set.seed(123)
dfj=data.frame(diam=rnorm(n = 45,mean = 1.2,sd = 0.18),
medio=gl(n = 3,k = 15,length = 45,labels = c('Medio1','Medio2','Medio3'))
)
View(dfj)
boxplot(dfj$diam~dfj$medio)
medias=tapply(dfj$diam, dfj$medio, mean)
medias
## Medio1 Medio2 Medio3
## 1.227429 1.155613 1.253152
var=tapply(dfj$diam, dfj$medio, var)
var
## Medio1 Medio2 Medio3
## 0.02315300 0.03867627 0.02435146
anova1=aov(dfj$diam~dfj$medio)
summary(anova1)
## Df Sum Sq Mean Sq F value Pr(>F)
## dfj$medio 2 0.0767 0.03833 1.334 0.274
## Residuals 42 1.2065 0.02873
#Aquí se saca el pvalor con la función pf
pf(0.58,df1 = 2,df2 = 42,lower.tail = FALSE)
## [1] 0.5643194
#Simulación del experimento
set.seed(123)
sim=replicate(n = 500,rnorm(n = 45,mean = 1.2,sd = 0.18))
dfc=data.frame(sim,medio=dfj$medio)
dfc[1:5,496:501]
## X496 X497 X498 X499 X500 medio
## 1 1.1283043 1.224979 1.0571428 1.040175 0.8924435 Medio1
## 2 0.9286414 1.235310 1.1686341 1.132010 1.3139903 Medio1
## 3 1.2079925 1.232764 0.8968581 1.266609 1.1661883 Medio1
## 4 0.9644863 1.155931 1.0334848 1.247690 1.1902469 Medio1
## 5 1.1910844 1.275822 1.0560513 1.368592 1.0737146 Medio1
#Ahora se corren 500 ANOVAS
f_sim = c()
for(i in seq(500)){
y = as.vector(sim[,i])
mod = aov(y ~ dfc$medio)
smod = summary(mod)
f_sim[i]=smod[[1]][1,4]
}
f_sim
## [1] 1.334372004 0.506398093 1.161530715 0.709458918 0.444178913 0.311678367
## [7] 2.289548581 1.333054406 1.565946642 1.157076637 0.398314734 1.825982944
## [13] 1.609714086 0.031606281 0.896485086 4.817263420 1.639658455 2.250555837
## [19] 2.503641466 1.060880000 2.356121117 0.329123770 3.467918610 1.204965657
## [25] 0.107585861 0.467237672 0.469691938 2.865261138 5.879012844 1.025510683
## [31] 0.293570372 0.920414027 1.762914220 0.749664511 2.811705666 2.871928495
## [37] 1.081859716 1.458697514 0.552401301 0.060176215 0.477337034 0.400612685
## [43] 0.224717192 0.120431335 0.276607939 0.103641009 1.818338424 2.123088955
## [49] 3.642377700 0.707458563 1.004418505 0.253010050 1.721602699 3.839323269
## [55] 0.362093762 0.223524203 0.619815326 0.489897885 0.586715136 1.122742759
## [61] 4.835142047 1.264698907 0.068343481 0.988372593 0.097154280 6.246262865
## [67] 1.204149462 0.188834101 0.357613280 0.095120403 0.163368262 2.425110902
## [73] 3.027457600 1.177437153 0.577870403 0.875926533 1.455077345 0.527421521
## [79] 0.558659128 0.742570680 0.450357110 0.464360308 2.255272422 0.493822797
## [85] 1.613385426 0.954767502 0.229956852 0.387515185 0.601718567 0.880529872
## [91] 1.125055333 2.349554644 3.087713930 0.771507215 0.252055822 1.424109427
## [97] 0.171855030 0.745508894 3.104893416 0.448202986 0.093347217 0.345909021
## [103] 0.927912944 0.234345870 1.240794617 0.714733228 0.061957806 0.643149713
## [109] 1.738063306 0.283124038 0.783757064 1.460225544 1.144015764 1.029902766
## [115] 1.462338105 1.653623598 0.006701271 0.289290373 1.351791172 0.060410172
## [121] 0.728167093 0.447718171 2.153178240 1.530051867 0.791971442 0.454085197
## [127] 0.920569451 0.431783193 1.112894808 0.945467308 0.291684594 0.804807993
## [133] 0.101061162 0.540363616 0.155289264 0.607928775 1.650296758 0.918449150
## [139] 0.506412320 0.651332343 