item2 ~ ~ 1 * item2
item3 ~ ~ 1 * item3
item4 ~ ~ 1 * item4
# thresholds
item1 | t1
item2 | t1
item3 | t1
item4 | t1
"
fit <- lavaan :: sem ( model , data = aaaa , estimator = ' WLSMV' , link = " logit" , do.fit = T , mimic = " Mplus" )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
# residual correlations
item1 ~ ~ 1 * item2 +1 * item3 +1 * item4
item2 ~ ~ 1 * item3 +1 * item4
item3 ~ ~ 1 * item4
"
fit <- lavaan :: sem ( model , data = aaaa , estimator = ' WLSMV' , link = " logit" , do.fit = T , mimic = " Mplus" )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ item1
lt = ~ item2
lt = ~ item3
lt = ~ item4
# regressions
lt ~ TT
# residual correlations
item1 ~ ~ 1 * item2 +1 * item3 +1 * item4
item2 ~ ~ 1 * item3 +1 * item4
item3 ~ ~ 1 * item4
"
fit <- lavaan :: sem ( model , data = aaaa , estimator = ' WLSMV' , link = " logit" , do.fit = T , mimic = " Mplus" )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ item1
lt = ~ item2
lt = ~ item3
lt = ~ item4
# regressions
lt ~ TT
# residual correlations
item1 ~ ~ 1 * item2 +1 * item3 +1 * item4
item2 ~ ~ 1 * item3 +1 * item4
item3 ~ ~ 1 * item4
"
fit <- lavaan :: sem ( model , data = aaaa , estimator = ' MML' , link = " logit" , do.fit = T , mimic = " Mplus" )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
# residual correlations
item1 ~ ~ item1
item2 ~ ~ item2
item3 ~ ~ item3
item4 ~ ~ item4
"
fit <- lavaan :: cfa ( model , data = aaaa , estimator = ' MML' , link = " logit" , do.fit = T , mimic = " Mplus" )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
# residual correlations
item1 ~ ~ item1
item2 ~ ~ item2
item3 ~ ~ item3
item4 ~ ~ item4
"
fit <- lavaan :: cfa ( model , data = aaaa , estimator = ' MML' , link = " logit" , do.fit = T , mimic = " Mplus" , std.lv = T )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
item1 | t1
item2 | t1
item3 | t1
item4 | t1
# residual correlations
item1 ~ ~ item1
item2 ~ ~ item2
item3 ~ ~ item3
item4 ~ ~ item4
"
fit <- lavaan :: cfa ( model , data = aaaa , estimator = ' MML' , link = " logit" , do.fit = T , mimic = " Mplus" , std.lv = T )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
item1 | t1
item2 | t2
item3 | t3
item4 | t4
# residual correlations
item1 ~ ~ item1
item2 ~ ~ item2
item3 ~ ~ item3
item4 ~ ~ item4
"
fit <- lavaan :: cfa ( model , data = aaaa , estimator = ' MML' , link = " logit" , do.fit = T , mimic = " Mplus" , std.lv = T )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
item1 | t1
item2 | t1
item3 | t1
item4 | t1
# residual correlations
item1 ~ ~ item1
item2 ~ ~ item2
item3 ~ ~ item3
item4 ~ ~ item4
"
fit <- lavaan :: cfa ( model , data = aaaa , estimator = ' MML' , link = " logit" , do.fit = T , mimic = " Mplus" , std.lv = T )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
item1 | t1
item2 | t1
item3 | t1
item4 | t1
# residual correlations
item1 ~ ~ item1
item2 ~ ~ item2
item3 ~ ~ item3
item4 ~ ~ item4
"
fit <- lavaan :: sem ( model , data = aaaa , estimator = ' MML' , link = " logit" , do.fit = T , mimic = " Mplus" , std.lv = T )
summary ( fit )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
item1 | t1
item2 | t1
item3 | t1
item4 | t1
# residual correlations
item1 ~ ~ item1
item2 ~ ~ item2
item3 ~ ~ item3
item4 ~ ~ item4
"
fit <- lavaan :: sem ( model , data = aaaa , estimator = ' ML' , link = " logit" , do.fit = T , mimic = " Mplus" , std.lv = T )
library ( lavaan )
model <- "
# measurement model
lt = ~ 1 * item1
