The transpose operator does not seem to work when used on a symbolic matrix
created using Ryacas.
> library(Ryacas)
Loading required package: XML
>
> u=Sym("u")
> v=Sym("v")
> w=Sym("w")
> DG=List(List(w-v), List(u-w), List(v-u))
> PrettyForm(DG)
[1] "Starting Yacas!"
Accepting requests from port 9734
/ \
| ( w - v ) |
| |
| ( u - w ) |
| |
| ( v - u ) |
\ /
> DGT=t(DG)
> PrettyForm(DGT)
/ \
| ( w - v ) |
| |
| ( u - w ) |
| |
| ( v - u ) |
\ /
> DG
{{w-v},{u-w},{v-u}};
> DGT
{{w-v},{u-w},{v-u}};
>
I was expecting DGT to be a 1 x 3 row vector.
Ryacas does not provide it. Try this:
t.Sym <- function(x) Sym("Transpose(", x, ")")
PrettyForm(t(DG))
Related
I want to add rows at specific place to expss output etable. I did that with some brute force method which always add the row at the start of etable. Any method to add rows at specific place.
library(tidyverse)
library(expss)
test1 <-
mtcars %>%
tab_cells(cyl) %>%
tab_cols(vs) %>%
tab_stat_cpct() %>%
tab_pivot()
test1 %>%
tibble() %>%
tibble::add_row(.data = tibble("", test1[2, -1]/test1[1, -1]*100) %>%
set_names(names(test1))
, .before = 3)
Not sure there's a simple method exported with expss, but we can use expss::add_rows() with a simple custom function to split a table to accomplish this.
insert_row <- function(tbl, where, ...) {
args <- c(...)
tbl1 <- tbl[1:where,]
tbl2 <- tbl[(where+1):nrow(tbl),]
tbl1 %>%
add_rows(args) %>%
add_rows(tbl2)
}
insert_row(test1, 2, c("cyl|4", 300, 40))
| | | vs | |
| | | 0 | 1 |
| --- | ------------ | ---------------- | ---------------- |
| cyl | 4 | 5.55555555555556 | 71.4285714285714 |
| | 6 | 16.6666666666667 | 28.5714285714286 |
| | 4 | 300 | 40 |
| | 8 | 77.7777777777778 | |
| | #Total cases | 18 | 14 |
Solution based on #caldwellst code but with automatic ratio calculation:
insert_ratio <- function(tbl, where) {
if(is.character(where)) {
# if where is character we search it in the rowlabels
where = grep(where, tbl[[1]], fixed = TRUE)[1]
}
isTRUE(where>1) || stop("'where' should be greater than 1 for ratio calculation.")
isTRUE(where<=NROW(tbl)) || stop("'where' should be less or equal than number of rows in the table.")
tbl1 <- tbl[1:where,]
to_insert = c(row_labels = tbl[[1]][where], tbl[where, -1]/tbl[where - 1, -1]*100)
tbl2 <- tbl[(where+1):nrow(tbl),]
tbl1 %>%
add_rows(to_insert) %>%
add_rows(tbl2)
}
insert_ratio(test1, 2)
# | | | vs | |
# | | | 0 | 1 |
# | --- | ------------ | ----- | ---- |
# | cyl | 4 | 5.6 | 71.4 |
# | | 6 | 16.7 | 28.6 |
# | | | 300.0 | 40.0 |
# | | 8 | 77.8 | |
# | | #Total cases | 18.0 | 14.0 |
insert_ratio(test1, "cyl|6")
# the same result
UPDATE
Ratio calculation is moved to separate function:
ratio = function(tbl, where, label = NULL){
if(is.character(where)) {
# if where is character we search it in the rowlabels
where = grep(where, tbl[[1]], fixed = TRUE)[1]
}
isTRUE(where>1) || stop("'where' should be greater than 1 for ratio calculation.")
isTRUE(where<=NROW(tbl)) || stop("'where' should be less or equal than number of rows in the table.")
if(is.null(label)) label = tbl[[1]][where]
c(row_labels = label, tbl[where, -1]/tbl[where - 1, -1]*100)
}
insert_row = function(tbl, where, row) {
if(is.character(where)) {
# if where is character we search it in the rowlabels
where = grep(where, tbl[[1]], fixed = TRUE)[1]
}
isTRUE(where<=NROW(tbl)) || stop("'where' should be less or equal than number of rows in the table.")
first_part = seq_len(where)
tbl1 <- tbl[first_part,]
tbl2 <- tbl[-first_part,]
tbl1 %>%
add_rows(row) %>%
add_rows(tbl2)
}
insert_row(test1, 2, ratio(test1, 2))
insert_row(test1, "cyl|6", ratio(test1, "cyl|6"))
I'm trying to adapt the following snippet:
echo '{"a":{"value":"b"}, "c":{"value":"d"}}' \
| jq -r '. as $in | keys[] | [$in[.].value | tostring + " 1"] | #tsv'
b 1
d 1
to output:
b 1
b 2
d 1
d 2
The following adaptation produces the desired output:
echo '{"a":{"value":"b"}, "c":{"value":"d"}}' |
jq -r '
def addindex(start;lessthan):
range(start;lessthan) as $i | "\(.) \($i)";
. as $in
| keys[]
| $in[.].value
| addindex(1;3)'
Note that keys emits the key names after they have been sorted, whereas keys_unsorted retains the ordering.
