Re: [R] How to save output of multiple loops in a matrix
nlist(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
Sent: 21 March 2020 16:27
To: r-help@r-project.org ; Ioanna Ioannou
; 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 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 = ","))) -
[R] How to save output of multiple loops in a matrix
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$Y_vals <- Vulnerability
Best,
ioanna
NameTaxonomyDamage_StateY_vals
Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1// Slight
4.61e-149,0.007234459,0.158482316,0.438164341,0.671470035,0.818341464,0.901312438,0.946339742,0.970531767,0.983584997,0.990707537,0.994650876,0.996869188,0.998137671,0.998874868,0.9993101,0.999570978,0.9997296
[R] How to create a vector by searching information in multiple data.tables in r?
Hello everyone,
Once again i am a bit stack. I have over 200 json files with information. I
managed to manipulate them and their format is rather difficult as shown below.
Unfortunately, not all these files contain the same fields. I want to extract
e.g., the country from all these files. How can i add NA for the files for
which the country is not mentioned?
Here is a reproducible example. Lets say i have two files, three files, two
provide the country and the one does not. essentially i want a vector called
country which will look like this:
Country <- c('Colombia', 'Greece', NA)
Any help much appreciated!
Best,
ioanna
A<- data.frame( name1 = c('fields', 'fields', 'fields'),
name2= c('category', 'asset', 'country'),
value = c('Structure Class', 'Building',
'Colombia')
B<- data.frame( name1 = c('fields', 'fields', 'fields'),
name2= c('category', 'asset', 'country'),
value = c('Structure Class', 'Building',
'Greece')
C<- data.frame( name1 = c('fields', 'fields', 'fields'),
name2= c('category', 'asset', 'assessment'),
value = c('Structure Class', 'Building',
'Fragility')
[[alternative HTML version deleted]]
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to save multiple values of a variable in a json file in R
ok, my problem is the follwoing
a<- read_json(json_file)
a$fields$geo_applicability$fields$countries[[1]]$fields$name
this was i can see the name of a single country reported first in the json.
HOwever, i need to save all 59. My worry is that i have multiple files that i
need to read and in some cases there will be one country reported and in some
others multiple. How can i optimise the code?
Best,
From: Rainer M Krug
Sent: 16 January 2020 14:47
To: Ioanna Ioannou
Subject: Re: [R] How to save multiple values of a variable in a json file in R
Check the hep of the read_json() function and please keep the conversation on
the list
On 16 Jan 2020, at 15:42, Ioanna Ioannou
mailto:ii54...@msn.com>> wrote:
I will try the package but i could do with a worked example.
From: Rainer M Krug mailto:rai...@krugs.de>>
Sent: 16 January 2020 14:29
To: Ioanna Ioannou mailto:ii54...@msn.com>>
Subject: Re: [R] How to save multiple values of a variable in a json file in R
Have you looked at the jsonlite package?
Rainer
On 16 Jan 2020, at 15:21, Ioanna Ioannou
mailto:ii54...@msn.com>> wrote:
hello everyone,
and happy new year!
I have this problem: I want to save the name of the 'countries', the
'taxonomy_gem' and the 'minimum_im' and 'maximum_im' . The problem is that
there are several names of countries. How can i transfer the information from
the json file to an R data.frame? See below for the json file.
Best,
ioanna
json_file<- {
"pk": 670,
"model": "vulnerability.generalinformation",
"fields": {
"category": "Structure class",
"article_title": "A GLOBAL DATABASE OF VULNERABILITY MODELS FOR SEISMIC
RISK ASSESSMENT",
"name": "CR/LFINF/DUL/H:2",
"publication_conference_name": "16EECE",
"llrs": null,
"material": null,
"web_link": "",
"owner": {
"pk": 1900,
"model": "people.profile",
"fields": {
"username": "lmartins",
"first_name": "",
"last_name": "",
"email":
"luis.mart...@globalquakemodel.org<mailto:luis.mart...@globalquakemodel.org>"
}
},
"general_comments": "",
"geo_applicability": {
"pk": 669,
"model": "vulnerability.geoapplicability",
"fields": {
"general_information": 670,
"area": "",
"countries": [
{
"pk": "CIV",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "Cte d'Ivoire"
}
},
{
"pk": "CMR",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "Cameroon"
}
},
{
"pk": "SDN",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "Sudan"
}
},
{
"pk": "SSD",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "South Sudan"
}
},
{
"pk": "TUN",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"
[R] How to save multiple values of a variable in a json file in R
hello everyone,
and happy new year!
I have this problem: I want to save the name of the 'countries', the
'taxonomy_gem' and the 'minimum_im' and 'maximum_im' . The problem is that
there are several names of countries. How can i transfer the information from
the json file to an R data.frame? See below for the json file.
Best,
ioanna
{
"pk": 670,
"model": "vulnerability.generalinformation",
"fields": {
"category": "Structure class",
"article_title": "A GLOBAL DATABASE OF VULNERABILITY MODELS FOR SEISMIC
RISK ASSESSMENT",
"name": "CR/LFINF/DUL/H:2",
"publication_conference_name": "16EECE",
"llrs": null,
"material": null,
"web_link": "",
"owner": {
"pk": 1900,
"model": "people.profile",
"fields": {
"username": "lmartins",
"first_name": "",
"last_name": "",
"email": "luis.mart...@globalquakemodel.org"
}
},
"general_comments": "",
"geo_applicability": {
"pk": 669,
"model": "vulnerability.geoapplicability",
"fields": {
"general_information": 670,
"area": "",
"countries": [
{
"pk": "CIV",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "Cte d'Ivoire"
}
},
{
"pk": "CMR",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "Cameroon"
}
},
{
"pk": "SDN",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "Sudan"
}
},
{
"pk": "SSD",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "South Sudan"
}
},
{
"pk": "TUN",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "Tunisia"
}
},
{
"pk": "TGO",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "Togo"
}
},
{
"pk": "ZAF",
"model": "vulnerability.country",
"fields": {
"is_visible": true,
"region": 2,
"name": "South Africa"
}
},
{
"pk": "NER",
"model": "vulnerability.country",
"fields": {
Re: [R] How to create a new data.frame based on calculation of subsets of an existing data.frame
Hello Jim ,
Thank you ever so much for your help. I was truly stuck!
This looks much better and yes I can turn them into a matrix no problem. Indeed
I need only the results for ER+ETR_H1,PGA and ER+ETR_H2,Sa. One minor point as
it is the VC has 4 values for three cases instead of the aforementioned two. In
fact, the third is identical to the first. Could you please optimize?
Thank you very much again,
Best,
ioanna
-Original Message-
From: Jim Lemon [mailto:drjimle...@gmail.com]
Sent: Friday, December 20, 2019 9:04 PM
To: Ioannou, Ioanna
Cc: r-help mailing list
Subject: Re: [R] How to create a new data.frame based on calculation of subsets
of an existing data.frame
Hi Ioanna,
We're getting somewhere, but there are four unique combinations of Taxonomy
and IM.type:
ER+ETR_H1,PGA
ER+ETR_H2,PGA
ER+ETR_H1,Sa
ER+ETR_H2,Sa
Perhaps you mean that ER+ETR_H1 only occurs with PGA and ER+ETR_H2 only occurs
with Sa. I handled that by checking that there were any rows that corresponded
to the condition requested.