lt = ~ 1 * item2
lt = ~ 1 * item3
lt = ~ 1 * item4
# regressions
lt ~ TT
item1 | t1
item2 | t1
item3 | t1
item4 | t1
# residual correlations
item1 ~ ~ item1
item2 ~ ~ item2
item3 ~ ~ item3
item4 ~ ~ item4
"
fit <- lavaan :: sem ( model , data = aaaa , estimator = ' WLSMV' , link = " logit" , do.fit = T , mimic = " Mplus" , std.lv = T )
summary ( fit )
lavaanCatItemPlot = function ( lavObject , varname , sds = 3 ) { output = inspect ( object = lavObject , what = " est" ) if ( ! varname %in% rownames ( output $ lambda ) ) stop ( paste ( varname , " not found in lavaan object" ) ) if ( dim ( output $ lambda ) [2 ] > 1 ) stop ( " plots only given for one factor models" ) itemloading = output $ lambda [which ( rownames ( output $ lambda ) == varname ) , 1 ] itemthresholds = output $ tau [grep ( pattern = varname , x = rownames ( output $ tau ) ) ] factorname = colnames ( output $ lambda ) factormean = output $ alpha [which ( rownames ( output $ alpha ) == factorname ) ] factorvar = output $ psi [which ( rownames ( output $ psi ) == factorname ) ] factormin = factormean - 3 * sqrt ( factorvar ) factormax = factormean + 3 * sqrt ( factorvar ) factorX = seq ( factormin , factormax , .01 ) itemloc = which ( lavObject @ Data @ ov $ name == varname ) itemlevels = unlist ( strsplit ( x = lavObject @ Data @ ov $ lnam [itemloc ] , split = " \\|" ) ) if ( length ( itemthresholds ) > 1 ) { plotdata = NULL plotdata2 = NULL itemY = NULL itemY2 = NULL itemX = NULL itemText = NULL for ( level in 1 : length ( itemthresholds ) ) { itemY = pnorm ( q = -1 * itemthresholds [level ] + itemloading * factorX ) itemY2 = cbind ( itemY2 , pnorm ( q = -1 * itemthresholds [level ] + itemloading * factorX ) ) itemText = paste0 ( " P(" , varname , " > " , itemlevels [level ] , " )" ) itemText2 = paste0 ( " P(" , varname , " = " , itemlevels [level ] , " )" ) plotdata = rbind ( plotdata , data.frame ( factor = factorX , prob = itemY , plot = itemText ) ) if ( level == 1 ) { plotdata2 = data.frame ( factor = factorX , plot = itemText2 , prob = matrix ( 1 , nrow = dim ( itemY2 ) [1 ] , ncol = 1 ) - itemY2 [ , level ] ) } else if ( level == length ( itemthresholds ) ) { plotdata2 = rbind ( plotdata2 , data.frame ( factor = factorX , plot = itemText2 , prob = itemY2 [ , level -1 ] - itemY2 [ , level ] ) ) plotdata2 = rbind ( plotdata2 , data.frame ( factor = factorX , plot = paste0 ( " P(" , varname , " = " , itemlevels [level +1 ] , " )" ) , prob = itemY2 [ , level ] ) ) } else { plotdata2 = rbind ( plotdata2 , data.frame ( factor = factorX , plot = itemText2 , prob = itemY2 [ , level -1 ] - itemY2 [ , level ] ) ) } } names ( plotdata ) = c ( factorname , " Probability" , " Cumulative" ) ggplot ( data = plotdata , aes_string ( x = factorname , y = " Probability" , colour = " Cumulative" ) ) + geom_line ( size = 2 ) names ( plotdata2 ) = c ( factorname , " Response" , " Probability" ) ggplot ( data = plotdata2 , aes_string ( x = factorname , y = " Probability" , colour = " Response" ) ) + geom_line ( size = 2 ) } else { itemY = pnorm ( q = -1 * itemthresholds [1 ] + itemloading * factorX ) itemText2 = paste0 ( " P(" , varname , " = " , itemlevels [1 ] , " )" ) plotdata = data.frame ( factor = factorX , prob = itemY , plot = itemText2 ) names ( plotdata ) = c ( factorname , " Probability" , " Response" ) ggplot ( data = plotdata , aes_string ( x = factorname , y = " Probability" , colour = " Response" ) ) + geom_line ( size = 2 ) } } lavaanCatItemPlot ( lavObject = grm2Pestimates , varname = " cia2" , sds = 3 )
