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LECASofts
ProcMod
Commits
1f246237
Commit
1f246237
authored
Jun 19, 2018
by
Eric Coissac
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DESCRIPTION
DESCRIPTION
+2
-0
NAMESPACE
NAMESPACE
+1
-0
anova.pm.R
R/anova.pm.R
+65
-0
mcov.R
R/mcov.R
+6
-10
mprocuste.R
R/mprocuste.R
+20
-128
ProcMod.Rmd
vignettes/ProcMod.Rmd
+36
-3
No files found.
DESCRIPTION
View file @
1f246237
...
...
@@ -20,4 +20,6 @@ Collate:
'omit.action.R'
'model.procmod.R'
'mprocuste.R'
'anova.pm.R'
'mcov.R'
'plot.pm.R'
NAMESPACE
View file @
1f246237
...
...
@@ -23,6 +23,7 @@ S3method(subset,procmod.frame)
export(as.procmod.frame)
export(is.procmod.frame)
export(mcor)
export(mcor.partial)
export(model.procmod.default)
export(mvar)
export(pm)
...
...
R/anova.pm.R
0 → 100644
View file @
1f246237
#' @include mprocuste.R
#' Anova function for `pm` objects.
#'
#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
#' @export
anova.pm
=
function
(
m
,
...
)
{
mf
=
m
$
model
coef
=
m
$
coefficients
sdxy
=
sqrt
(
diag
(
m
$
cov
))
mt
=
terms
(
mf
)
reponse
=
attr
(
mt
,
"response"
)
explicatives
=
attr
(
mt
,
'term.labels'
)
nXs
=
length
(
explicatives
)
coef.norm
=
coef
*
sdxy
[
explicatives
]
/
sdxy
[
reponse
]
cors
=
mcor
(
mf
)
print
(
reponse
)
print
(
explicatives
)
cors.Y
=
cors
[
reponse
,
explicatives
]
SCT
=
m
$
SCT
SCR
=
sum
(
m
$
residuals
^
2
)
SCE
=
SCT
*
coef.norm
*
cors.Y
names
(
SCE
)
=
explicatives
SCET
=
sum
(
SCE
)
SCIT
=
SCT
-
SCET
-
SCR
SumSq
=
c
(
SCE
,
Residuals
=
SCR
)
Df
=
c
(
rep
(
1
,
nXs
),
m
$
df.residual
)
MeanSq
=
SumSq
/
Df
Fvalue
=
c
(
MeanSq
[
-
length
(
MeanSq
)]
/
MeanSq
[
length
(
MeanSq
)],
NA
)
Pvalue
=
1
-
pf
(
Fvalue
,
Df
[
-
length
(
MeanSq
)],
Df
[
length
(
MeanSq
)])
result
=
data.frame
(
Df
,
SumSq
,
MeanSq
,
Fvalue
,
Pvalue
)
colnames
(
result
)
=
c
(
"Df"
,
"Sum Sq"
,
"Mean Sq"
,
"F value"
,
"Pr(>F)"
)
attr
(
result
,
"heading"
)
=
paste
(
"Analysis of Variance Table\n\nResponse:"
,
all.vars
(
m
$
terms
)[
1
]
)
class
(
result
)
=
c
(
"anova"
,
class
(
result
))
return
(
result
)
}
R/mcov.R
View file @
1f246237
...
...
@@ -24,32 +24,28 @@ NULL
mvar
=
function
(
...
)
{
Xs
<-
list
(
...