1.228174778 0.455614240 0.167558711 4.780001253
## [145] 0.258740267 0.804839049 0.050631894 1.242750691 0.389359959 1.025966134
## [151] 1.684042740 1.381813874 3.079531049 1.790472855 0.250568538 0.844000935
## [157] 0.887883883 0.401115433 1.587033797 0.049727559 1.851520943 0.068521552
## [163] 1.737136374 0.871318157 4.166151673 0.085289194 1.877852046 0.586537235
## [169] 0.389425242 0.643407941 0.327077012 1.693219626 1.483166573 1.124409180
## [175] 0.557717170 1.375336563 1.662664571 0.241970282 1.829176035 1.059819654
## [181] 0.028864728 0.457697050 1.781533179 0.104067692 0.143434390 0.484911988
## [187] 1.484562281 1.199647389 2.121210583 0.392228378 0.371394400 0.262441395
## [193] 6.058665394 1.449716733 0.679610836 1.202003347 0.265246707 0.949555189
## [199] 0.050278942 0.193250138 0.616126970 0.304501228 0.617520517 0.621833876
## [205] 1.213448306 0.230107848 0.027516906 0.461695387 1.098146850 0.468415342
## [211] 0.223897494 3.648919365 1.117791104 1.710467376 0.663189481 0.690707146
## [217] 0.008301932 0.951393225 1.411351756 1.130947421 1.368251368 1.285836958
## [223] 0.337619091 0.167090372 0.211196367 2.189019526 1.741203565 0.344794147
## [229] 1.224205247 1.024218009 1.269630308 1.022054752 2.916396225 0.268504611
## [235] 0.067635003 1.925345669 4.874112024 0.266573530 0.540574739 0.830716950
## [241] 1.654080412 2.005211134 0.780449090 1.050416525 3.806373741 1.459792337
## [247] 0.819020672 0.604322821 0.551587186 0.777237768 0.647977205 0.924524287
## [253] 1.530278398 0.061114040 0.631005368 0.288988779 0.426467247 1.062824964
## [259] 0.164065349 0.679582523 0.333821914 0.351917203 1.051695055 2.533097709
## [265] 0.222055106 1.062918773 0.091603317 0.238620426 0.634681169 0.091638272
## [271] 4.033547879 1.212860454 4.702907574 0.852505157 2.721306856 0.325439124
## [277] 1.186982767 0.595937948 0.541390436 0.745856628 0.337532664 0.517081474
## [283] 0.446715714 0.917857721 0.772315632 1.465971563 1.585837275 2.182105179
## [289] 2.059227519 3.053856994 0.393238392 1.455708957 0.152614207 0.260424020
## [295] 0.031811266 0.730955538 0.663303793 0.525062069 0.378460219 0.508215403
## [301] 0.792441968 0.357990277 0.255091431 1.590377395 0.537658578 0.188604025
## [307] 0.425543336 0.091171841 0.494636631 0.425398720 1.081263475 0.101088866
## [313] 0.297718848 2.119979365 3.625975664 0.346896548 0.480709702 0.556905681
## [319] 2.427291503 1.508136197 0.642719428 0.930329475 1.855155956 2.202687822
## [325] 0.913730798 1.105024373 0.458131680 0.138619626 0.258434106 1.564044657
## [331] 0.469322242 1.788114452 1.180128920 0.225078056 0.690003469 0.495059450
## [337] 0.417010380 0.220502182 1.122276399 1.054402497 1.461990723 0.387405732
## [343] 0.010575562 2.126257762 0.002911895 2.460806351 0.239001707 0.371006842
## [349] 0.753429365 0.535951399 2.940399074 3.246366965 0.019708124 0.505955817
## [355] 2.323457003 0.585686956 0.786706980 1.726967559 0.427437314 1.277879661
## [361] 1.417845781 0.467147691 0.193303918 1.383520440 0.281735856 4.775588541
## [367] 2.189356692 0.253995195 1.512134581 0.427347101 0.094046586 0.479399003
## [373] 0.510026129 2.407363662 0.220808336 0.382844063 