## Liste des scenarios
results <- c ( sapply ( 1 : 4 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' ) ) ) , sapply ( 5 : 9 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' , ' F' , ' G' ) ) ) )
results2 <- c ( sapply ( 10 : 20 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' , ' F' , ' G' ) ) ) )
results <- c ( sapply ( c ( 50 , 100 , 200 , 300 ) , function ( x ) paste0 ( results , ' _' , x ) ) )
results2 <- c ( sapply ( c ( 50 , 100 , 200 , 300 ) , function ( x ) paste0 ( results2 , ' _' , x ) ) )
results <- sort ( results )
results2 <- sort ( results2 )
results <- c ( results , results2 )
for ( r in results [5 ] ) {
cat ( ' --------------------------------------------------------------------------\n' )
cat ( paste0 ( r , " \n" ) )
cat ( ' --------------------------------------------------------------------------\n' )
#### Importer les datas
scen <- as.numeric ( gsub ( " [A,B,C,D,E,F,G,_]" , " " , substr ( r , 0 , 3 ) ) )
if ( substr ( r , start = nchar ( r ) -1 , stop = nchar ( r ) ) == " 50" ) {
N <- 50
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 100" ) {
N <- 100
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 200" ) {
N <- 200
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 300" ) {
N <- 300
}
#### Importer l'analyse
analyse <- read.csv ( paste0 ( " /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/" , r , " .csv" ) )
analyse [is.na ( analyse ) ] <- " "
names ( analyse ) [names ( analyse ) == " dif.detect.1" ] <- " dif_detect_1"
names ( analyse ) [names ( analyse ) == " dif.detect.2" ] <- " dif_detect_2"
names ( analyse ) [names ( analyse ) == " dif.detect.3" ] <- " dif_detect_3"
names ( analyse ) [names ( analyse ) == " dif.detect.4" ] <- " dif_detect_4"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.1" ] <- " dif_detect_unif_1"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.2" ] <- " dif_detect_unif_2"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.3" ] <- " dif_detect_unif_3"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.4" ] <- " dif_detect_unif_4"
analyse [analyse $ dif_detect_1 == 999 , " dif_detect_1" ] <- " "
analyse [analyse $ dif_detect_2 == 999 , " dif_detect_2" ] <- " "
analyse [analyse $ dif_detect_3 == 999 , " dif_detect_3" ] <- " "
analyse [analyse $ dif_detect_4 == 999 , " dif_detect_4" ] <- " "
analyse [analyse $ dif_detect_unif_1 == 999 , " dif_detect_unif_1" ] <- " "
analyse [analyse $ dif_detect_unif_2 == 999 , " dif_detect_unif_2" ] <- " "
analyse [analyse $ dif_detect_unif_3 == 999 , " dif_detect_unif_3" ] <- " "
analyse [analyse $ dif_detect_unif_4 == 999 , " dif_detect_unif_4" ] <- " "
if ( scen == 3 | scen == 4 | scen >= 13 ) {
names ( analyse ) [names ( analyse ) == " dif.detect.5" ] <- " dif_detect_5"
names ( analyse ) [names ( analyse ) == " dif.detect.6" ] <- " dif_detect_6"
names ( analyse ) [names ( analyse ) == " dif.detect.7" ] <- " dif_detect_7"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.5" ] <- " dif_detect_unif_5"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.6" ] <- " dif_detect_unif_6"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.7" ] <- " dif_detect_unif_7"
analyse [analyse $ dif_detect_5 == 999 , " dif_detect_5" ] <- " "
analyse [analyse $ dif_detect_6 == 999 , " dif_detect_6" ] <- " "
analyse [analyse $ dif_detect_7 == 999 , " dif_detect_7" ] <- " "