Also you want a matrix for each row containing Taxonomy and IM.type in the
output. When I run what I think you are asking, I only get a two element list,
each a vector of values. Maybe this is what you want, and it could be coerced
into matrix format:
D<- data.frame(Ref.No = c(1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629),
Region = rep(c('South America'), times = 8), IM.type = c('PGA', 'PGA', 'PGA',
'PGA', 'Sa', 'Sa', 'Sa', 'Sa'), Damage.state = c('DS1', 'DS2', 'DS3',
'DS4','DS1', 'DS2', 'DS3', 'DS4'), Taxonomy =
c('ER+ETR_H1','ER+ETR_H1','ER+ETR_H1','ER+ETR_H1','ER+ETR_H2','ER+ETR_H2','ER+ETR_H2','ER+ETR_H2'),
Prob.of.exceedance_1 = c(0,0,0,0,0,0,0,0),
Prob.of.exceedance_2 = c(0,0,0,0,0,0,0,0),
Prob.of.exceedance_3 =
c(0.26,0.001,0.00019,0.00573,0.04,0.00017,0.000215,0.000472),
Prob.of.exceedance_4 =
c(0.72,0.03,0.008,0.61,0.475,0.0007,0.00435,0.000405),
stringsAsFactors=FALSE)
# names of the variables used in the calculations
calc_vars<-paste("Prob.of.exceedance",1:4,sep="_")
# get the rows for the four damage states DS1_rows <-D$Damage.state == "DS1"
DS2_rows <-D$Damage.state == "DS2"
DS3_rows <-D$Damage.state == "DS3"
DS4_rows <-D$Damage.state == "DS4"
# create an empty list
VC<-list()
# set an index variable for VC
VCindex<-1
# step through all possible values of IM.type and Taxonomy for(IM in
unique(D$IM.type)) { for(Tax in unique(D$Taxonomy)) {
# get a logical vector of the rows to be used in this calculation
calc_rows <- D$IM.type == IM & D$Taxonomy == Tax
cat(IM,Tax,calc_rows,"\n")
# check that there are any such rows in the data frame
if(sum(calc_rows)) {
# if so, fill in the four values for these rows
VC[[VCindex]] <- 0.0 * (1- D[calc_rows & DS1_rows,calc_vars]) +
0.02* (D[calc_rows & DS1_rows,calc_vars] -
D[calc_rows & DS2_rows,calc_vars]) +
0.10* (D[calc_rows & DS2_rows,calc_vars] -
D[calc_rows & DS3_rows,calc_vars]) +
0.43 * (D[calc_rows & DS3_rows,calc_vars] -
D[calc_rows & DS4_rows,calc_vars]) +
1.0* D[calc_rows & DS4_rows,calc_vars]
# increment the index
VCindex<-VCindex+1
}
}
}
I think we'll get there.
Jim
On Sat, Dec 21, 2019 at 12:45 AM Ioannou, Ioanna
wrote:
>
> Hello Jim,
>
> I made some changes to the code essentially I substitute each 4 lines DS1-4
> with one. I estimate VC which in an ideal world should be a matrix with 4
> columns one for every exceedance_probability_1-4 and 2 rowsfor each unique
> combination of taxonomy and IM.Type. Coukd you please check the code I sent
> last and based on that give your solution?
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to create a new data.frame based on calculation of subsets of an existing data.frame
Hello Jim,
I made some changes to the code essentially I substitute each 4 lines DS1-4
with one. I estimate VC which in an ideal world should be a matrix with 4
columns one for every exceedance_probability_1-4 and 2 rowsfor each unique
combination of taxonomy and IM.Type. Coukd you please check the code I sent
last and based on that give your solution?
Many thanks.
Get Outlook for Android<https://aka.ms/ghei36>
From: Jim Lemon
Sent: Friday, December 20, 2019 11:40:28 AM
To: Ioannou, Ioanna
Cc: r-help mailing list
Subject: Re: [R] How to create a new data.frame based on calculation of subsets
of an existing data.frame
Hi Ioanna,
For simplicity assume that the new data frame will be named E:
E<-D[,c("Taxonomy","IM.type",paste("VC,1:4,sep="_"))]
While I haven't tested this, I'm pretty sure I have it correct. Just
extract the columns you want from D and assign that to E.
Jim
On Fri, Dec 20, 2019 at 9:02 PM Ioannou, Ioanna
wrote:
>
> Hello Jim,
>
> Thank you every so much it ws very helful. In fact what I want to calculate
> is the following. My very last question is if I want to save the outcome VC,
> IM.type and Taxonomy in a new data.frame how can I do it?
>
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to create a new data.frame based on calculation of subsets of an existing data.frame
Hello Jim,
Thank you every so much it ws very helful. In fact what I want to calculate is
the following. My very last question is if I want to save the outcome VC,
IM.type and Taxonomy in a new data.frame how can I do it?
# names of the variables used in the calculations
calc_vars<-paste("Prob.of.exceedance",1:4,sep="_")
# get the rows for the four damage states
DS1_rows <-D$Damage.state == "DS1"
DS2_rows <-D$Damage.state == "DS2"
DS3_rows <-D$Damage.state == "DS3"
DS4_rows <-D$Damage.state == "DS4"
# step through all possible values of IM.type and Taxonomy
for(IM in unique(D$IM.type)) { for(Tax in unique(D$Taxonomy)) {
# get a logical vector of the rows to be used in this calculation
calc_rows <- D$IM.type == IM & D$Taxonomy == Tax
cat(IM,Tax,calc_rows,"\n")
# check that there are any such rows in the data frame
if(sum(calc_rows)) {
# if so, fill in the four values for these rows
VC <- 0.0 * (1- D[calc_rows & DS1_rows,calc_vars]) +
0.02* (D[calc_rows & DS1_rows,calc_vars] -
D[calc_rows & DS2_rows,calc_vars]) +
0.10* (D[calc_rows & DS2_rows,calc_vars] -
D[calc_rows & DS3_rows,calc_vars]) +
0.43 * (D[calc_rows & DS3_rows,calc_vars] -
D[calc_rows & DS4_rows,calc_vars]) +
1.0* D[calc_rows & DS4_rows,calc_vars]
}
}
}
-----Original Message-
From: Jim Lemon [mailto:drjimle...@gmail.com]
Sent: Thursday, December 19, 2019 2:05 AM
To: Ioannou, Ioanna ; r-help mailing list
Subject: Re: [R] How to create a new data.frame based on calculation of subsets
of an existing data.frame
Hi Ioanna,
I looked at the problem this morning and tried to work out what you wanted.
With a problem like this, it is often easy when you have someone point to the
data and say "I want this added to that and this multiplied by that". I have
probably made the wrong guesses, but I hope that you can correct my guesses and
I can get the calculations correct for you. For example, I have assumed that
you want the sum of the IM_* values for each set of damage states as the values
for VC_1,
VC_2 etc.