I'm using a double apply function to get a list of p-values for cor.test between any two columns of two tables.
hel_plist<-apply(bc, 2, function(x) { apply(otud, 2, function(y) { if (cor.test(x,y,method="spearman", exact=FALSE)$p.value<0.05){cor.test(x,y,method="spearman", exact=FALSE)$p.value}}) })
The otud data.frame is 90X11 (90rows,11 colums or to say dim(otud) 90 11) and will be used with different data.frames.
bc and hel - are both 90X2 data.frame-s - so for both I get 2*11=22 p-values out of functions
bc_plist<-apply(bc, 2, function(x) { apply(otud, 2, function(y) { if (cor.test(x,y,method="spearman", exact=FALSE)$p.value<0.05){cor.test(x,y,method="spearman", exact=FALSE)$p.value}}) })
hel_plist<-apply(hel, 2, function(x) { apply(otud, 2, function(y) { if (cor.test(x,y,method="spearman", exact=FALSE)$p.value<0.05){cor.test(x,y,method="spearman", exact=FALSE)$p.value}}) })
For bc I will have an output with dim=NULL a list of elements of otunames$bcnames$ p-value (a format that I have always got from these scripts and are happy with)
But for hel I will get and output of dim(hel) 11 2 - an 11X2 table with p-values written inside.
Shortened examples of output.
hel_plist
+--------+--------------+--------------+
| | axis1 | axis2 |
+--------+--------------+--------------+
| Otu037 | 1.126362e-18 | 0.01158251 |
| Otu005 | 3.017458e-2 | NULL |
| Otu068 | 0.00476002 | NULL |
| Otu070 | 1.27646e-15 | 5.252419e-07 |
+--------+--------------+--------------+
bc_plist
$axis1
$axis1$Otu037
[1] 1.247717e-06
$axis1$Otu005
[1] 1.990313e-05
$axis1$Otu068
[1] 5.664597e-07
Why is it like that when the input formats are all the same? (Shortened examples)
bc
+-------+-----------+-----------+
| group | axis1 | axis2 |
+-------+-----------+-----------+
| 1B041 | 0.125219 | 0.246319 |
| 1B060 | -0.022412 | -0.030227 |
| 1B197 | -0.088005 | -0.305351 |
| 1B222 | -0.119624 | -0.144123 |
| 1B227 | -0.148946 | -0.061741 |
+-------+-----------+-----------+
hel
+-------+---------------+---------------+
| group | axis1 | axis2 |
+-------+---------------+---------------+
| 1B041 | -0.0667782322 | -0.1660606406 |
| 1B060 | 0.0214470932 | -0.0611351008 |
| 1B197 | 0.1761876858 | 0.0927570627 |
| 1B222 | 0.0681058251 | 0.0549292399 |
| 1B227 | 0.0516864361 | 0.0774155225 |
| 1B235 | 0.1205676221 | 0.0181712761 |
+-------+---------------+---------------+
How could I force my scripts to always produce "flat" outputs as in the case of bc
OK different output-s are caused because of the NULL results from conditional function in bc_plist case. If I'd to modify code to replace possible NULL-s with NA-s I'd get 2d tables in any case.
So to keep things constant :
bc_nmds_plist<-apply(bc_nmds, 2, function(x) { apply(stoma_otud, 2, function(y) { if (cor.test(x,y,method="spearman", exact=FALSE)$p.value<0.05){cor.test(x,y,method="spearman", exact=FALSE)$p.value}else NA}) })
And I get a 2d tabel out for bc_nmds_plist too.
So I guess this thing can be called solved - as I now have a piece of code that produces predictable output on any correct input.
If anyone has any idea how to force the output to conform to previos bc_plist format instead I would still be interested as I do actually prefer that form:
$axis1
$axis1$Otu037
[1] 1.247717e-06
$axis1$Otu005
[1] 1.990313e-05
$axis1$Otu068
[1] 5.664597e-07
I want to render something like below in a view.
