You really need to pick small problems and build solutions for them as
functions rather than copy-pasting code. Then you can build more
complicated solutions using those small solutions that can actually be
understood. I suspect that in a few days you would not understand your own
code because it is so complicated... but that is not inevitable... you
_can_ avoid creating write-only code.
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(tidyr)
damage_states <- c( 'Collapse', 'Extensive', 'Moderate', 'Slight')
# the following re-definition of d1 uses the stringsAsFactors parameter to
# prevent the character data from automatically being converted to
# factors, which is much better than having to use as.character later
# throughout your code.
d3 <- data.frame( Name = rep(c( 'Hancilar et. al (2014) - CR/LDUAL school -
Case V (Sd)'
, 'Rojas(2010) - CR/LFM/DNO 2storey'
, 'Rojas(2010) - CR/LFM/DNO 3storey'
)
, each = 4
)
, Taxonomy = rep(c(
'CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//'
, 'CR/LFM/DNO/H:2/EDU2'
, 'CR/LFM/DNO/H:3'
)
, each = 4
)
, Damage_State = rep( damage_states, times = 3 )
, Y_vals = c(
'0,6.49e-45,1.29e-29,3.35e-22,1.25e-17,1.8e-14,3.81e-12,2.35e-10,6.18e-09,8.78e-08,7.86e-07,4.92e-06,2.32e-05,8.76e-05,0.000274154,0.000736426,0.001740046,0.003688955,0.007130224,0.012730071,0.021221055,0.0333283,0.049687895,0.070771949,0.096832412,0.12787106'
,
'5.02e-182,3.52e-10,8.81e-07,3.62e-05,0.000346166,0.001608096,0.004916965,0.01150426,0.022416772,0.038311015,0.059392175,0.085458446,0.115998702,0.150303282,0.187564259,0.226954808,0.267685669,0.309041053,0.35039806,0.391233913,0.431124831,0.469739614,0.506830242,0.542221151,0.575798268,0.607498531'
,
'0,1.05e-10,4.75e-06,0.000479751,0.006156253,0.02983369,0.084284357,0.171401809,0.281721077,0.401071017,0.516782184,0.620508952,0.708327468,0.779597953,0.835636781,0.87866127,0.911104254,0.935237852,0.95300803,0.965993954,0.97543154,0.982263787,0.987197155,0.990753887,0.993316294,0.99516227'
,
'4.61e-149,0.007234459,0.158482316,0.438164341,0.671470035,0.818341464,0.901312438,0.946339742,0.970531767,0.983584997,0.990707537,0.994650876,0.996869188,0.998137671,0.998874868,0.9993101,0.999570978,0.999729626,0.999827443,0.999888548,0.999927197,0.999951931,0.999967938,0.999978407,0.999985325,0.999989939'
,
'0,4.91e-109,2.88e-47,3.32e-23,1.65e-11,1.78e-05,0.018162775,0.356628282,0.870224163,0.992779045,0.999855873,0.999998652,0.999999993,1,1,1,1,1,1,1,1,1,1,1,1,1'
,
'0,1.21e-32,1.78e-05,0.645821244,0.999823159,0.999999999,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1'
,
'0,0.077161367,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1'
,
'0,0.996409276,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1'
,
'0,1.29e-144,1.99e-71,1.16e-40,3.23e-24,1.59e-14,1.41e-08,6.42e-05,0.00971775,0.153727719,0.562404795,0.889217735,0.985915683,0.998997836,0.999955341,0.999998628,0.999999969,0.999999999,1,1,1,1,1,1,1,1'
,
'0,2.12e-51,4.89e-14,0.001339285,0.559153268,0.995244295,0.999997786,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1'
,
'0,3.22e-07,0.992496021,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1'
,
'0,0.368907496,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1'
)
, stringsAsFactors = FALSE
)
# convert Damage_State to a factor using the desired sequence of levels
# This is useful when spread'ing the values to create columns in the
# desired order
d3$Damage_State = factor( d3$Damage_State, levels = damage_states )
d3long <- ( d3
%>% separate_rows( Y_vals, sep="," )
%>% mutate( Y_vals = as.numeric( Y_vals ) )
)
str( d3long )
#> 'data.frame': 312 obs. of 4 variables:
#> $ Name : chr "Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)" "Hancilar et. al
(2014) - CR/LDUAL school - Case V (Sd)" "Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)"
"Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)" ...
#> $ Taxonomy : chr "CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//"
"CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//"
"CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//"
"CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//" ...