D<-data.frame(Ref.No = c(1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629),
Region = rep(c('South America'), times = 8), IM.type = c('PGA', 'PGA', 'PGA',
'PGA', 'Sa', 'Sa', 'Sa', 'Sa'), Damage.state = c('DS1', 'DS2', 'DS3',
'DS4','DS1', 'DS2', 'DS3', 'DS4'), Taxonomy =
c('ER+ETR_H1','ER+ETR_H1','ER+ETR_H1','ER+ETR_H1','ER+ETR_H2',
'ER+ETR_H2','ER+ETR_H2','ER+ETR_H2'),
IM_1 = c(0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00),
IM_2 = c(0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08),
IM_3 = c(0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16),
IM_4 = c(0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24),
Prob.of.exceedance_1 = c(0,0,0,0,0,0,0,0),
Prob.of.exceedance_2 = c(0,0,0,0,0,0,0,0),
Prob.of.exceedance_3 =
c(0.26,0.001,0.00019,0.00573,0.04,0.00017,0.000215,0.000472),
Prob.of.exceedance_4 =
c(0.72,0.03,0.008,0.61,0.475,0.0007,0.00435,0.000405),
stringsAsFactors=FALSE)
# assume the above has been read in
# add the four columns to the data frame filled with NAs
D$VC_1<-D$VC_2<-D$VC_3<-D$VC_4<-NA
# names of the variables used in the calculations
calc_vars<-paste("Prob.of.exceedance",1:4,sep="_")
# get the rows for the four damage states DS1_rows<-D$Damage.state == "DS1"
DS2_rows<-D$Damage.state == "DS2"
DS3_rows<-D$Damage.state == "DS3"
DS4_rows<-D$Damage.state == "DS4"
# step through all possible values of IM.type and Taxonomy for(IM in
unique(D$IM.type)) { for(Tax in unique(D$Taxonomy)) {
# get a logical vector of the rows to be used in this calculation
calc_rows<-D$IM.type == IM & D$Taxonomy == Tax
cat(IM,Tax,calc_rows,"\n")
# check that there are any such rows in the data frame
if(sum(calc_rows)) {
# if so, fill in the four values for these rows
D$VC_1[calc_rows]<-sum(0.01 * (D[calc_rows & DS1_rows,calc_vars] -
D[calc_rows & DS2_rows,calc_vars]))
D$VC_2[calc_rows]<-sum(0.02 * (D[calc_rows & DS2_rows,calc_vars] -
D[calc_rows & DS3_rows,calc_vars]))
D$VC_3[calc_rows]<-sum(0.43 * (D[calc_rows & DS3_rows,calc_vars] -
D[calc_rows & DS4_rows,calc_vars]))
D$VC_4[calc_rows]<-sum(D[calc_rows & DS4_rows,calc_vars])
}
}
}
Jim
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[R] How to save output of multiple unique loops in R.
Hello everyone,
Could you please let me know how to create a new data.frame with the output of
the 2 unique loops. Essentially i want a data.frame with the IM, Taxonomy and
VC . MInd you VC is a vector with 33 elements.
Any ideas?
best,
ioanna
D<- data.frame(Ref.No = c(1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629),
Region = rep(c('South America'), times = 8),
IM.type = c('PGA', 'PGA', 'PGA', 'PGA', 'Sa', 'Sa', 'Sa', 'Sa'),
Damage.state = c('DS1', 'DS2', 'DS3', 'DS4','DS1', 'DS2', 'DS3',
'DS4'),
Taxonomy =
c('ER+ETR_H1','ER+ETR_H1','ER+ETR_H1','ER+ETR_H1','ER+ETR_H2','ER+ETR_H2','ER+ETR_H2','ER+ETR_H2'),
Prob.of.exceedance_1 = c(0,0,0,0,0,0,0,0),
Prob.of.exceedance_2 = c(0,0,0,0,0,0,0,0),
Prob.of.exceedance_3
=c(0.26,0.001,0.00019,0.00573,0.04,0.00017,0.000215,0.000472),
Prob.of.exceedance_4 =
c(0.72,0.03,0.008,0.61,0.475,0.0007,0.00435,0.000405),
stringsAsFactors=FALSE)
# names of the variables used in the calculations
calc_vars<-paste("Prob.of.exceedance",1:4,sep="_")
# get the rows for the four damage states
DS1_rows <-D$Damage.state == "DS1"
DS2_rows <-D$Damage.state == "DS2"
DS3_rows <-D$Damage.state == "DS3"
DS4_rows <-D$Damage.state == "DS4"
# step through all possible values of IM.type and Taxonomy
for(IM in unique(D$IM.type)) { for(Tax in unique(D$Taxonomy)) {
# get a logical vector of the rows to be used in this calculation
calc_rows <- D$IM.type == IM & D$Taxonomy == Tax
cat(IM,Tax,calc_rows,"\n")
# check that there are any such rows in the data frame
if(sum(calc_rows)) {
# if so, fill in the four values for these rows
VC <- 0.0 * (1- D[calc_rows & DS1_rows,calc_vars]) +
0.02* (D[calc_rows & DS1_rows,calc_vars] -
D[calc_rows & DS2_rows,calc_vars]) +
0.10* (D[calc_rows & DS2_rows,calc_vars] -
D[calc_rows & DS3_rows,calc_vars]) +
0.43 * (D[calc_rows & DS3_rows,calc_vars] -
D[calc_rows & DS4_rows,calc_vars]) +
1.0* D[calc_rows & DS4_rows,calc_vars]
}
}
}
[[alternative HTML version deleted]]
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and provide commented, minimal, self-contained, reproducible code.
[R] How to create a new data.frame based on calculation of subsets of an existing data.frame
Hello everyone,
I have the following problem: I have a data.frame with multiple fields.
If I had to do my calculations for a given combination of IM.type and Taxonomy
is the following:
D <- read.csv('Test_v2.csv')
names(D)
VC <- 0.01*( subset(D, IM.type == 'PGA' & Damage.state == 'DS1' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
0.02*( subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
0.43*( subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
1.0*( subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13])
So the question is how can I do that in an automated way for all possible
combinations and store the results in new data.frame which would look like
this:
Ref.No. Region IM.type TaxonomyIM_1IM_2IM_3IM_4VC_1
VC_2VC_3VC_4
1622South America PGA ER+ETR_H1 1.00E-060.080.16
0.24 3.49e-294 3.449819e-05 0.002748889 0.01122911
Best, ,
ioanna
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and provide commented, minimal, self-contained, reproducible code.
[R] FW: How to create a new data.frame based on calculation of subsets of an existing data.frame
Hello everyone,
I have the following problem: I have a data.frame with multiple fields.