2.516390227 1.645133258
## [379] 0.540268021 0.357355015 0.008448825 0.034180715 0.582335968 0.878449390
## [385] 0.096387134 0.405616102 1.026536129 0.266072077 0.869284492 0.092175297
## [391] 0.839880291 0.192488497 0.376317134 1.447306049 0.403429377 0.209252423
## [397] 0.877327263 2.768376310 0.830210720 1.293610710 0.837051977 0.211340960
## [403] 0.227418808 0.119018980 0.892711754 1.104449718 0.682462002 1.983921126
## [409] 1.003539376 1.052600503 0.465085882 1.257439654 1.180906825 1.096648789
## [415] 1.338805533 0.310661301 0.209285487 0.709879464 1.942889778 1.483909930
## [421] 2.500604516 0.248536026 1.372250536 0.155707670 4.646809149 0.020310546
## [427] 0.432123286 0.198123303 2.759514634 0.320455316 4.214950355 0.445805670
## [433] 0.913324272 0.062411854 0.183487161 6.202569188 0.103651824 0.675196800
## [439] 2.042562161 0.023054753 1.315150782 0.195240849 0.016401868 0.681606473
## [445] 0.356986908 0.370091013 2.990333045 1.490106206 0.880429746 2.005775660
## [451] 1.012144349 0.071481189 0.747675772 0.003433504 0.234262384 3.733651531
## [457] 4.295273213 2.286135902 1.908520578 2.528496192 0.151280571 2.373690949
## [463] 0.613286489 4.617887437 0.849053931 3.581376175 0.815623021 0.292267031
## [469] 0.610344149 0.027295165 1.994475438 0.193925440 0.548112979 1.198749304
## [475] 0.669774221 0.138286295 0.525750030 0.199399493 1.401293243 0.149079253
## [481] 1.208026980 4.386688857 2.578351545 1.011898631 0.420415865 0.616602898
## [487] 0.129135500 0.678504549 0.905867707 1.154877627 0.784861593 1.090642439
## [493] 1.267573560 2.640014562 0.258420027 0.348205602 0.679841268 0.136363467
## [499] 0.781413880 0.397950775
hist(f_sim,breaks = 30)
abline(v = 1.334,col='blue',lwd=2)
FT=qf(0.05,2,42,lower.tail = FALSE)
abline(v = FT,col='red',lwd=2)
p5=quantile(f_sim,0.95)
p5
## 95%
## 3.257445
abline(v = p5,col='orange',lwd=2)
#Simulación del experimento 5000 veces
set.seed(123)
sim=replicate(n = 5000,rnorm(n = 45,mean = 1.2,sd = 0.18))
dfc=data.frame(sim,medio=dfj$medio)
dfc[1:5,496:501]
## X496 X497 X498 X499 X500 X501
## 1 1.1283043 1.224979 1.0571428 1.040175 0.8924435 1.249901
## 2 0.9286414 1.235310 1.1686341 1.132010 1.3139903 1.287430
## 3 1.2079925 1.232764 0.8968581 1.266609 1.1661883 1.252529
## 4 0.9644863 1.155931 1.0334848 1.247690 1.1902469 1.352110
## 5 1.1910844 1.275822 1.0560513 1.368592 1.0737146 1.474503
#Ahora se corren 500 ANOVAS
f_sim = c()
for(i in seq(500)){
y = as.vector(sim[,i])
mod = aov(y ~ dfc$medio)
smod = summary(mod)
f_sim[i]=smod[[1]][1,4]
}
f_sim
## [1] 1.334372004 0.506398093 1.161530715 0.709458918 0.444178913 0.311678367
## [7] 2.289548581 1.333054406 1.565946642 1.157076637 0.398314734 1.825982944
## [13] 1.609714086 0.031606281 0.896485086 4.817263420 1.639658455 2.250555837
## [19] 2.503641466 1.060880000 2.356121117 0.329123770 3.467918610 1.204965657
## [25] 0.107585861 0.467237672 0.469691938 2.865261138 5.879012844 1.025510683
## [31] 0.293570372 0.920414027 1.762914220 0.749664511 2.811705666 2.871928495
## [37] 1.081859716 1.458697514 0.552401301 0.060176215 0.477337034 0.400612685
## [43] 0.224717192 0.120431335 0.276607939 0.103641009 1.818338424 