analyse [analyse $ dif_detect_unif_5 == 999 , " dif_detect_unif_5" ] <- " "
analyse [analyse $ dif_detect_unif_6 == 999 , " dif_detect_unif_6" ] <- " "
analyse [analyse $ dif_detect_unif_7 == 999 , " dif_detect_unif_7" ] <- " "
}
analyse <- analyse [ , ! names ( analyse ) %in% c ( " X" , " X.1" , " X.2" ) ]
write.csv ( analyse , paste0 ( " /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/" , r , " .csv" ) )
}
## Liste des scenarios
results <- c ( sapply ( 1 : 4 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' ) ) ) , sapply ( 5 : 9 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' , ' F' , ' G' ) ) ) )
results2 <- c ( sapply ( 10 : 20 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' , ' F' , ' G' ) ) ) )
results <- c ( sapply ( c ( 50 , 100 , 200 , 300 ) , function ( x ) paste0 ( results , ' _' , x ) ) )
results2 <- c ( sapply ( c ( 50 , 100 , 200 , 300 ) , function ( x ) paste0 ( results2 , ' _' , x ) ) )
results <- sort ( results )
results2 <- sort ( results2 )
results <- c ( results , results2 )
## Importer l'analyse resali pour chaque scenario
for ( r in results [5 ] ) {
cat ( ' --------------------------------------------------------------------------\n' )
cat ( paste0 ( r , " \n" ) )
cat ( ' --------------------------------------------------------------------------\n' )
#### Importer les datas
scen <- as.numeric ( gsub ( " [A,B,C,D,E,F,G,_]" , " " , substr ( r , 0 , 3 ) ) )
if ( substr ( r , start = nchar ( r ) -1 , stop = nchar ( r ) ) == " 50" ) {
N <- 50
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 100" ) {
N <- 100
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 200" ) {
N <- 200
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 300" ) {
N <- 300
}
if ( scen < 5 ) {
datt <- read.csv ( paste0 ( ' /home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N' , N , ' /scenario_' , r , ' .csv' ) )
}
if ( scen >= 5 ) {
datt <- read.csv ( paste0 ( ' /home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N' , N , ' /scenario_' , r , ' .csv' ) )
}
#### Importer l'analyse
analyse <- read.csv ( paste0 ( ' /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection/' , r , " .csv" ) )
#### Pour chaque replication
for ( k in 1 : 1000 ) {
if ( k %% 100 == 0 ) {
cat ( paste0 ( " N = " , k , " / 1000\n" ) )
}
datt [datt $ replication == k , " dif.detect.1" ] <- analyse [analyse $ X == k , " dif.detect.1" ]
datt [datt $ replication == k , " dif.detect.2" ] <- analyse [analyse $ X == k , " dif.detect.2" ]
datt [datt $ replication == k , " dif.detect.3" ] <- analyse [analyse $ X == k , " dif.detect.3" ]
datt [datt $ replication == k , " dif.detect.4" ] <- analyse [analyse $ X == k , " dif.detect.4" ]
datt [datt $ replication == k , " dif.detect.unif.1" ] <- analyse [analyse $ X == k , " dif.detect.unif.1" ]
datt [datt $ replication == k , " dif.detect.unif.2" ] <- analyse [analyse $ X == k , " dif.detect.unif.2" ]
datt [datt $ replication == k , " dif.detect.unif.3" ] <- analyse [analyse $ X == k , " dif.detect.unif.3" ]
datt [datt $ replication == k , " dif.detect.unif.4" ] <- analyse [analyse $ X == k , " dif.detect.unif.4" ]
if ( scen == 3 | scen == 4 | scen >= 13 ) {
datt [datt $ replication == k , " dif.detect.5" ] <- analyse [analyse $ X == k , " dif.detect.5" ]
datt [datt $ replication == k , " dif.detect.6" ] <- analyse [analyse $ X == k , " dif.detect.6" ]
datt [datt $ replication == k , " dif.detect.7" ] <- analyse [analyse $ X == k , " dif.detect.7" ]