If I had to do my calculations for a given combination of IM.type and
Taxonomy is the following:
D <- read.csv('Test_v2.csv')
names(D)
VC <- 0.01*( subset(D, IM.type == 'PGA' & Damage.state == 'DS1' & Taxonomy
== 'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy
== 'ER+ETR_H1')[10:13]) +
0.02*( subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy
== 'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy
== 'ER+ETR_H1')[10:13]) +
0.43*( subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
1.0*( subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13])
So the question is how can I do that in an automated way for all possible
combinations and store the results in new data.frame which would look like
this:
Ref.No. Region IM.type TaxonomyIM_1IM_2IM_3IM_4VC_1
VC_2VC_3VC_4
1622South America PGA ER+ETR_H1 1.00E-060.080.16
0.24 3.49e-294 3.449819e-05 0.002748889 0.01122911
Best, ,
ioanna
__
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to create a new data.frame based on calculation of subsets of an existing data.frame
Just i case you cant see the data:
Test.v2 <- data.frame(Ref.No = c(1622, 1623, 1624, 1625, 1626, 1627, 1628,
1629),
IM.type = c('PGA', 'PGA', 'PGA', 'PGA', 'Sa', 'Sa', 'Sa',
'Sa'),
Damage.state = c('DS1', 'DS2', 'DS3', 'DS4','DS1', 'DS2',
'DS3', 'DS4'),
Taxonomy =
c('ER+ETR_H1','ER+ETR_H1','ER+ETR_H1','ER+ETR_H1','ER+ETR_H2','ER+ETR_H2','ER+ETR_H2','ER+ETR_H2'),
IM_1 = c(0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00),
IM_2 = c(0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08),
IM_3 = c(0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16),
IM_1 = c(0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24),
Prob.of.exceedance_1 = c(0,0,0,0,0,0,0,0),
Prob.of.exceedance_2 = c(0,0,0,0,0,0,0,0),
Prob.of.exceedance_3 =
c(0.26,0.001,0.00019,0.00573,0.04,0.00017,0.000215,0.000472),
Prob.of.exceedance_4 =
c(0.72,0.03,0.008,0.61,0.475,0.0007,0.00435,0.000405)
)
From: R-help on behalf of Ioanna Ioannou
Sent: 17 December 2019 19:43
To: r-help@r-project.org
Subject: [R] FW: How to create a new data.frame based on calculation of subsets
of an existing data.frame
Hello everyone,
I have the following problem: I have a data.frame with multiple fields.
If I had to do my calculations for a given combination of IM.type and Taxonomy
is the following:
D <- read.csv('Test_v2.csv')
names(D)
VC <- 0.01*( subset(D, IM.type == 'PGA' & Damage.state == 'DS1' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
0.02*( subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
0.43*( subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
1.0*( subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13])
So the question is how can I do that in an automated way for all possible
combinations and store the results in new data.frame which would look like
this:
Ref.No. Region IM.type TaxonomyIM_1 IM_2 IM_3 IM_4 VC_1 VC_2
VC_3 VC_4
1622 South America PGA ER+ETR_H1 1.00E-06 0.08 0.16
0.24 3.49e-294 3.449819e-05 0.002748889 0.01122911
Thanks in advance,
Best, ,
ioanna
__
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[[alternative HTML version deleted]]
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and provide commented, minimal, self-contained, reproducible code.
[R] FW: How to create a new data.frame based on calculation of subsets of an existing data.frame
Hello everyone,
I have the following problem: I have a data.frame with multiple fields.
If I had to do my calculations for a given combination of IM.type and Taxonomy
is the following:
D <- read.csv('Test_v2.csv')
names(D)
VC <- 0.01*( subset(D, IM.type == 'PGA' & Damage.state == 'DS1' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
0.02*( subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
0.43*( subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
1.0*( subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13])
So the question is how can I do that in an automated way for all possible
combinations and store the results in new data.frame which would look like
this:
Ref.No. Region IM.type TaxonomyIM_1 IM_2 IM_3 IM_4 VC_1 VC_2
VC_3 VC_4
1622 South America PGA ER+ETR_H1 1.00E-06 0.08 0.16
0.24 3.49e-294 3.449819e-05 0.002748889 0.01122911
Thanks in advance,
Best, ,
ioanna
__
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and provide commented, minimal, self-contained, reproducible code.
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] How to create a new data.frame based on calculation of subsets of an existing data.frame
Hello everyone,
I have the following problem: I have a data.frame with multiple fields.
If I had to do my calculations for a given combination of IM.type and Taxonomy
is the following:
D <- read.csv('Test_v2.csv')
names(D)
VC <- 0.01*( subset(D, IM.type == 'PGA' & Damage.state == 'DS1' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
0.02*( subset(D, IM.type == 'PGA' & Damage.state == 'DS2' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
0.43*( subset(D, IM.type == 'PGA' & Damage.state == 'DS3' & Taxonomy ==
'ER+ETR_H1')[10:13] -
subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13]) +
1.0*( subset(D, IM.type == 'PGA' & Damage.state == 'DS4' & Taxonomy ==
'ER+ETR_H1')[10:13])
So the question is how can I do that in an automated way for all possible
combinations and store the results in new data.frame which would look like
this:
Ref.No. Region IM.type TaxonomyIM_1 IM_2 IM_3 IM_4 VC_1 VC_2
VC_3 VC_4
1622 South America PGA ER+ETR_H1 1.00E-06 0.08 0.16
0.24 3.49e-294 3.449819e-05 0.002748889 0.01122911
Thanks in advance,
Best, ,
ioanna
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Re: [R] Manipulation of data.frame into an array
Hello everyone, Thank you for this. Nonetheless it is not exactly want i need. I need mydata[[1]] to provide the values for all 3 variables (Y, X1 and X2) of the first imputation only. As it stands it returns the whole database. Any ideas? Best, ioanna From: Bert Gunter Sent: 24 May 2018 16:04 To: Ioanna Ioannou Cc: r-help@r-project.org Subject: Re: [R] Manipulation of data.frame into an array This is one of those instances where a less superficial knowledge of R's technical details comes in really handy. What you need to do is convert the data frame to a single (numeric) vector for, e.g. a matrix() call. This can be easily done by noting that a data frame is also a list and using do.call(): ## imp is the data frame: do.call(c,imp) X11 X12 X13 X14 X15 X16 X17 X18 X19 X110 X111 X112 X113 X114 12121212121212 X115 X116 X21 X22 X23 X24 X25 X26 X27 X28 X29 X210 X211 X212 12010111010101 X213 X214 X215 X216 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 11011234567812 Y11 Y12 Y13 Y14 Y15 Y16 345678 So, e.g. for a 3 column matrix: > matrix(do.call(c,imp), ncol=3) [,1] [,2] [,3] [1,]101 [2,]212 [3,]103 [4,]214 [5,]115 [6,]216 [7,]107 [8,]218 [9,]101 [10,]212 [11,]103 [12,]214 [13,]115 [14,]216 [15,]107 [16,]218 Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, May 24, 2018 at 7:46 AM, Ioanna Ioannou mailto:ii54...@msn.com>> wrote: Hello everyone, I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y. Lets assume a simple data.frame: Imputed = data.frame( X1 = c(1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2), X2 = c(0,1,0,1,1,1,0,1, 0,1,0,1,1,1,0,1), Y = c(1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8)) The first 8 have been obtained by the first imputation and the later 8 by the 2nd. Can you help me please? Best, ioanna [[alternative HTML version deleted]] __ R-help@r-project.org<mailto:R-help@r-project.org> mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Manipulation of data.frame into an array
Hello everyone, I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y. Lets assume a simple data.frame: Imputed = data.frame( X1 = c(1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2), X2 = c(0,1,0,1,1,1,0,1, 0,1,0,1,1,1,0,1), Y = c(1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8)) The first 8 have been obtained by the first imputation and the later 8 by the 2nd. Can you help me please? Best, ioanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Change values of a column based on the values of a third
Hello all,
I have a rather easy question. I want to add a column to the database which
will change the values of vector a based on the values to vector b. Any
ideas how?
For example:
Dat <- data.frame(a= c('A','A','C','B','D','D','B'),
b= c('N','N','Y','N','Y','N','N') )
I want to add a column c which will change 'C' to 'D' if column b is 'Y'.
> Dat
a b c
1 A N A
2 A N A
3 C Y D
4 B N B
5 C Y D
6 C N C
7 B N B
Any ideas?