#> $ Damage_State: Factor w/ 4 levels "Collapse","Extensive",..: 1 1 1 1 1 1 1
1 1 1 ...
#> $ Y_vals : num 0.00 6.49e-45 1.29e-29 3.35e-22 1.25e-17 ...
# By putting the vector of weights into a data frame you can make the
# meaning of the vector D1L self-documenting
weights <- data.frame( Damage_State = c( "Initial", rev( damage_states ) )
, D2L = c( 0, 2, 10, 50, 100 )
)
weights
#> Damage_State D2L
#> 1 Initial 0
#> 2 Slight 2
#> 3 Moderate 10
#> 4 Extensive 50
#> 5 Collapse 100
# Group-by to get one row per combination of Taxonomy, Damage_State,
# and Name, then create a position index value for each Y_vals value
# then split the dataframe by combinations of Taxonomy and Name. Each
# small data frame in the `data` column has its own columns Damage_State,
# Index, and Y_vals.
d3long1 <- ( d3long
%>% group_by( Taxonomy, Damage_State, Name )
%>% mutate( Index = seq.int( n() ) )
%>% ungroup()
%>% nest( data = -c( Taxonomy, Name ) )
)
d3long1
#> # A tibble: 3 x 3
#> Name Taxonomy data
#> <chr> <chr> <list>
#> 1 Hancilar et. al (2014) - CR/L~ CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990~ <tibble
[1~
#> 2 Rojas(2010) - CR/LFM/DNO 2sto~ CR/LFM/DNO/H:2/EDU2 <tibble
[1~
#> 3 Rojas(2010) - CR/LFM/DNO 3sto~ CR/LFM/DNO/H:3 <tibble
[1~
# function to convert a sub-dataframe into a matrix,
# built with tidyverse tools
DFL_to_matrix <- function( DFL, labelCol, indexCol, valueCol ) {
stopifnot( 3 == length( DFL ) )
( DFL
%>% spread( !!labelCol, !!valueCol )
%>% select( -!!indexCol )
%>% as.matrix
)
}
# same function built with base R
DFL_to_matrix2 <- function( DFL, labelCol, indexCol, valueCol ) {
as.matrix( data.frame( split( DFL[[ valueCol ]]
, DFL[[ labelCol ]]
)
)
)
}
# you can test small functions and later re-use them
#DFL_to_matrix2( d3long1$data[[1]], "Damage_State", "Index", "Y_vals" )
# Now convert the long data frames into matrices
# then use matrix multiplication to weight the damage
d3long2 <- ( d3long1
%>% mutate( mat = lapply( data
, DFL_to_matrix2
, labelCol = "Damage_State"
, indexCol = "Index"
, valueCol = "Y_vals"
)
, vc = lapply( mat
, function( m ) {
mr <- m[ , rev( seq.int( ncol( m ) ) ) ]
m1 <- cbind( matrix( 1, nrow = nrow( m
) )
, mr
)
m0 <- cbind( mr
, matrix( 0, nrow = nrow( m
) )
)
c( ( m1 - m0 ) %*% weights$D2L )
}
)
)
)
str( d3long2[ 1,] )
#> Classes 'tbl_df', 'tbl' and 'data.frame': 1 obs. of 5 variables:
#> $ Name : chr "Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)"
#> $ Taxonomy: chr
"CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//"
#> $ data :List of 1
#> ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 104 obs. of 3 variables:
#> .. ..$ Damage_State: Factor w/ 4 levels "Collapse","Extensive",..: 1 1 1 1
1 1 1 1 1 1 ...
#> .. ..$ Y_vals : num 0.00 6.49e-45 1.29e-29 3.35e-22 1.25e-17 ...
#> .. ..$ Index : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ mat :List of 1
#> ..$ : num [1:26, 1:4] 0.00 6.49e-45 1.29e-29 3.35e-22 1.25e-17 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : NULL
#> .. .. ..$ : chr "Collapse" "Extensive" "Moderate" "Slight"
#> $ vc :List of 1
#> ..$ : num 9.22e-149 1.45e-02 3.17e-01 8.82e-01 1.41 ...