)
if
(
length
(
Xs
)
==
1
)
if
(
length
(
Xs
)
==
1
)
{
if
(
is.list
(
Xs
[[
1
]]))
Xs
=
as.procmod.frame
(
Xs
[[
1
]])
else
if
(
is.procmod.frame
(
Xs
[[
1
]]))
Xs
=
Xs
[[
1
]]
else
Xs
=
procmod.frame
(
Xs
[[
1
]])
else
Xs
=
procmod.frame
(
Xs
[[
1
]])
}
else
Xs
=
as.procmod.frame
(
Xs
)
Xnames
=
names
(
Xs
)
Xs
<-
lapply
(
Xs
,
scale
,
scale
=
FALSE
)
Xs
<-
as.procmod.frame
(
lapply
(
Xs
,
scale
,
scale
=
FALSE
)
)
nX
=
length
(
Xs
)
Xx
<-
rep
(
1
:
nX
,
nX
)
Xy
<-
rep
(
1
:
nX
,
rep
(
nX
,
nX
))
XXs
<-
mapply
(
function
(
x
,
y
)
crossprod
(
Xs
[[
x
]],
Xs
[[
y
]]
),
CovXXs
<-
mapply
(
function
(
x
,
y
)
sum
(
svd
(
crossprod
(
Xs
[[
x
]],
Xs
[[
y
]]))
$
d
),
Xx
,
Xy
,
SIMPLIFY
=
FALSE
)
sol_xxs
<-
lapply
(
XXs
,
svd
)
CovXXs
=
sapply
(
sol_xxs
,
function
(
sol
)
sum
(
sol
$
d
))
SIMPLIFY
=
TRUE
)
/
(
nrow
(
Xs
)
-1
)
dim
(
CovXXs
)
=
c
(
nX
,
nX
)
...
...
R/mprocuste.R
View file @
1f246237
...
...
@@ -8,92 +8,27 @@
NULL
#' Compute the variance, covariance matrix of K coordinate matrices.
#'
#' Covariance between two matrices is defined as the sum of the
#' sigular values of the X'Y matrix. All the matrices must have
#' the same number of rows.
#'
#' @param ... the set of matrices
#'
#' @examples
#' # Build Three matrices of 3 rows.
#' A <- matrix(1:9,nrow=3)
#' B <- matrix(10:15,nrow=3)
#' C <- matrix(20:31,nrow=3)
#' # compute the variance covariance matrix
#' mvar(A,B,C)
#' mvar(A=A,B=B,C=C)
#'
#' @author Eric Coissac & Christelle Gonindard-Melodelima
#' @export
mvar
=
function
(
...
)
{
Xs
<-
list
(
...
)
if
(
length
(
Xs
)
==
1
)
if
(
is.list
(
Xs
[[
1
]]))
Xs
=
as.procmod.frame
(
Xs
[[
1
]])
else
if
(
is.procmod.frame
(
Xs
[[
1
]]))
Xs
=
Xs
[[
1
]]
else
Xs
=
procmod.frame
(
Xs
[[
1
]])
else
Xs
=
as.procmod.frame
(
Xs
)
Xnames
=
names
(
Xs
)
Xs
<-
lapply
(
Xs
,
scale
,
scale
=
FALSE
)
nX
=
length
(
Xs
)
Xx
<-
rep
(
1
:
nX
,
nX
)
Xy
<-
rep
(
1
:
nX
,
rep
(
nX
,
nX
))
XXs
<-
mapply
(
function
(
x
,
y
)
crossprod
(
Xs
[[
x
]],
Xs
[[
y
]]),
Xx
,
Xy
,
SIMPLIFY
=
FALSE
)
sol_xxs
<-
lapply
(
XXs
,
svd
)
CovXXs
=
sapply
(
sol_xxs
,
function
(
sol
)
sum
(
sol
$
d
))
dim
(
CovXXs
)
=
c
(
nX
,
nX
)
colnames
(
CovXXs
)
=
Xnames
rownames
(
CovXXs
)
=
Xnames
return
(
CovXXs
)
}
#' Compute the person correlation matrix of K coordinate matrices
#'
#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
#' @export
mcor
=
function
(
...
)
{
cov
=
mvar
(
...