Best,
ioanna
[[alternative HTML version deleted]]
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[R] FW: Completing Unordered Categorical missing variables using package mi
Hello all,
A perhaps simple question. I am trying to complete unordered categorical
missing data using mi package. There are two variables with missing data:
Mat and Use. The problem is that the Use has several categories and somehow
this means I can't plot the results as I get this error. Any idea how to fix
the problem?
Any help much appreciated,
Best,
Ioanna
new<-read(Sample.csv)
new$Use<-factor(new$Use)
MissingData <- missing_data.frame(new)
MissingData <- change(MissingData, y = "DS", what = "type", to =
"ordered-categorical")
# STEP 3: look deeper
summary(MissingData)
summary(MissingData@patterns)
show(MissingData)
hist(MissingData)
# STEP 4: impute
## Not run:
IMPsample <- mi(MissingData)
#STEP5: diagnostics
Plot(IMPsample)
Error in `rownames<-`(`*tmp*`, value = c("Oc11", "Oc12", "Oc13", "Oc14", :
length of 'dimnames' [1] not equal to array extent
Data
new
DS Use Material SurfaceIM
31237 3 Oc22 Wood 95.710401 3.148
48947 1 Oc19 Wood 124.427200 1.762
7038 5 Oc11 Wood 142.113800 1.890
8150 3 Oc32Steel 70.709451 2.530
43471 1 Oc19 NA3.609650 1.597
24815 5 Oc11 Wood 121.541500 3.638
8683 2 Oc11 Wood 80.341550 2.242
47303 1 Oc39 NA 19.018000 1.410
27467 5 Oc11 Wood 86.799050 3.782
35026 2 Oc11 Wood 113.185700 2.565
50635 5 Oc19 NA 28.169550 3.887
9459 2 Oc11 Wood 40.825350 1.779
14042 2 Oc13Steel 36.233100 4.293
17393 3 Oc11 Wood 56.069700 2.833
21157 5 Oc11 Wood 89.254700 4.958
42345 2 Oc19 NA2.973600 0.468
4372 5 Oc11 Wood7.872750 3.950
16654 5 Oc11 Wood 75.315600 6.395
47335 1 Oc19 NA 14.564400 1.781
49609 5 Oc19 NA 17.249000 3.545
4973 5 Oc19 Wood 28.511699 3.243
44784 2 Oc19 NA4.473000 2.328
29581 5 Oc11 Wood 96.884250 4.378
31949 3 Oc11 Wood 126.996500 3.225
7352 5 Oc11 Wood 71.905200 3.430
43139 1 Oc19 NA 133.573551 1.849
43350 2 Oc19 NA3.739350 1.205
11592 2 Oc11 Wood 72.146800 2.906
33767 3 Oc11 Wood 91.578001 2.905
51748 5 Oc39 NA9.108800 3.138
21160 5 Oc12 Wood 100.677100 4.268
34390 2 Oc12 Wood 120.401199 1.603
23255 5 Oc12 Wood 122.333801 5.557
38414 2 Oc21Steel 69.686100 3.021
48810 3 Oc29 Wood 79.609950 3.670
44611 2 Oc19 NA 15.328000 1.480
17905 3 Oc11 Wood 61.188500 1.857
35509 2 Oc11 Wood 160.180349 2.511
10252 2 Oc11 Wood 94.414799 1.446
47152 2 Oc19 Wood 12.160450 2.285
43221 2 Oc11 Wood 70.796299 1.361
32569 3 Oc11 Wood 97.269300 2.842
5671 5 Oc11 Wood 84.672250 3.050
1157 3 Oc11 Wood 79.297800 2.612
3441 2 Oc11 Wood 112.435650 2.105
36678 5 Oc21 Wood 27.223500 4.017
52241 5 Oc19 Wood3.946150 3.373
4688 5 Oc11 Wood 68.009700 3.766
42933 2 Oc19 Wood3.946801 2.039
31048 5 Oc11 Wood 25.172301 3.633
28660 1 Oc11 Wood 133.387099 1.285
22726 5 Oc12 Wood 216.952900 7.550
22397 5 Oc11 Wood 115.320750 5.825
41008 3 Oc11 Wood 97.253199 1.960
49054 3 Oc21 NA8.542800 1.329
5594 5 Oc41 RC 264.505000 3.185
45379 3 Oc41 Wood 39.357100 2.909
17498 3 Oc11 Wood 45.544750 1.459
1176 5 Oc11 Wood 87.020400 2.750
33055 1 Oc11 Wood 55.777250 1.437
37071 5 Oc32 Wood 201.629599 3.287
53813 5 Oc19 Wood 56.919600 4.322
11037 2 Oc11 Wood 107.886600 1.479
14453 2 Oc11 Wood 106.369949 2.508
3767 1 Oc11 Wood 71.325500 2.012
52303 5 Oc29 NA8.916150 6.092
19706 5 Oc19 Wood 25.936699 6.417
37658 5 Oc14 Wood 65.761651 4.317
26195 1 Oc12 Wood 82.510849 0.835
35808 3 Oc13 Wood 54.798851 2.337
6035 5 Oc12 Wood 286.075700 3.905
33383 3 Oc11 Wood 96.809150 2.560
43497 1 Oc19 NA 13.039000 1.103
41777 5 NA NA 47.153349 2.658
12024 2 Oc11 Wood 19.851000 2.567
39538 1 Oc19 NA 31.996200 2.108
15553 3 Oc19 Wood 197.062201 2.559
31522 3 Oc11 Wood 129.499700 2.906
11916 2 Oc11 Wood 58.358951 2.939
9688 2 Oc11 Wood 106.568201 1.822
1690 3 Oc11 Wood 202.613700 2.290
9773 Oc11 Wood 179.321800 1.987
12410 2 Oc14Steel 108.682100 2.821
52428 5 Oc19 NA 36.041699 6.042
14109 2 Oc21Steel 130.929300 4.178
52769 5 Oc31 Wood 18.525650 2.187
11324 1 Oc11 Wood 94.108351 1.456
12394 2 Oc21 RC 1836.975800 2.415
35991 3 Oc11 Wood 114.716550 2.664
4006 5 Oc39Steel 309.854000 4.041
43404 2 Oc19 RC 27.745400 1.772
12680 1 Oc21 RC 327.789699 2.669
43607 1 Oc29 NA5.812499 2.406
335
[R] Completing Unordered Categorical missing variables using package mi
Hello all,
A perhaps simple question. I am trying to complete unordered categorical
missing data using mi package. There are two variables with missing data:
Mat and Use. The problem is that the Use has several categories and somehow
this means I can't plot the results as I get this error. Any idea how to fix
the problem?