2.123088955
## [49] 3.642377700 0.707458563 1.004418505 0.253010050 1.721602699 3.839323269
## [55] 0.362093762 0.223524203 0.619815326 0.489897885 0.586715136 1.122742759
## [61] 4.835142047 1.264698907 0.068343481 0.988372593 0.097154280 6.246262865
## [67] 1.204149462 0.188834101 0.357613280 0.095120403 0.163368262 2.425110902
## [73] 3.027457600 1.177437153 0.577870403 0.875926533 1.455077345 0.527421521
## [79] 0.558659128 0.742570680 0.450357110 0.464360308 2.255272422 0.493822797
## [85] 1.613385426 0.954767502 0.229956852 0.387515185 0.601718567 0.880529872
## [91] 1.125055333 2.349554644 3.087713930 0.771507215 0.252055822 1.424109427
## [97] 0.171855030 0.745508894 3.104893416 0.448202986 0.093347217 0.345909021
## [103] 0.927912944 0.234345870 1.240794617 0.714733228 0.061957806 0.643149713
## [109] 1.738063306 0.283124038 0.783757064 1.460225544 1.144015764 1.029902766
## [115] 1.462338105 1.653623598 0.006701271 0.289290373 1.351791172 0.060410172
## [121] 0.728167093 0.447718171 2.153178240 1.530051867 0.791971442 0.454085197
## [127] 0.920569451 0.431783193 1.112894808 0.945467308 0.291684594 0.804807993
## [133] 0.101061162 0.540363616 0.155289264 0.607928775 1.650296758 0.918449150
## [139] 0.506412320 0.651332343 1.228174778 0.455614240 0.167558711 4.780001253
## [145] 0.258740267 0.804839049 0.050631894 1.242750691 0.389359959 1.025966134
## [151] 1.684042740 1.381813874 3.079531049 1.790472855 0.250568538 0.844000935
## [157] 0.887883883 0.401115433 1.587033797 0.049727559 1.851520943 0.068521552
## [163] 1.737136374 0.871318157 4.166151673 0.085289194 1.877852046 0.586537235
## [169] 0.389425242 0.643407941 0.327077012 1.693219626 1.483166573 1.124409180
## [175] 0.557717170 1.375336563 1.662664571 0.241970282 1.829176035 1.059819654
## [181] 0.028864728 0.457697050 1.781533179 0.104067692 0.143434390 0.484911988
## [187] 1.484562281 1.199647389 2.121210583 0.392228378 0.371394400 0.262441395
## [193] 6.058665394 1.449716733 0.679610836 1.202003347 0.265246707 0.949555189
## [199] 0.050278942 0.193250138 0.616126970 0.304501228 0.617520517 0.621833876
## [205] 1.213448306 0.230107848 0.027516906 0.461695387 1.098146850 0.468415342
## [211] 0.223897494 3.648919365 1.117791104 1.710467376 0.663189481 0.690707146
## [217] 0.008301932 0.951393225 1.411351756 1.130947421 1.368251368 1.285836958
## [223] 0.337619091 0.167090372 0.211196367 2.189019526 1.741203565 0.344794147
## [229] 1.224205247 1.024218009 1.269630308 1.022054752 2.916396225 0.268504611
## [235] 0.067635003 1.925345669 4.874112024 0.266573530 0.540574739 0.830716950
## [241] 1.654080412 2.005211134 0.780449090 1.050416525 3.806373741 1.459792337
## [247] 0.819020672 0.604322821 0.551587186 0.777237768 0.647977205 0.924524287
## [253] 1.530278398 0.061114040 0.631005368 0.288988779 0.426467247 1.062824964
## [259] 0.164065349 0.679582523 0.333821914 0.351917203 1.051695055 2.533097709
## [265] 0.222055106 1.062918773 0.091603317 0.238620426 0.634681169 0.091638272
## [271] 4.033547879 1.212860454 4.702907574 0.852505157 2.721306856 0.325439124
## [277] 1.186982767 0.595937948 0.541390436 0.745856628 0.337532664 0.517081474
## [283] 