d3long3 <- ( d3long2
%>% select( Name, Taxonomy, vc )
%>% mutate( Y_vals = unlist( lapply( vc
, function(v){paste(v,collapse =
",") } )))
%>% select( -vc )
)
d3long3
#> # A tibble: 3 x 3
#> Name Taxonomy Y_vals
#> <chr> <chr> <chr>
#> 1 Hancilar et. al (201~ CR/LDUAL/HEX:4+HFEX:12.8/~
9.22e-149,0.01446893292,0.31~
#> 2 Rojas(2010) - CR/LFM~ CR/LFM/DNO/H:2/EDU2
0,2.610109488,10.000712,35.8~
#> 3 Rojas(2010) - CR/LFM~ CR/LFM/DNO/H:3
0,0.737817568,9.939968168001~
On Sat, 21 Mar 2020, Ioanna Ioannou wrote:
Hello again,
Here is the reproducible example:
rm(list = ls())
library(plyr)
library(dplyr)
library( data.table)
library(stringr)
d1 <- data.frame( Name = rep(c('Hancilar et. al (2014) - CR/LDUAL school - Case
V (Sd)',
'Rojas(2010) - CR/LFM/DNO 2storey',
'Rojas(2010) - CR/LFM/DNO 3storey'), each = 4),
Taxonomy =
rep(c('CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//',
'CR/LFM/DNO/H:2/EDU2',
'CR/LFM/DNO/H:3'), each = 4),
Damage_State =rep(c('Collapse', 'Extensive', 'Moderate',
'Slight'), times =3),
Y_vals =
c('0,6.49e-45,1.29e-29,3.35e-22,1.25e-17,1.8e-14,3.81e-12,2.35e-10,6.18e-09,8.78e-08,7.86e-07,4.92e-06,2.32e-05,8.76e-05,0.000274154,0.000736426,0.001740046,0.003688955,0.007130224,0.012730071,0.021221055,0.0333283,0.049687895,0.070771949,0.096832412,0.12787106',
'5.02e-182,3.52e-10,8.81e-07,3.62e-05,0.000346166,0.001608096,0.004916965,0.01150426,0.022416772,0.038311015,0.059392175,0.085458446,0.115998702,0.150303282,0.187564259,0.226954808,0.267685669,0.309041053,0.35039806,0.391233913,0.431124831,0.469739614,0.506830242,0.542221151,0.575798268,0.607498531',
'0,1.05e-10,4.75e-06,0.000479751,0.006156253,0.02983369,0.084284357,0.171401809,0.281721077,0.401071017,0.516782184,0.620508952,0.708327468,0.779597953,0.835636781,0.87866127,0.911104254,0.935237852,0.95300803,0.965993954,0.97543154,0.982263787,0.987197155,0.990753887,0.993316294,0.99516227',
'4.61e-149,0.007234459,0.158482316,0.438164341,0.671470035,0.818341464,0.901312438,0.946339742,0.970531767,0.983584997,0.990707537,0.994650876,0.996869188,0.998137671,0.998874868,0.9993101,0.999570978,0.999729626,0.999827443,0.999888548,0.999927197,0.999951931,0.999967938,0.999978407,0.999985325,0.999989939',
'0,4.91e-109,2.88e-47,3.32e-23,1.65e-11,1.78e-05,0.018162775,0.356628282,0.870224163,0.992779045,0.999855873,0.999998652,0.999999993,1,1,1,1,1,1,1,1,1,1,1,1,1',
'0,1.21e-32,1.78e-05,0.645821244,0.999823159,0.999999999,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1',
'0,0.077161367,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1',
'0,0.996409276,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1',
'0,1.29e-144,1.99e-71,1.16e-40,3.23e-24,1.59e-14,1.41e-08,6.42e-05,0.00971775,0.153727719,0.562404795,0.889217735,0.985915683,0.998997836,0.999955341,0.999998628,0.999999969,0.999999999,1,1,1,1,1,1,1,1',
'0,2.12e-51,4.89e-14,0.001339285,0.559153268,0.995244295,0.999997786,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1',
'0,3.22e-07,0.992496021,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1',
'0,0.368907496,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1')
)
D2L <- c(0, 2, 10, 50, 100)
VC_final <- array(NA, length(distinct(d1[,c(1,2)])$Name) )
# get the rows for the four damage states
DS1_rows <- d1$Damage_State == unique(d1$Damage_State)[4]
DS2_rows <- d1$Damage_State == unique(d1$Damage_State)[3]
DS3_rows <- d1$Damage_State == unique(d1$Damage_State)[2]
DS4_rows <- d1$Damage_State == unique(d1$Damage_State)[1]
# step through all possible values of IM.type and Taxonomy and Name
#### This is true for this subset not generalibale needs to be checked first ##
VC <- matrix(NA, 3,26)
for(Tax in unique(d1$Taxonomy)) {
for(Name in unique(d1$Name)) {
# get a logical vector of the rows to be use DS5 in this calculation
calc_rows <- d1$Taxonomy == Tax & d1$Name == Name
# check that there are any such rows in the DS5ata frame
if(sum(calc_rows)) {
cat(Tax,Name,"\n")