Any help much appreciated,
Best,
Ioanna
new<-read(Sample.csv)
new$Use<-factor(new$Use)
MissingData <- missing_data.frame(new)
MissingData <- change(MissingData, y = "DS", what = "type", to =
"ordered-categorical")
# STEP 3: look deeper
summary(MissingData)
summary(MissingData@patterns)
show(MissingData)
hist(MissingData)
# STEP 4: impute
## Not run:
IMPsample <- mi(MissingData)
#STEP5: diagnostics
Plot(IMPsample)
Error in `rownames<-`(`*tmp*`, value = c("Oc11", "Oc12", "Oc13", "Oc14", :
length of 'dimnames' [1] not equal to array extent
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] gaussian Kernel smoothing using Nadaraya-Watson estimator and confidence bands
Hello, I have a database and I would like to fit a Nadaraya-Watson Gaussian kernel estimator and produce the confidence bands around the mean estimate. Any ideas how to do this with R? I cannot find a way to produce the confidence bands. I will use a fixed bandwidth. Lets use this database for illustration purposes: d <- data.frame(x = runif(N)) d$y <- d$x^2 - d$x + 1 + (1+d$x)*rnorm(N, sd = 0.1) Any help much appreciated. Best, Ioanna __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] FW: FW: confidence intervals values in locpol
Hello all, A straightforward question. How can I get a the values of the 90% confidence intervals of a locpol in R? I can see how you can plot the mean as well as the confidence intervals. I would like the matrix of the values corresponding to the 95% and 5% exceedance probability. Any ideas? For example N <- 250 xeval <- 0:100/100 ## ex1 d <- data.frame(x = runif(N)) d$y <- d$x^2 - d$x + 1 + rnorm(N, sd = 0.1) r <- locpol(y~x,d) plot(r) Best, Ioanna - E-Mail: (Ted Harding) Date: 03-Mar-2015 Time: 23:28:20 This message was sent by XFMail __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Legend having lines with different types
Hello all, I want to plot the legend for the following two lines: I have two lines: X1<-c(0,1,2,3,4) Y1<-c(0,1,2,3,4) Y2<-c(5,6,7,8,9) Y3<-(32,33,34,35,36) plot(X1,Y3,pch=20) lines(X1,Y1,lty=1,type='o') lines(X1,Y2,lty=1,type='b') lines(X1,Y3,lty=2) Any ideas how? Best IOanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] LInes with types
Hello all, I want to plot the legend for the following two lines: I have two lines: X1<-c(0,1,2,3,4) Y1<-c(0,1,2,3,4) Y2<-c(5,6,7,8,9) Y3<-(32,33,34,35,36) plot(X1,Y3,pch=20) lines(X1,Y1,lty=1,type='o') lines(X1,Y2,lty=1,type='b') lines(X1,Y3,lty=2) Any ideas how? Best IOanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] grf in geoR
Hello all, A simple question. When I use grf from the package 'geoR' , I adopt the exponential model. For this model is the parameter phi in m or km? Best ioanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] grf in geoR
Hello all, A simple question. When I use grf from the package 'geoR' , I adopt the exponential model. For this model is the parameter range in m or km? Best ioanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Data manipulation in a data.frame
Thank you very much. One further question. Assuming that for some points there is no classification for example: A<-data.frame(A=c(10,100,1000,30,50,60,300,3), B=c(0,1,1,1,0,0,0,0), C=c(0,0,0,0,1,1,0,0), D=c(1,0,0,0,0,0,1,0)) Is there an easy way to introduce an extra none option in the variable? A<-data.frame(A=c(10,100,1000,30,50,60,300,3), B=c(0,1,1,1,0,0,0,0), C=c(0,0,0,0,1,1,0,0), D=c(1,0,0,0,0,0,1,0), Variable=c(D,B,B,B,C,C,D,none)) Thanks in advance, IOanna -Original Message- From: arun [mailto:smartpink...@yahoo.com] Sent: 21 February 2014 00:19 To: r-help@r-project.org Cc: ioanna ioannou Subject: Re: [R] Data manipulation in a data.frame Also, rownames(which(t(!!A[,-1]),arr.ind=TRUE)) A.K. On Thursday, February 20, 2014 6:48 PM, arun wrote: Hi, May be this helps: A$Variable <- rep(colnames(A[,-1]),nrow(A))[t(!!A[,-1])] A.K. On Thursday, February 20, 2014 5:55 PM, ioanna ioannou wrote: Hello, Assuming that I have a data frame A<-data.frame(A=c(10,100,1000,30,50,60,300), B=c(0,1,1,1,0,0,0), C=c(0,0,0,0,1,1,0), D=c(1,0,0,0,0,0,1)) What I would like is to introduce a new column Variable such that: A<-data.frame(A=c(10,100,1000,30,50,60,300), B=c(0,1,1,1,0,0,0), C=c(0,0,0,0,1,1,0), D=c(1,0,0,0,0,0,1), Variable=c(D,B,B,B,C,C,D)) How can I do it? Best IOanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Data manipulation in a data.frame
Hello, Assuming that I have a data frame A<-data.frame(A=c(10,100,1000,30,50,60,300), B=c(0,1,1,1,0,0,0), C=c(0,0,0,0,1,1,0), D=c(1,0,0,0,0,0,1)) What I would like is to introduce a new column Variable such that: A<-data.frame(A=c(10,100,1000,30,50,60,300), B=c(0,1,1,1,0,0,0), C=c(0,0,0,0,1,1,0), D=c(1,0,0,0,0,0,1), Variable=c(D,B,B,B,C,C,D)) How can I do it? Best IOanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Aggregating spatial data
Fantastic. Thanks very much! Is there an easy way to plot the points and
the 4 areas?
Best,
Ioanna
-Original Message-
From: David Carlson [mailto:dcarl...@tamu.edu]
Sent: 25 November 2013 15:21
To: 'IOANNA'; r-help@r-project.org
Subject: RE: [R] Aggregating spatial data
Something like this?
> s <- expand.grid(x=seq(1,100,by=1),y=seq(1,100,by=1))
> w <-
data.frame(x=s$x,y=s$y,z1=rep(c(1,2,3,4),times=length(s$x/4)),
+ z2=seq(1,length(s$x),by=1))
> w$EW <- cut(w$x, breaks=c(.5, 50.5, 100.5), labels=c("West",
"East"))
> w$NS <- cut(w$y, breaks=c(.5, 50.5, 100.5), labels=c("South",
"North"))
> aggregate(z1~EW+NS, w, table)
EWNS z1.1 z1.2 z1.3 z1.4
1 West South 2600 2600 2400 2400
2 East South 2400 2400 2600 2600
3 West North 2600 2600 2400 2400
4 East North 2400 2400 2600 2600
> table(w$z1)
1 2 3 4
1 1 1 1
> aggregate(z2~EW+NS, w, mean)
EWNS z2
1 West South 2475.5
2 East South 2525.5
3 West North 7475.5
4 East North 7525.5
-
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
-Original Message-
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of IOANNA
Sent: Monday, November 25, 2013 8:46 AM
To: r-help@r-project.org
Subject: [R] Aggregating spatial data
Importance: High
Hello all,
I have a data frame in the form:
s<-expand.grid(x=seq(1,100,by=1),y=seq(1,100,by=1))
w<-data.frame(x=s$x,y=s$y,z1=rep(c(1,2,3,4),times=length(s$x/4))
,z2=seq(1,le
ngth(s$x),by=1))
The w$x and w$y represent the location of points and z1 and z2 attributes
corresponding to these points.
My question is how to divide this area in 4 sub-areas of equal points each
and produce the counts of z1= '1', '2' , '3' in each quarter as well as mean
values of z2 for each quarter.