datt [datt $ replication == k , " dif.detect.unif.5" ] <- analyse [analyse $ X == k , " dif.detect.unif.5" ]
datt [datt $ replication == k , " dif.detect.unif.6" ] <- analyse [analyse $ X == k , " dif.detect.unif.6" ]
datt [datt $ replication == k , " dif.detect.unif.7" ] <- analyse [analyse $ X == k , " dif.detect.unif.7" ]
}
}
datt [is.na ( datt $ dif.detect.1 ) , " dif.detect.1" ] <- " "
datt [is.na ( datt $ dif.detect.2 ) , " dif.detect.2" ] <- " "
datt [is.na ( datt $ dif.detect.3 ) , " dif.detect.3" ] <- " "
datt [is.na ( datt $ dif.detect.4 ) , " dif.detect.4" ] <- " "
datt [is.na ( datt $ dif.detect.unif.1 ) , " dif.detect.unif.1" ] <- " "
datt [is.na ( datt $ dif.detect.unif.2 ) , " dif.detect.unif.2" ] <- " "
datt [is.na ( datt $ dif.detect.unif.3 ) , " dif.detect.unif.3" ] <- " "
datt [is.na ( datt $ dif.detect.unif.4 ) , " dif.detect.unif.4" ] <- " "
if ( scen == 3 | scen == 4 | scen >= 13 ) {
datt [is.na ( datt $ dif.detect.5 ) , " dif.detect.5" ] <- " "
datt [is.na ( datt $ dif.detect.6 ) , " dif.detect.6" ] <- " "
datt [is.na ( datt $ dif.detect.7 ) , " dif.detect.7" ] <- " "
datt [is.na ( datt $ dif.detect.unif.5 ) , " dif.detect.unif.5" ] <- " "
datt [is.na ( datt $ dif.detect.unif.6 ) , " dif.detect.unif.6" ] <- " "
datt [is.na ( datt $ dif.detect.unif.7 ) , " dif.detect.unif.7" ] <- " "
}
write.csv ( datt , paste0 ( " /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/" , r , " .csv" ) )
}
for ( r in results [5 ] ) {
cat ( ' --------------------------------------------------------------------------\n' )
cat ( paste0 ( r , " \n" ) )
cat ( ' --------------------------------------------------------------------------\n' )
#### Importer les datas
scen <- as.numeric ( gsub ( " [A,B,C,D,E,F,G,_]" , " " , substr ( r , 0 , 3 ) ) )
if ( substr ( r , start = nchar ( r ) -1 , stop = nchar ( r ) ) == " 50" ) {
N <- 50
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 100" ) {
N <- 100
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 200" ) {
N <- 200
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 300" ) {
N <- 300
}
#### Importer l'analyse
analyse <- read.csv ( paste0 ( " /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/" , r , " .csv" ) )
analyse [is.na ( analyse ) ] <- " "
names ( analyse ) [names ( analyse ) == " dif.detect.1" ] <- " dif_detect_1"
names ( analyse ) [names ( analyse ) == " dif.detect.2" ] <- " dif_detect_2"
names ( analyse ) [names ( analyse ) == " dif.detect.3" ] <- " dif_detect_3"
names ( analyse ) [names ( analyse ) == " dif.detect.4" ] <- " dif_detect_4"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.1" ] <- " dif_detect_unif_1"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.2" ] <- " dif_detect_unif_2"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.3" ] <- " dif_detect_unif_3"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.4" ] <- " dif_detect_unif_4"
if ( scen == 3 | scen == 4 | scen >= 13 ) {
names ( analyse ) [names ( analyse ) == " dif.detect.5" ] <- " dif_detect_5"
names ( analyse ) [names ( analyse ) == " dif.detect.6" ] <- " dif_detect_6"
names ( analyse ) [names ( analyse ) == " dif.detect.7" ] <- " dif_detect_7"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.5" ] <- " dif_detect_unif_5"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.6" ] <- " dif_detect_unif_6"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.7" ] <- " dif_detect_unif_7"
}