# if so, fill in the four values for these rows
VC[calc_rows] <- D2L[1] * (1- as.numeric(unlist(str_split(as.character(d1[calc_rows
& DS1_rows,]$Y_vals), pattern = ","))) ) +
D2L[2]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS1_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS2_rows,]$Y_vals), pattern = ",")))) +
D2L[3]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS2_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS3_rows,]$Y_vals), pattern = ",")))) +
D2L[4] * (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS3_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS4_rows,]$Y_vals), pattern = ",")))) +
D2L[5]* as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS4_rows,]$Y_vals), pattern = ",")))
print(VC[calc_rows] )
}
}
}
Vul <- distinct(d1[,c(1,2)])
dim(VC) <- c(length(unlist(str_split(as.character(d1[2,]$Y_vals), pattern =
","))),length(distinct(d1[,c(1,2)])$Name)) ## (rows, cols)
VC
VC_t <- t(VC)
Vulnerability <- matrix(apply(VC_t, 1, function(x) paste(x, collapse = ',')))
Vul$Y_vals <- Vulnerability
________________________________
From: Jeff Newmiller <jdnew...@dcn.davis.ca.us>
Sent: 21 March 2020 16:27
To: r-help@r-project.org <r-help@r-project.org>; Ioanna Ioannou <ii54...@msn.com>;
r-help@r-project.org <r-help@r-project.org>
Subject: Re: [R] How to save output of multiple loops in a matrix
You have again posted using HTML and the result is unreadable. Please post a
reproducible example using dput instead of assuming we can read your formatted
code or table.
On March 21, 2020 8:59:58 AM PDT, Ioanna Ioannou <ii54...@msn.com> wrote:
Hello everyone,
I am having this data.frame. For each row you have 26 values aggregated
in a cell and separated by a comma. I want to do some calculations for
all unique names and taxonomy which include the four different damage
states. I can estimate the results but i am struggling to save them in
a data.frame and assign next to them the unique combination of the
name, taxonomy. Any help much appreciated.
d1 <- read.csv('test.csv')
D2L <- c(0, 2, 10, 50, 100)
VC_final <- array(NA, length(distinct(d1[,c(65,4,3)])$Name) )
VC <- matrix(NA,
length(distinct(d1[,c(65,4,3)])$Name),length(unlist(str_split(as.character(d1[1,]$Y_vals),
pattern = ","))))
# get the rows for the four damage states
DS1_rows <- d1$Damage_State == unique(d1$Damage_State)[4]
DS2_rows <- d1$Damage_State == unique(d1$Damage_State)[3]
DS3_rows <- d1$Damage_State == unique(d1$Damage_State)[2]
DS4_rows <- d1$Damage_State == unique(d1$Damage_State)[1]
# step through all possible values of IM.type and Taxonomy and Name
#### This is true for this subset not generalibale needs to be checked
first ##
for(IM in unique(d1$IM_type)) {
for(Tax in unique(d1$Taxonomy)) {
for(Name in unique(d1$Name)) {
# get a logical vector of the rows to be use DS5 in this calculation
calc_rows <- d1$IM_type == IM & d1$Taxonomy == Tax & d1$Name == Name
# check that there are any such rows in the DS5ata frame
if(sum(calc_rows)) {
cat(IM,Tax,Name,"\n")
# if so, fill in the four values for these rows
VC[calc_rows] <- D2L[1] * (1-
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS1_rows,]$Y_vals), pattern = ","))) ) +
D2L[2]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS1_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS2_rows,]$Y_vals), pattern = ",")))) +
D2L[3]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS2_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS3_rows,]$Y_vals), pattern = ",")))) +
D2L[4] * (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS3_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS4_rows,]$Y_vals), pattern = ",")))) +
D2L[5]* as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS4_rows,]$Y_vals), pattern = ",")))
print(VC[calc_rows] )
}
}
}
}
for(Tax in unique(d1$Taxonomy)) {
for(Name in unique(d1$Name)) {
# get a logical vector of the rows to be use DS5 in this calculation
calc_rows <- d1$IM_type == IM & d1$Taxonomy == Tax & d1$Name == Name
# check that there are any such rows in the DS5ata frame
if(sum(calc_rows)) {
