	0.79545455	69.9886364	0.612854281	0.73001365	0.0654408934	69.2586227
37.147186	84	0.80681818	70.9886364	0.631832029	0.73625168	0.0705665028	70.2523847
40.320875	7	0.81818182	71.9886364	0.685812928	0.75358446	0.0645973596	71.2350519
44.334467	30	0.82954545	72.9886364	0.754079634	0.77459930	0.0549461574	72.2140371
46.907165	28	0.84090909	73.9886364	0.797838357	0.78751785	0.0533912405	73.2011185
54.418366	87	0.85227273	74.9886364	0.925595465	0.82267187	0.0296008528	74.1659645
55.091131	35	0.86363636	75.9886364	0.937038450	0.82563061	0.0380057535	75.1630058
55.470305	44	0.87500000	76.9886364	0.943487765	0.82728426	0.0477157353	76.1613521
62.939597	6	0.88636364	77.9886364	1.070532059	0.85781006	0.0285535797	77.1308263
66.478628	50	0.89772727	78.9886364	1.130727018	0.87091500	0.0268122757	78.1177214
67.426518	63	0.90909091	79.9886364	1.146849569	0.87427810	0.0348128083	79.1143583
67.603959	19	0.92045455	80.9886364	1.149867648	0.87490081	0.0455537393	80.1137356
69.707122	64	0.93181818	81.9886364	1.185640095	0.88211777	0.0497004123	81.1065186
69.843246	8	0.94318182	82.9886364	1.187955411	0.88257451	0.0606073068	82.1060619
74.848732	2	0.95454545	83.9886364	1.273093116	0.89850750	0.0560379553	83.0901289
112.729191	66	0.96590909	84.9886364	1.917397313	0.97240626	0.0064971714	84.0162301
163.795081	73	0.97727273	85.9886364	2.785970904	0.99733162	0.0200588896	84.9913047
198.660139	42	0.98863636	86.9886364	3.378986513	0.99963623	0.0109998685	85.9890001
209.375830	76	1.00000000	87.9886364	3.561248407	0.99981545	0.0001845478	86.9888209

PRUEBA SM

residuos_sw <- residuals(modelo_hrpice)
n_sw <- length(residuos_sw)
SW <- shapiro.test(residuos_sw)$statistic
print(SW)

##         W 
## 0.9413208

library(dplyr)
library(gt)
library(gtExtras)
residuos_sw %>%
  as_tibble() %>%
  mutate(posicion=row_number()) %>%
  arrange(value)  %>%
  mutate(dist1=row_number()/n())  %>%
  mutate(dist2=(row_number()-1/n())) %>%
  mutate(zi=as.vector(scale(value,center=TRUE))) %>%
  mutate(pi=pnorm(zi,lower.tail = TRUE)) %>%
  mutate(dif1=abs(dist1-pi)) %>%
  mutate(dist2=abs(dist2-pi)) %>%
  rename(residuales=value) -> tabla_sw

tabla_sw  %>%
  gt()  %>%
  tab_header("tabla para calsular sw")