0.446715714 0.917857721 0.772315632 1.465971563 1.585837275 2.182105179
## [289] 2.059227519 3.053856994 0.393238392 1.455708957 0.152614207 0.260424020
## [295] 0.031811266 0.730955538 0.663303793 0.525062069 0.378460219 0.508215403
## [301] 0.792441968 0.357990277 0.255091431 1.590377395 0.537658578 0.188604025
## [307] 0.425543336 0.091171841 0.494636631 0.425398720 1.081263475 0.101088866
## [313] 0.297718848 2.119979365 3.625975664 0.346896548 0.480709702 0.556905681
## [319] 2.427291503 1.508136197 0.642719428 0.930329475 1.855155956 2.202687822
## [325] 0.913730798 1.105024373 0.458131680 0.138619626 0.258434106 1.564044657
## [331] 0.469322242 1.788114452 1.180128920 0.225078056 0.690003469 0.495059450
## [337] 0.417010380 0.220502182 1.122276399 1.054402497 1.461990723 0.387405732
## [343] 0.010575562 2.126257762 0.002911895 2.460806351 0.239001707 0.371006842
## [349] 0.753429365 0.535951399 2.940399074 3.246366965 0.019708124 0.505955817
## [355] 2.323457003 0.585686956 0.786706980 1.726967559 0.427437314 1.277879661
## [361] 1.417845781 0.467147691 0.193303918 1.383520440 0.281735856 4.775588541
## [367] 2.189356692 0.253995195 1.512134581 0.427347101 0.094046586 0.479399003
## [373] 0.510026129 2.407363662 0.220808336 0.382844063 2.516390227 1.645133258
## [379] 0.540268021 0.357355015 0.008448825 0.034180715 0.582335968 0.878449390
## [385] 0.096387134 0.405616102 1.026536129 0.266072077 0.869284492 0.092175297
## [391] 0.839880291 0.192488497 0.376317134 1.447306049 0.403429377 0.209252423
## [397] 0.877327263 2.768376310 0.830210720 1.293610710 0.837051977 0.211340960
## [403] 0.227418808 0.119018980 0.892711754 1.104449718 0.682462002 1.983921126
## [409] 1.003539376 1.052600503 0.465085882 1.257439654 1.180906825 1.096648789
## [415] 1.338805533 0.310661301 0.209285487 0.709879464 1.942889778 1.483909930
## [421] 2.500604516 0.248536026 1.372250536 0.155707670 4.646809149 0.020310546
## [427] 0.432123286 0.198123303 2.759514634 0.320455316 4.214950355 0.445805670
## [433] 0.913324272 0.062411854 0.183487161 6.202569188 0.103651824 0.675196800
## [439] 2.042562161 0.023054753 1.315150782 0.195240849 0.016401868 0.681606473
## [445] 0.356986908 0.370091013 2.990333045 1.490106206 0.880429746 2.005775660
## [451] 1.012144349 0.071481189 0.747675772 0.003433504 0.234262384 3.733651531
## [457] 4.295273213 2.286135902 1.908520578 2.528496192 0.151280571 2.373690949
## [463] 0.613286489 4.617887437 0.849053931 3.581376175 0.815623021 0.292267031
## [469] 0.610344149 0.027295165 1.994475438 0.193925440 0.548112979 1.198749304
## [475] 0.669774221 0.138286295 0.525750030 0.199399493 1.401293243 0.149079253
## [481] 1.208026980 4.386688857 2.578351545 1.011898631 0.420415865 0.616602898
## [487] 0.129135500 0.678504549 0.905867707 1.154877627 0.784861593 1.090642439
## [493] 1.267573560 2.640014562 0.258420027 0.348205602 0.679841268 0.136363467
## [499] 0.781413880 0.397950775
hist(f_sim,breaks = 30)
abline(v = 1.334,col='blue',lwd=2)
FT=qf(0.05,2,42,lower.tail = FALSE)
abline(v = FT,col='red',lwd=2)
p5=quantile(f_sim,0.95)
p5
## 95%
## 3.257445
abline(v = p5,col='orange',lwd=2)