Best,
Ioanna
[[alternative HTML version deleted]]
__
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https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Aggregating spatial data
Hello all, I have a data frame in the form: s<-expand.grid(x=seq(1,100,by=1),y=seq(1,100,by=1)) w<-data.frame(x=s$x,y=s$y,z1=rep(c(1,2,3,4),times=length(s$x/4)),z2=seq(1,le ngth(s$x),by=1)) The w$x and w$y represent the location of points and z1 and z2 attributes corresponding to these points. My question is how to divide this area in 4 sub-areas of equal points each and produce the counts of z1= '1', '2' , '3' in each quarter as well as mean values of z2 for each quarter. Best, Ioanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Error in grf using geoR
Hello all, I cant find a thread with a problem similar to mine. I am trying to create a random fields and I get the same error: library(geoR) N<-10 nslon<-250 nslat<-250 range<-10 sim2 <- grf(nslon*nslat, grid="reg", nx=nslon, ny=nslat,cov.pars=c(1, range), nsim=N, cov.model = "exponential", xlims=c(30.02,35.00),ylims=c(30.02,35.00) ) Error in FUN(X[[1L]], ...) : different grid distances detected, but the grid must have equal distances in each direction -- try gridtriple=TRUE that avoids numerical errors. I can't understand why as the code seems to run if I change the limits of the grid: sim2 <- grf(nslon*nslat, grid="reg", nx=nslon, ny=nslat,cov.pars=c(1, range), nsim=N, cov.model = "exponential", xlims=c(0.02,5.00),ylims=c(0.02,5.00) ) Any ideas? Best Ioanna __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Identifying the bin where a value is included.
Hello all,
A very simple problem.
Lets assume I have an interval [0,1] and I split it in 6 bins having
thresholds:
pro= cbind(0, 0.3675509, 0.8618615, 0.9814291, 0.9975283, 0.9997789,
1.000,
0, 0.3662881, 0.8609743, 0.9812032, 0.9974822, 0.9997738,
1.000)
dim(pro)<-c(7,2)
I randomly generate a number and I want to identify which bin it belongs
to. How? What I provide below doesn't seem to be working.
Any ideas?
for (i in 1:2){
ids<-runif(1)
for (j in 1:length(pro[,i])-1){
if (ids < pro[j,i]) {
ds[i]<-j
}
else {
ds[i]<-6
}
}
}
Best,
IOanna
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and provide commented, minimal, self-contained, reproducible code.
[R] FW: Kernel smoothing with bandwidth which varies with x
Hello all, I would like to use the Nadaraya-Watson estimator assuming a Gaussian kernel: So far I sued the library(sm) library(sm) x<-runif(5000) y<-rnorm(5000) plot(x,y,col='black') h1<-h.select(x,y,method='aicc') lines(ksmooth(x,y,bandwidth=h1)) which works fine. What if my data were clustered requiring a bandwidth that varies with x? How can I do that? Thanks in advance, Ioanna __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Reshaping a table
Hello all, I have data in the form of a table: X Y1Y2 0.1 3 2 0.2 2 1 And I would like to transform in the form: X Y 0.1 Y1 0.1 Y1 0.1 Y1 0.1 Y2 0.1 Y2 0.2 Y1 0.2 Y1 0.2 Y2 Any ideas how? Thanks in advance, IOanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Data manipulation
Hello John,
I thought I attached the file. So here we go:
Class=c(1,1,1,1,1,1,1,2,2,2,2,2,2,2,3,3,
3,3,3,3,3,3,3)
X=c(0.1,0.1,0.1,0.1,0.2,0.2,0.2,0.1,0.1,
0.1,0.1,0.1,0.1,0.1,0.1,0.2,0.2,0.2,0.2,0.3,0.3,0.3,0.3)
Count=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
by1<-factor(Class)
by2<-factor(X)
W<-aggregate(x=Count,by=list(by1,by2),FUN="sum")
However, what I want is a table that also include lines for the Group.1 and
Group.2 values for which there are no records. In other words something like
this:
Thanks again. I hope its clearer now.
Ioanna
-Original Message-
From: John Kane [mailto:jrkrid...@inbox.com]
Sent: 15 March 2013 12:51
To: IOANNA; r-help@r-project.org
Subject: RE: [R] Data manipulation
What zero values? And are they acutall zeros or are the NA's, that is,
missing values?
The code looks okay but without some sample data it is difficult to know
exactly what you are doing.
The easiest way to supply data is to use the dput() function. Example with
your file named "testfile":
dput(testfile)
Then copy the output and paste into your email. For large data sets, you
can just supply a representative sample. Usually,
dput(head(testfile, 100)) will be sufficient.
http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducibl
e-example
Please supply some sample data.
John Kane
Kingston ON Canada
> -Original Message-
> From: ii54...@msn.com
> Sent: Fri, 15 Mar 2013 12:40:54 +
> To: r-help@r-project.org
> Subject: [R] Data manipulation
>
> Hello all,
>
>
>
> I would appreciate your thoughts on a seemingly simple problem. I have
> a database, where each row represent a single record. I want to
> aggregate this database so I use the aggregate command :
>
>
>
> D<-read.csv("C:\\Users\\test.csv")
>
>
>
> attach(D)
>
>
>
> by1<-factor(Class)
>
> by2<-factor(X)
>
> W<-aggregate(x=Count,by=list(by1,by2),FUN="sum")
>
>
>
> The results I get following the form:
>
>
>
> >W
>
> Group.1 Group.2 x
>
> 1 1 0.1 4
>
> 2 2 0.1 7
>
> 3 3 0.1 1
>
> 4 1 0.2 3
>
> 5 3 0.2 4
>
> 6 3 0.3 4
>
>
>
>
>
> However, what I really want is an aggregation which includes the zero
> values, i.e.:
>
>
>
> >W
>
> Group.1 Group.2 x
>
> 1 1 0.1 4
>
> 2 2 0.1 7
>
> 3 3 0.1 1
>
> 4 1 0.2 3
>
> 2 0.2 0
>
> 5 3 0.2 4
>
> 10.3 0
>
> 20.3 0
>
> 6 3 0.3 4
>
>
>
>
>
> How can I achieve what I want?
>
>
>
> Best regards,
>
> Ioanna
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
family!
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Re: [R] Data manipulation
Thanks a lot!
-Original Message-
From: John Kane [mailto:jrkrid...@inbox.com]
Sent: 15 March 2013 13:41
To: Blaser Nello; IOANNA; r-help@r-project.org
Subject: Re: [R] Data manipulation
Nice. That does look like it. IOANNA?
John Kane
Kingston ON Canada
> -Original Message-
> From: nbla...@ispm.unibe.ch
> Sent: Fri, 15 Mar 2013 14:27:03 +0100
> To: ii54...@msn.com, r-help@r-project.org
> Subject: Re: [R] Data manipulation
>
> Is this what you want to do?
>
> D2 <- expand.grid(Class=unique(D$Class), X=unique(D$X))
> D2 <- merge(D2, D, all=TRUE)
> D2$Count[is.na(D2$Count)] <- 0
>
> W <- aggregate(D2$Count, list(D2$Class, D2$X), "sum") W
>
> Best,
> Nello
>
>
> -Original Message-
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org]
> On Behalf Of IOANNA
> Sent: Freitag, 15. März 2013 13:41
> To: r-help@r-project.org
> Subject: [R] Data manipulation
>
> Hello all,
>
>
>
> I would appreciate your thoughts on a seemingly simple problem. I have
> a database, where each row represent a single record. I want to
> aggregate this database so I use the aggregate command :
>
>
>
> D<-read.csv("C:\\Users\\test.csv")
>
>
>
> attach(D)
>
>
>
> by1<-factor(Class)
>
> by2<-factor(X)
>
> W<-aggregate(x=Count,by=list(by1,by2),FUN="sum")
>
>
>
> The results I get following the form:
>
>
>
> >W
>
> Group.1 Group.2 x
>
> 1 1 0.1 4
>
> 2 2 0.1 7
>
> 3 3 0.1 1
>
> 4 1 0.2 3
>
> 5 3 0.2 4
>
> 6 3 0.3 4
>
>
>
>
>
> However, what I really want is an aggregation which includes the zero
> values, i.e.:
>
>
>
> >W
>
> Group.1 Group.2 x
>
> 1 1 0.1 4
>
> 2 2 0.1 7
>
> 3 3 0.1 1
>
> 4 1 0.2 3
>
> 2 0.2 0
>
> 5 3 0.2 4
>
> 10.3 0
>
> 20.3 0
>
> 6 3 0.3 4
>
>
>
>
>
> How can I achieve what I want?