cat(IM,Tax,Name,"\n")
# if so, fill in the four values for these rows
VC[calc_rows] <- D2L[1] * (1-
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS1_rows,]$Y_vals), pattern = ","))) ) +
D2L[2]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS1_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS2_rows,]$Y_vals), pattern = ",")))) +
D2L[3]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS2_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS3_rows,]$Y_vals), pattern = ",")))) +
D2L[4] * (as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS3_rows,]$Y_vals), pattern = ","))) -
as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS4_rows,]$Y_vals), pattern = ",")))) +
D2L[5]* as.numeric(unlist(str_split(as.character(d1[calc_rows &
DS4_rows,]$Y_vals), pattern = ",")))
print(unique(VC ))
}
}
}
Vul <- distinct(d1[,c(65,4,3)])
dim(VC) <- c(length(unlist(str_split(as.character(d1[1,]$Y_vals),
pattern = ","))),length(distinct(d1[,c(65,4,3)])$Name)) ## (rows,
cols)
VC
VC_t <- t(VC)
Vulnerability <- matrix(apply(VC_t, 1, function(x) paste(x, collapse =
',')))
Vul$Y_vals <- Vulnerability
Best,
ioanna
Name Taxonomy Damage_State Y_vals
Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1// Slight
4.61e-149,0.007234459,0.158482316,0.438164341,0.671470035,0.818341464,0.901312438,0.946339742,0.970531767,0.983584997,0.990707537,0.994650876,0.996869188,0.998137671,0.998874868,0.9993101,0.999570978,0.999729626,0.999827443,0.999888548,0.999927197,0.999951931,0.999967938,0.999978407,0.999985325,0.9
Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//
Collapse
0,6.49e-45,1.29e-29,3.35e-22,1.25e-17,1.8e-14,3.81e-12,2.35e-10,6.18e-09,8.78e-08,7.86e-07,4.92e-06,2.32e-05,8.76e-05,0.000274154,0.000736426,0.001740046,0.003688955,0.007130224,0.012730071,0.021221055,0.0333283,0.049687895,0.070771949,0.096832412,0.12787106
Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//
Extensive
5.02e-182,3.52e-10,8.81e-07,3.62e-05,0.000346166,0.001608096,0.004916965,0.01150426,0.022416772,0.038311015,0.059392175,0.085458446,0.115998702,0.150303282,0.187564259,0.226954808,0.267685669,0.309041053,0.35039806,0.391233913,0.431124831,0.469739614,0.506830242,0.542221151,0.575798268,0.607498531
Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//
Moderate
0,1.05e-10,4.75e-06,0.000479751,0.006156253,0.02983369,0.084284357,0.171401809,0.281721077,0.401071017,0.516782184,0.620508952,0.708327468,0.779597953,0.835636781,0.87866127,0.911104254,0.935237852,0.95300803,0.965993954,0.97543154,0.982263787,0.987197155,0.990753887,0.993316294,0.99516227
Rojas(2010) - CR/LFM/DNO 2storey CR/LFM/DNO/H:2/EDU2
Collapse
0,4.91e-109,2.88e-47,3.32e-23,1.65e-11,1.78e-05,0.018162775,0.356628282,0.870224163,0.992779045,0.999855873,0.999998652,0.999999993,1,1,1,1,1,1,1,1,1,1,1,1,1
Rojas(2010) - CR/LFM/DNO 2storey CR/LFM/DNO/H:2/EDU2
Extensive
0,1.21e-32,1.78e-05,0.645821244,0.999823159,0.999999999,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
Rojas(2010) - CR/LFM/DNO 2storey CR/LFM/DNO/H:2/EDU2
Moderate
0,0.077161367,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
Rojas(2010) - CR/LFM/DNO 2storey CR/LFM/DNO/H:2/EDU2 Slight
0,0.996409276,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
Rojas(2010) - CR/LFM/DNO 3storey CR/LFM/DNO/H:3 Collapse
0,1.29e-144,1.99e-71,1.16e-40,3.23e-24,1.59e-14,1.41e-08,6.42e-05,0.00971775,0.153727719,0.562404795,0.889217735,0.985915683,0.998997836,0.999955341,0.999998628,0.999999969,0.999999999,1,1,1,1,1,1,1,1
Rojas(2010) - CR/LFM/DNO 3storey CR/LFM/DNO/H:3 Extensive
0,2.12e-51,4.89e-14,0.001339285,0.559153268,0.995244295,0.999997786,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
Rojas(2010) - CR/LFM/DNO 3storey CR/LFM/DNO/H:3 Moderate
0,3.22e-07,0.992496021,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
Rojas(2010) - CR/LFM/DNO 3storey CR/LFM/DNO/H:3 Slight
0,0.368907496,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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