#Hacemos nuevamente el boxplot para analizar los tratamientos
boxplot(dfj$diam~dfj$medio)
points(1:3,medias,col='red',cex=1.5,pch=19)
abline(h = mean(dfj$diam),col='purple',lwd=2)
efecto=medias-mean(dfj$diam)
round(efecto,3)
## Medio1 Medio2 Medio3
## 0.015 -0.056 0.041
text(1:3,medias,round(efecto,3),pos=3)
efecto
## Medio1 Medio2 Medio3
## 0.01536426 -0.05645145 0.04108719
#Enviar los datos a un excel
library(openxlsx)
write.xlsx(dfj,'dfj.xlsx')
## Warning in file.create(to[okay]): cannot create file 'dfj.xlsx', reason
## 'Permission denied'
getwd()
## [1] "G:/Mi unidad/MAESTRÍA CIENCIA Y TECNOLOGÍA ALIMENTOS/Métodos Multivariados/Clases/Clase 6 16.03.2023"
#Se sacan los efectos del anova
efectos=anova1$effects
efectos
## (Intercept) dfj$medioMedio2 dfj$medioMedio3
## -8.130778667 -0.267772758 0.070445154 -0.028536646 -0.017956364
##
## 0.267483542 0.041736761 -0.268939178 -0.164861669 -0.121447311
##
## 0.179106568 0.023538333 0.030910705 -0.021305267 -0.141279560
##
## 0.369789688 0.137758410 -0.305845765 0.174389386 -0.036957130
##
## -0.144062943 0.008909839 -0.136535477 -0.083055098 -0.064361745
##
## -0.255459472 0.198946992 0.075752485 -0.156719325 0.273832009
##
## 0.097206841 -0.032669586 0.181565900 0.178507309 0.168327876
##
## 0.144398527 0.120148459 0.009299173 -0.034629998 -0.048041499
##
## -0.104603975 -0.016981829 -0.207328062 0.410855355 0.237876441
#Aquí se incluye una nueva variable en el dataframe, los efectos obtenidos del anova
dfj$efectos=efectos
dfj
## diam medio efectos
## 1 1.0991144 Medio1 -8.130778667
## 2 1.1585681 Medio1 -0.267772758
## 3 1.4805675 Medio1 0.070445154
## 4 1.2126915 Medio1 -0.028536646
## 5 1.2232718 Medio1 -0.017956364
## 6 1.5087117 Medio1 0.267483542
## 7 1.2829649 Medio1 0.041736761
## 8 0.9722890 Medio1 -0.268939178
## 9 1.0763665 Medio1 -0.164861669
## 10 1.1197808 Medio1 -0.121447311
## 11 1.4203347 Medio1 0.179106568
## 12 1.2647665 Medio1 0.023538333
## 13 1.2721389 Medio1 0.030910705
## 14 1.2199229 Medio1 -0.021305267
## 15 1.0999486 Medio1 -0.141279560
## 16 1.5216444 Medio2 0.369789688
## 17 1.2896131 Medio2 0.137758410
## 18 0.8460089 Medio2 -0.305845765
## 19 1.3262441 Medio2 0.174389386
## 20 1.1148975 Medio2 -0.036957130
## 21 1.0077917 Medio2 -0.144062943
## 22 1.1607645 Medio2 0.008909839
## 23 1.0153192 Medio2 -0.136535477
## 24 1.0687996 Medio2 -0.083055098
## 25 1.0874929 Medio2 -0.064361745
## 26 0.8963952 Medio2 -0.255459472
## 27 1.3508017 Medio2 0.198946992
## 28 1.2276072 Medio2 0.075752485
## 29 0.9951354 Medio2 -0.156719325
## 30 1.4256867 Medio2 0.273832009
## 31 1.2767636 Medio3 0.097206841
## 32 1.1468871 Medio3 -0.032669586
## 33 1.3611226 Medio3 0.181565900
## 34 1.3580640 Medio3 0.178507309
## 35 1.3478846 Medio3 0.168327876
## 36 1.3239552 Medio3 0.144398527
## 37 1.2997052 Medio3 0.120148459
## 38 1.1888559 Medio3 0.009299173
## 39 1.1449267 Medio3 -0.034629998
## 40 1.1315152 Medio3 -0.048041499
## 41 1.0749527 Medio3 -0.104603975
## 42 1.1625749 Medio3 -0.016981829
## 43 0.9722287 Medio3 -0.207328062
## 44 1.5904121 Medio3 0.410855355
## 45 1.4174332 Medio3 0.237876441
tapply(efectos,dfj$medio,mean)
## Medio1 Medio2 Medio3
## -0.56997709 0.00375879 0.07359540