residuales	posicion	dist1	dist2	zi	pi	dif1
tabla para calsular sw
-120.026447	81	0.01136364	0.9680366	-2.041515459	0.02059981	0.0092361731
-115.508697	77	0.02272727	1.9639103	-1.964673586	0.02472601	0.0019987418
-107.080889	24	0.03409091	2.9543577	-1.821326006	0.03427866	0.0001877487
-91.243980	48	0.04545455	3.9283002	-1.551957925	0.06033615	0.0148816002
-85.461169	12	0.05681818	4.9156076	-1.453598781	0.07302879	0.0162106057
-77.172687	32	0.06818182	5.8939810	-1.312620980	0.09465535	0.0264735301
-74.702719	54	0.07954545	6.8867026	-1.270609602	0.10193378	0.0223883300
-65.502849	39	0.09090909	7.8560247	-1.114130117	0.13261169	0.0417025941
-63.699108	69	0.10227273	8.8493321	-1.083450505	0.13930425	0.0370315271
-62.566594	83	0.11363636	9.8450145	-1.064187703	0.14362184	0.0299854747
-59.845223	36	0.12500000	10.8342737	-1.017900230	0.15436269	0.0293626861
-54.466158	13	0.13636364	11.8115195	-0.926408352	0.17711690	0.0407532663
-54.300415	14	0.14772727	12.8107863	-0.923589260	0.17785010	0.0301228311
-52.129801	15	0.15909091	13.8010079	-0.886669532	0.18762842	0.0285375141
-51.441108	17	0.17045455	14.7978373	-0.874955638	0.19079902	0.0203444766
-48.704980	47	0.18181818	15.7849192	-0.828417174	0.20371714	0.0218989601
-48.350295	29	0.19318182	16.7832073	-0.822384375	0.20542908	0.0122472664
-47.855859	11	0.20454545	17.7808066	-0.813974573	0.20782976	0.0032843043
-45.639765	1	0.21590909	18.7698449	-0.776281294	0.21879146	0.0028823668
-43.142550	9	0.22727273	19.7571030	-0.733806463	0.23153335	0.0042606233
-41.749618	57	0.23863636	20.7498197	-0.710114247	0.23881665	0.0001802823
-40.869022	27	0.25000000	21.7451514	-0.695136302	0.24348494	0.0065150566
-37.749811	34	0.26136364	22.7282264	-0.642082009	0.26040997	0.0009536682
-36.663785	71	0.27272727	23.7221945	-0.623609925	0.26644190	0.0062853771
-36.646568	79	0.28409091	24.7220983	-0.623317083	0.26653809	0.0175528221
-33.801248	37	0.29545455	25.7059641	-0.574921384	0.28267223	0.0127823120
-29.766931	16	0.30681818	26.6823141	-0.506302171	0.30632227	0.0004959124
-26.696234	22	0.31818182	27.6637482	-0.454073044	0.32488813	0.0067063089
-24.271531	23	0.32954545	28.6487714	-0.412831567	0.33986501	0.0103195566
-23.651448	86	0.34090909	29.6448991	-0.402284648	0.34373728	0.0028281851
-19.683427	88	0.35227273	30.6197458	-0.334793052	0.36889060	0.0166178738
-17.817835	10	0.36363636	31.6077148	-0.303061413	0.38092153	0.0172851663
-16.762094	60	0.37500000	32.6008543	-0.285104441	0.38778206	0.0127820638
-16.596960	21	0.38636364	33.5997780	-0.282295711	0.38885839	0.0024947507
-16.271207	58	0.39772727	34.5976522	-0.276755010	0.39098411	0.0067431583
-13.815798	56	0.40909091	35.5815286	-0.234991254	0.40710776	0.0019831485
-13.462160	75	0.42045455	36.5791927	-0.228976273	0.40944368	0.0110108666
-12.081520	4	0.43181818	37.5700429	-0.205493119	0.41859344	0.0132247451
-11.629207	51	0.44318182	38.5670355	-0.197799788	0.42160086	0.0215809622
-11.312669	74	0.45454545	39.5649281	-0.192415834	0.42370825	0.0308372092
-8.236558	3	0.46590909	40.5443438	-0.140094626	0.44429261	0.0216164775
-7.662789	70	0.47727273	41.5404858	-0.130335452	0.44815052	0.0291222111
-6.752801	67	0.48863636	42.5343574	-0.114857588	0.45427900	0.0343573625
-6.707262	31	0.50000000	43.5340504	-0.114083016	0.45458599	0.0454140074
-6.402439	85	0.51136364	44.5319948	-0.108898313	0.45664157	0.0547220642