analyse <- analyse [ , ! names ( analyse ) %in% c ( " X" , " X.1" , " X.2" ) ]
write.csv ( analyse , paste0 ( " /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/" , r , " .csv" ) )
}
results
## Liste des scenarios
results <- c ( sapply ( 1 : 4 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' ) ) ) , sapply ( 5 : 9 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' , ' F' , ' G' ) ) ) )
results2 <- c ( sapply ( 10 : 20 , function ( x ) paste0 ( x , c ( ' A' , ' B' , ' C' , ' D' , ' E' , ' F' , ' G' ) ) ) )
results <- c ( sapply ( c ( 50 , 100 , 200 , 300 ) , function ( x ) paste0 ( results , ' _' , x ) ) )
results2 <- c ( sapply ( c ( 50 , 100 , 200 , 300 ) , function ( x ) paste0 ( results2 , ' _' , x ) ) )
results <- sort ( results )
results2 <- sort ( results2 )
results <- c ( results , results2 )
## Importer l'analyse resali pour chaque scenario
for ( r in results [17 ] ) {
cat ( ' --------------------------------------------------------------------------\n' )
cat ( paste0 ( r , " \n" ) )
cat ( ' --------------------------------------------------------------------------\n' )
#### Importer les datas
scen <- as.numeric ( gsub ( " [A,B,C,D,E,F,G,_]" , " " , substr ( r , 0 , 3 ) ) )
if ( substr ( r , start = nchar ( r ) -1 , stop = nchar ( r ) ) == " 50" ) {
N <- 50
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 100" ) {
N <- 100
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 200" ) {
N <- 200
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 300" ) {
N <- 300
}
if ( scen < 5 ) {
datt <- read.csv ( paste0 ( ' /home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N' , N , ' /scenario_' , r , ' .csv' ) )
}
if ( scen >= 5 ) {
datt <- read.csv ( paste0 ( ' /home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N' , N , ' /scenario_' , r , ' .csv' ) )
}
#### Importer l'analyse
analyse <- read.csv ( paste0 ( ' /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection/' , r , " .csv" ) )
#### Pour chaque replication
for ( k in 1 : 1000 ) {
if ( k %% 100 == 0 ) {
cat ( paste0 ( " N = " , k , " / 1000\n" ) )
}
datt [datt $ replication == k , " dif.detect.1" ] <- analyse [analyse $ X == k , " dif.detect.1" ]
datt [datt $ replication == k , " dif.detect.2" ] <- analyse [analyse $ X == k , " dif.detect.2" ]
datt [datt $ replication == k , " dif.detect.3" ] <- analyse [analyse $ X == k , " dif.detect.3" ]
datt [datt $ replication == k , " dif.detect.4" ] <- analyse [analyse $ X == k , " dif.detect.4" ]
datt [datt $ replication == k , " dif.detect.unif.1" ] <- analyse [analyse $ X == k , " dif.detect.unif.1" ]
datt [datt $ replication == k , " dif.detect.unif.2" ] <- analyse [analyse $ X == k , " dif.detect.unif.2" ]
datt [datt $ replication == k , " dif.detect.unif.3" ] <- analyse [analyse $ X == k , " dif.detect.unif.3" ]
datt [datt $ replication == k , " dif.detect.unif.4" ] <- analyse [analyse $ X == k , " dif.detect.unif.4" ]
if ( scen == 3 | scen == 4 | scen >= 13 ) {
datt [datt $ replication == k , " dif.detect.5" ] <- analyse [analyse $ X == k , " dif.detect.5" ]
datt [datt $ replication == k , " dif.detect.6" ] <- analyse [analyse $ X == k , " dif.detect.6" ]
datt [datt $ replication == k , " dif.detect.7" ] <- analyse [analyse $ X == k , " dif.detect.7" ]
datt [datt $ replication == k , " dif.detect.unif.5" ] <- analyse [analyse $ X == k , " dif.detect.unif.5" ]
datt [datt $ replication == k , " dif.detect.unif.6" ] <- analyse [analyse $ X == k , " dif.detect.unif.6" ]