==================================================================
View: Car parts (Content type)
A | [B] | C | D | E | [F] | [G] | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
BONNET
FRONT BUMPER
FRONT BAR REINFORCEMENT
GUARD LEFT
GUARD RIGHT
==================================================================
How do you create the top A – Z index. The letters in [ ] are anchor links.
I think this discussion could help you:
https://drupal.org/node/403012
How can I find the symbolic inverse of a matrix in R; for example:
Matrix.test <- function(x) matrix(c(x, x^2, x^3, x^4, x^5, x^6, x^7, x^8, x^9, 2*x, 3*x, 4*x, 2*x^2, 3*x^3, 4*x^4, 5*x^5), 4, 4)
I Know there is a package called 'Ryacas' which is an interface to 'yacas', but I could not apply it to do such this computations.
'yacas' is a program for symbolic manipulation of mathematical expressions.
Please see link for more details.
thank you
It works fine for me:
> library(Ryacas)
> x <- Sym('x')
> M <- List(List(1,x),List(x,1))
> PrettyForm(M)
/ \
| ( 1 ) ( x ) |
| |
| ( x ) ( 1 ) |
\ /
> PrettyForm(Inverse(M))
/ \
| / 1 \ / -( x ) \ |
| | ------ | | ------ | |
| | 2 | | 2 | |
| \ 1 - x / \ 1 - x / |
| |
| / -( x ) \ / 1 \ |
| | ------ | | ------ | |
| | 2 | | 2 | |
| \ 1 - x / \ 1 - x / |
\ /
And following on:
M2 <- List(List( x, x^2, x^3, x^4),
List( x^5, x^6, x^7, x^8),
List( x^9, 2*x ,3*x , 4*x),
List(2*x^2, 3*x^3, 4*x^4, 5*x^5))
Inverse(M2)
The answer's pretty complicated, though (I could only be bothered to re-format the first four lines):
{{(x^6*3*x*5*x^5-x^6*4*x*4*x^4+x^8*2*x*4*x^4-x^7*2*x*5*x^5+
x^7*4*x*3*x^3-x^8*3*x*3*x^3)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-
x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+
x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-
x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),(x^4*3*x*3*x^3-x^4*2*x*4*x^4+x^3*2*x*5*x^5-x^3*4*x*3*x^3-x^2*3*x*5*x^5+x^2*4*x*4*x^4)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),0,(x^10*3*x-x^9*4*x-x^10*3*x+x^9*4*x-x^11*2*x+x^11*2*x)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2)},{(x^5*4*x*4*x^4-x^5*3*x*5*x^5-x^17*4*x^4+x^16*5*x^5-x^7*4*x*2*x^2+x^8*3*x*2*x^2)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),(3*x^2*5*x^5-4*x^2*4*x^4+x^13*4*x^4-x^4*3*x*2*x^2-x^12*5*x^5+x^3*4*x*2*x^2)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),0,(x^8*4*x-x^9*3*x+x^9*3*x-x^8*4*x)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2)},{(x^5*2*x*5*x^5-x^5*4*x*3*x^3+x^17*3*x^3-x^15*5*x^5+x^6*4*x*2*x^2-x^8*2*x*2*x^2)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),(4*x^2*3*x^3-2*x^2*5*x^5-x^13*3*x^3+x^11*5*x^5-x^2*4*x*2*x^2+x^4*2*x*2*x^2)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),(x^7*5*x^5-x^9*3*x^3+x^9*3*x^3-x^7*5*x^5)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),(x^9*2*x-x^7*4*x-x^9*2*x+x^7*4*x-x^19+x^19)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2)},{(x^5*3*x*3*x^3-x^5*2*x*4*x^4-x^16*3*x^3+x^15*4*x^4-x^6*3*x*2*x^2+x^7*2*x*2*x^2)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),(2*x^2*4*x^4-3*x^2*3*x^3+x^12*3*x^3-x^11*4*x^4+x^2*3*x*2*x^2-x^3*2*x*2*x^2)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),(x^8*3*x^3-x^7*4*x^4-x^8*3*x^3+x^7*4*x^4-x^9*2*x^2+x^9*2*x^2)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2),(x^7*3*x-x^8*2*x+x^8*2*x-x^7*3*x)/(x^7*3*x*5*x^5-x^7*4*x*4*x^4-x^8*2*x*5*x^5+x^8*4*x*3*x^3-x^9*3*x*3*x^3+x^9*3*x*3*x^3+x^8*2*x*5*x^5-x^8*4*x*3*x^3-x^7*3*x*5*x^5+x^7*4*x*4*x^4-x^19*4*x^4+x^19*4*x^4+x^18*5*x^5-x^9*4*x*2*x^2-x^18*5*x^5+x^9*4*x*2*x^2-x^11*2*x*2*x^2+x^11*2*x*2*x^2)}};