-5.446904	82	0.52272727	45.5255439	-0.092645733	0.46309251	0.0596347676
-3.537785	43	0.53409091	46.5126277	-0.060173762	0.47600862	0.0580822876
-2.824941	61	0.54545455	47.5077978	-0.048049090	0.48083856	0.0646159857
-2.745208	68	0.55681818	48.5072574	-0.046692922	0.48137899	0.0754391961
-0.195089	65	0.56818182	49.4899601	-0.003318245	0.49867621	0.0695056040
1.399296	55	0.57954545	50.4791423	0.023800450	0.50949411	0.0700513452
5.363331	26	0.59090909	51.4522936	0.091224254	0.53634280	0.0545662924
6.700640	53	0.60227273	52.4432670	0.113970383	0.54536936	0.0569033628
7.386314	80	0.61363636	53.4386476	0.125632935	0.54998875	0.0636476093
9.099900	41	0.62500000	54.4271341	0.154779103	0.56150227	0.0634977329
12.433611	46	0.63636364	55.4048920	0.211481796	0.58374433	0.0526193043
16.718018	62	0.64772727	56.3767056	0.284354766	0.61193074	0.0357965328
18.093192	5	0.65909091	57.3677746	0.307744934	0.62086179	0.0382291219
18.801816	38	0.67045455	58.3631972	0.319797835	0.62543921	0.0450153400
19.168108	33	0.68181818	59.3608379	0.326028052	0.62779843	0.0540197476
19.219211	72	0.69318182	60.3605092	0.326897255	0.62812720	0.0650546167
20.334434	59	0.70454545	61.3533581	0.345865960	0.63527827	0.0692671805
24.909926	78	0.71590909	62.3245323	0.423689939	0.66410402	0.0518050676
26.236229	40	0.72727273	63.3163451	0.446248874	0.67229126	0.0549814685
30.924022	25	0.73863636	64.2880864	0.525982978	0.70054998	0.0380863808
32.253952	45	0.75000000	65.2802751	0.548603608	0.70836125	0.0416387548
32.529367	49	0.76136364	66.2786694	0.553288104	0.70996693	0.0513967091
32.675968	18	0.77272727	67.2778164	0.555781630	0.71081993	0.0619073452
33.275839	20	0.78409091	68.2743384	0.565984762	0.71429793	0.0697929786
36.031430	52	0.79545455	69.2586227	0.612854281	0.73001365	0.0654408934
37.147186	84	0.80681818	70.2523847	0.631832029	0.73625168	0.0705665028
40.320875	7	0.81818182	71.2350519	0.685812928	0.75358446	0.0645973596
44.334467	30	0.82954545	72.2140371	0.754079634	0.77459930	0.0549461574
46.907165	28	0.84090909	73.2011185	0.797838357	0.78751785	0.0533912405
54.418366	87	0.85227273	74.1659645	0.925595465	0.82267187	0.0296008528
55.091131	35	0.86363636	75.1630058	0.937038450	0.82563061	0.0380057535
55.470305	44	0.87500000	76.1613521	0.943487765	0.82728426	0.0477157353
62.939597	6	0.88636364	77.1308263	1.070532059	0.85781006	0.0285535797
66.478628	50	0.89772727	78.1177214	1.130727018	0.87091500	0.0268122757
67.426518	63	0.90909091	79.1143583	1.146849569	0.87427810	0.0348128083
67.603959	19	0.92045455	80.1137356	1.149867648	0.87490081	0.0455537393
69.707122	64	0.93181818	81.1065186	1.185640095	0.88211777	0.0497004123
69.843246	8	0.94318182	82.1060619	1.187955411	0.88257451	0.0606073068
74.848732	2	0.95454545	83.0901289	1.273093116	0.89850750	0.0560379553
112.729191	66	0.96590909	84.0162301	1.917397313	0.97240626	0.0064971714
163.795081	73	0.97727273	84.9913047	2.785970904	0.99733162	0.0200588896
198.660139	42	0.98863636	85.9890001	3.378986513	0.99963623	0.0109998685
209.375830	76	1.00000000	86.9888209	3.561248407	0.99981545	0.0001845478

#GRAFICO

library(fastGraph)
residuossw_graph <- scale(modelo_hrpice$residuals)
hist(residuossw_graph, freq=FALSE, main="distribucion de los residuos estandarizados")
curve(dnorm(x, mean=mean(residuossw_graph),sd=sd(residuossw_graph)),add=TRUE, col="blue", lwd=2)

GUIA 1 PERIODO 2

CRISSIA

2024-05-06

PRUEBA SM