datt [datt $ replication == k , " dif.detect.unif.7" ] <- analyse [analyse $ X == k , " dif.detect.unif.7" ]
}
}
datt [is.na ( datt $ dif.detect.1 ) , " dif.detect.1" ] <- " "
datt [is.na ( datt $ dif.detect.2 ) , " dif.detect.2" ] <- " "
datt [is.na ( datt $ dif.detect.3 ) , " dif.detect.3" ] <- " "
datt [is.na ( datt $ dif.detect.4 ) , " dif.detect.4" ] <- " "
datt [is.na ( datt $ dif.detect.unif.1 ) , " dif.detect.unif.1" ] <- " "
datt [is.na ( datt $ dif.detect.unif.2 ) , " dif.detect.unif.2" ] <- " "
datt [is.na ( datt $ dif.detect.unif.3 ) , " dif.detect.unif.3" ] <- " "
datt [is.na ( datt $ dif.detect.unif.4 ) , " dif.detect.unif.4" ] <- " "
if ( scen == 3 | scen == 4 | scen >= 13 ) {
datt [is.na ( datt $ dif.detect.5 ) , " dif.detect.5" ] <- " "
datt [is.na ( datt $ dif.detect.6 ) , " dif.detect.6" ] <- " "
datt [is.na ( datt $ dif.detect.7 ) , " dif.detect.7" ] <- " "
datt [is.na ( datt $ dif.detect.unif.5 ) , " dif.detect.unif.5" ] <- " "
datt [is.na ( datt $ dif.detect.unif.6 ) , " dif.detect.unif.6" ] <- " "
datt [is.na ( datt $ dif.detect.unif.7 ) , " dif.detect.unif.7" ] <- " "
}
write.csv ( datt , paste0 ( " /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/" , r , " .csv" ) )
}
for ( r in results [17 ] ) {
cat ( ' --------------------------------------------------------------------------\n' )
cat ( paste0 ( r , " \n" ) )
cat ( ' --------------------------------------------------------------------------\n' )
#### Importer les datas
scen <- as.numeric ( gsub ( " [A,B,C,D,E,F,G,_]" , " " , substr ( r , 0 , 3 ) ) )
if ( substr ( r , start = nchar ( r ) -1 , stop = nchar ( r ) ) == " 50" ) {
N <- 50
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 100" ) {
N <- 100
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 200" ) {
N <- 200
}
if ( substr ( r , start = nchar ( r ) -2 , stop = nchar ( r ) ) == " 300" ) {
N <- 300
}
#### Importer l'analyse
analyse <- read.csv ( paste0 ( " /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/" , r , " .csv" ) )
analyse [is.na ( analyse ) ] <- " "
names ( analyse ) [names ( analyse ) == " dif.detect.1" ] <- " dif_detect_1"
names ( analyse ) [names ( analyse ) == " dif.detect.2" ] <- " dif_detect_2"
names ( analyse ) [names ( analyse ) == " dif.detect.3" ] <- " dif_detect_3"
names ( analyse ) [names ( analyse ) == " dif.detect.4" ] <- " dif_detect_4"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.1" ] <- " dif_detect_unif_1"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.2" ] <- " dif_detect_unif_2"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.3" ] <- " dif_detect_unif_3"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.4" ] <- " dif_detect_unif_4"
if ( scen == 3 | scen == 4 | scen >= 13 ) {
names ( analyse ) [names ( analyse ) == " dif.detect.5" ] <- " dif_detect_5"
names ( analyse ) [names ( analyse ) == " dif.detect.6" ] <- " dif_detect_6"
names ( analyse ) [names ( analyse ) == " dif.detect.7" ] <- " dif_detect_7"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.5" ] <- " dif_detect_unif_5"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.6" ] <- " dif_detect_unif_6"
names ( analyse ) [names ( analyse ) == " dif.detect.unif.7" ] <- " dif_detect_unif_7"
}
analyse <- analyse [ , ! names ( analyse ) %in% c ( " X" , " X.1" , " X.2" ) ]
write.csv ( analyse , paste0 ( " /home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/" , r , " .csv" ) )
}