)
s
=
sqrt
(
diag
(
cov
))
vv
=
outer
(
s
,
s
)
return
(
cov
/
vv
)
}
pm.fit
=
function
(
covmat
,
y
,
xs
,
singular.ok
=
singular.ok
,
tol
)
{
xy.cov
=
covmat
[
xs
,
y
]
xy.cov
=
covmat
[
xs
,
y
,
drop
=
FALSE
]
xx.cov
=
covmat
[
xs
,
xs
,
drop
=
FALSE
]
xx.qr
=
qr
(
xx.cov
,
tol
=
tol
)
#
xx.qr = qr(xx.cov,tol=tol)
if
(
!
singular.ok
&&
xx.qr
$
rank
<
ncol
(
xx.cov
))
stop
(
"singular fit encountered"
)
#
if (!singular.ok && xx.qr$rank < ncol(xx.cov))
#
stop("singular fit encountered")
coef
=
qr.coef
(
xx.qr
,
xy.cov
)
coef
=
as.numeric
(
solve
(
xx.cov
)
%*%
xy.cov
)
names
(
coef
)
=
rownames
(
xx.cov
)
#coef = qr.coef(xx.qr,xy.cov)
#effects= qr.qty(xx.qr,xy.cov)
return
(
list
(
coefficients
=
coef
,
rank
=
xx.qr
$
rank
+
1
return
(
list
(
coefficients
=
coef
#rank=xx.qr$rank + 1
#qr=xx.qr,
)
)
}
...
...
@@ -167,7 +102,9 @@ pm = function (formula,data, subset, weights, na.action, method = "qr",
variances
=
diag
(
data.cov
)
std.dev
=
sqrt
(
variances
)
vars.norm
=
as.procmod.frame
(
mapply
(
function
(
x
)
scale
(
x
,
scale
=
FALSE
),
vars
))
vars.norm
=
as.procmod.frame
(
mapply
(
function
(
x
)
scale
(
x
,
scale
=
FALSE
),
vars
,
SIMPLIFY
=
FALSE
))
if
(
is.null
(
w
))
{
subset.w
=
rep
(
TRUE
,
nvars
)
...
...
@@ -188,6 +125,8 @@ pm = function (formula,data, subset, weights, na.action, method = "qr",
singular.ok
=
singular.ok
,
tol
=
tol
)
z
$
cov
=
data.cov
y.sd
=
std.dev
[
irep
]
xs.sd
=
std.dev
[
seq_len
(
nvars
)
!=
irep
]
...
...
@@ -195,7 +134,7 @@ pm = function (formula,data, subset, weights, na.action, method = "qr",
Y
=
vars.norm
[[
irep
]]
Xs
=
vars.norm
[
seq_len
(
nvars
)
!=
irep
]
Xs
=
vars.norm
[
seq_len
(
nvars
)
!=
irep
,
drop
=
FALSE
]
Ymean
<-
colMeans
(
Y
)
Xmeans
<-
lapply
(
Xs
,
colMeans
)
...
...
@@ -239,14 +178,14 @@ pm = function (formula,data, subset, weights, na.action, method = "qr",
z
$
residuals
=
Y
-
yhat
z
$
fitted.values
=
yhat
z
$
weights
=
w
z
$
df.residual
=
if
(
!
is.null
(
w
))
z
$
df.residual
=
(
if
(
!
is.null
(
w
))
sum
(
w
!=
0
)
else
nrow
(
Y
)
else
nrow
(
Y
))
-
nvars
+
1
if
(
ret.x
)
z
$
x
<-
vars.norm
[
-
irep
]
if
(
!
A
)
##
if (!A)
z
$
A
<-
A_xys
}
...
...
@@ -283,53 +222,6 @@ print.pm = function(m,...) {
}
#' Anova function for `pm` objects.
#'
#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
#' @export
anova.pm
=
function
(
m
,
...