>
>
>
> Best regards,
>
> Ioanna
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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and provide commented, minimal, self-contained, reproducible code.
[R] Data manipulation
Hello all,
I would appreciate your thoughts on a seemingly simple problem. I have a
database, where each row represent a single record. I want to aggregate this
database so I use the aggregate command :
D<-read.csv("C:\\Users\\test.csv")
attach(D)
by1<-factor(Class)
by2<-factor(X)
W<-aggregate(x=Count,by=list(by1,by2),FUN="sum")
The results I get following the form:
>W
Group.1 Group.2 x
1 1 0.1 4
2 2 0.1 7
3 3 0.1 1
4 1 0.2 3
5 3 0.2 4
6 3 0.3 4
However, what I really want is an aggregation which includes the zero
values, i.e.:
>W
Group.1 Group.2 x
1 1 0.1 4
2 2 0.1 7
3 3 0.1 1
4 1 0.2 3
2 0.2 0
5 3 0.2 4
10.3 0
20.3 0
6 3 0.3 4
How can I achieve what I want?
Best regards,
Ioanna
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Issue with using geocode
Hello,
A very simple question but I am stuck. I have an excel file each row is an
address. However, I cannot make geocode read each line and come up with the
latitude longitude. Could you please correct my code?
library(ggmap)
X<-c (2 Afxentiou Ampelokipi Thessaloniki Greece, 2 Afxentiou Ampelokipi
Thessaloniki Greece, 4 Afxentiou Ampelokipi Thessaloniki Greece, 55
Agathonos Ampelokipi Thessaloniki Greece)
For (i in 1:4){
Y<-geocode('X')
print Y[i]
}
Best wishes,
Ioanna
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and provide commented, minimal, self-contained, reproducible code.
[R] LOCFIT
Hello, I am using the locfit to fit a non parametric glm model to data with a gamma distributed response variable. In the parametric glm regression the diagnostics were based on the study of the standardized deviance or pearson residuals. How can I estimate the the standardized Pearson residuals for the nonparametric model? Thanks, Ioanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Error: (subscript) logical subscript too long
Hello,
I am trying to perform a logistic regression using counts. For example:
cedegren <-
read.table("http://www.cloudstat.org/index.php?do=/attachment/download/id_95
/", header=T)
attach(cedegren)
ced.del <- cbind(sDel, sNoDel)
ced.logr <- glm(ced.del ~ cat + follows + factor(class),
family=binomial("logit"))
This works. However, if I change the family to Gaussian:
ced.logr <- glm(ced.del ~ cat + follows + factor(class), family=gaussian)
I get the error:
Error: (subscript) logical subscript too long
I would like to use the probit function. Is this possible?
Best regards,
Ioanna
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[R] Call the Standard Error and t-test probability in linear regression
Hello, I run a linear regression I get the summary, e.g.: > summary(lm.r) Call: lm(formula = signal ~ conc) Residuals: 12 3 4 5 0.4 -1.0 1.6 -1.80.8 Coefficients: Estimate Std. Errort valuePr(>|t|) (Intercept) 3.61.232882.92 0.0615 . conc 1.94000 0.05033 38.54 3.84e-05 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.592 on 3 degrees of freedom Multiple R-Squared: 0.998, Adjusted R-squared: 0.9973 F-statistic: 1486 on 1 and 3 DF, p-value: 3.842e-0 I would like to call the probability of the t-test only in order to use it separately. For example I 'd like to get: Pr<-3.84e-05 Similarly I want to call the standard error of the parameters and the function: SEconc<-0.05033 I don't know how to do this. Any help? Regards, Ioanna __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] plotting confidence bands from predict.nls
It is not homework. My problem is this: I used nonlinear regression to fit a lognormal cumulative distribution to the probability of a buildings' collapse against a measure of seismic intensity. I am able to obtain the asymptotic confidence intervals. I am not , however, certain that they are accurate and I wonder whether a bootstrap or even a Monte carlo could improve their accuracy. You see I want a realistic represntation of the confidence intervals as I need to propagate it further in the risk assessment. Perhaps I m wrong. Could you please enlighten me? Yanna Date: Sun, 5 Feb 2012 03:42:05 -0800 From: ml-node+s789695n4358800...@n4.nabble.com To: ii54...@msn.com Subject: Re: plotting confidence bands from predict.nls On 05/02/2012 08:10, ioanna wrote: > How do you use bootstrap to estimate the confidence as well as the prediction > intervals in nonlinear regression ? With difficulty! There is far too little here to go on, and this seems an odd question unless it is homework (why dictate a problem-strewn method of solution if this is a real problem?) The real issue is how to bootstrap nonlinear regression, and you will find that discussed in all good books on the subject, such as Venables & Ripley and Davison & Hinkley. It is not trivial and the solutions are not altogether satisfactory > -- > View this message in context: > http://r.789695.n4.nabble.com/plotting-confidence-bands-from-predict-nls-tp3505012p4358572.html > Sent from the R help mailing list archive at Nabble.com. > > __ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Brian D. Ripley, [hidden email] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. If you reply to this email, your message will be added to the discussion below: http://r.789695.n4.nabble.com/plotting-confidence-bands-from-predict-nls-tp3505012p4358800.html To unsubscribe from plotting confidence bands from predict.nls, click here. NAML -- View this message in context: http://r.789695.n4.nabble.com/plotting-confidence-bands-from-predict-nls-tp3505012p4358822.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] plotting confidence bands from predict.nls
How do you use bootstrap to estimate the confidence as well as the prediction intervals in nonlinear regression ? -- View this message in context: http://r.789695.n4.nabble.com/plotting-confidence-bands-from-predict-nls-tp3505012p4358572.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] R: sample size package
Hello, Lets assume I have an ordinal response variable representing the D<-c(D0,D1,D2,D3,D4) where D0 is no damage and D4 is collapse which I want to correlate with a continuous predictor variable, wind speed at the location of each building. is there a function in R which I can use to estimate the sample size of buildings with a given power if I want to perform an ordinal logistic regression? Is this sample the same if I want to use kernel smoothing? Is this possible to estimate the sample size in D0,.,D4? Collapse is rather rare. Regards, Yanna [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] kernel smoothing of disease rates at locations
Is it possible to apply a kernel smoothing regression whose estimator or indeed the confidence intervals cannot take negative values or values greater than 1? Best regards, Ioanna -- View this message in context: http://r.789695.n4.nabble.com/kernel-smoothing-of-disease-rates-at-locations-tp799701p4352286.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