)
{
VarY
=
m
$
SCT
SCE
=
sum
(
m
$
residuals
^
2
)
VarYhats
=
VarY
*
m
$
varpart
names
(
VarYhats
)
=
names
(
m
$
coefficients
)
SChat
=
sum
(
VarYhats
)
if
(
length
(
Xs
)
>
1
)
{
SCI
=
VarY
-
SCE
-
sum
(
SChat
)
SumSq
=
c
(
VarYhats
,
Interactions
=
SCI
,
Residuals
=
SCE
)
Df
=
c
(
rep
(
1
,
length
(
VarYhats
)
+1
),
m
$
df.residual
)
}
else
{
SumSq
=
c
(
VarYhats
,
Residuals
=
SCE
)
Df
=
c
(
1
,
m
$
df.residual
)
}
MeanSq
=
SumSq
/
Df
Fvalue
=
c
(
MeanSq
[
-
length
(
MeanSq
)]
/
MeanSq
[
length
(
MeanSq
)],
NA
)
Pvalue
=
1
-
pf
(
Fvalue
,
Df
[
-
length
(
MeanSq
)],
Df
[
length
(
MeanSq
)])
result
=
data.frame
(
Df
,
SumSq
,
MeanSq
,
Fvalue
,
Pvalue
)
colnames
(
result
)
=
c
(
"Df"
,
"Sum Sq"
,
"Mean Sq"
,
"F value"
,
"Pr(>F)"
)
attr
(
result
,
"heading"
)
=
paste
(
"Analysis of Variance Table\n\nResponse:"
,
all.vars
(
m
$
terms
)[
1
]
)
class
(
result
)
=
c
(
"anova"
,
class
(
result
))
return
(
result
)
}
summary.pm
=
function
(
object
,
correlation
=
FALSE
,
...
...
vignettes/ProcMod.Rmd
View file @
1f246237
...
...
@@ -512,7 +512,7 @@ knitr::kable(cors)
#### Building the multiprocruste model
```{r}
euk.pm = pm(euk ~ soil + climat + geo + hist,data=data)
euk.pm = pm(euk ~ soil + climat + geo + hist
,data=data)
euk.pm
```
...
...
@@ -525,7 +525,7 @@ plot(euk.pm)
```{r}
W=1/rowSums(euk.pm$residuals^2)
W=W/max(W)
euk.pm.w = pm(euk ~ soil + climat + geo + hist,data=data,weights = W)
euk.pm.w = pm(euk ~ soil + climat + geo + hist
,data=data,weights = W)
euk.pm.w
```
...
...
@@ -580,7 +580,7 @@ partition
```
```{r}
soil.pm = pm(
hist ~ bac + euk + climat + geo + soil
,data=data)
soil.pm = pm(
soil ~ bac + euk + climat + geo
,data=data)
soil.pm
```
...
...
@@ -591,6 +591,7 @@ plot(soil.pm)
```{r}
soil.anova = anova(soil.pm)
soil.anova
soil.anova$`Sum Sq`/sum(soil.anova$`Sum Sq`)
```
...
...
@@ -602,3 +603,35 @@ pm(eukariote ~ bacterie)
cor(bac.dist,euk.dist)
```
```{r}
X1 = rnorm(10,0,2)
X2 = rnorm(10,0,2)
X3 = rnorm(10,0,2)
if (cor(X1,X2)< 0)
X2=-X2
if (cor(X1,X3)< 0)
X3=-X3
X1C = scale(X1,scale = TRUE)
X2C = scale(X2,scale = TRUE)
X3C = scale(X3,scale = TRUE)
Y = 1 *X1C+ 2 *X2C + 3 * X3C + rnorm(10)
YC = scale(Y,scale = TRUE)
XXXX = procmod.frame(YC,X1C,X2C,X3C)
L = lm(YC~X1C+X2C+X3C,data = XXXX)
P = pm(YC~X1C+X2C+X3C,data = XXXX)
mcor(XXXX)
cor(do.call(data.frame,XXXX))
anova(L)
anova(P)
L
P
```
```{r}
y = c(4,2,3,4,5,5,7,5,4,9,7,6)
x1= c(1,1,1,1,1,1,2,2,2,2,2,2)
x2= c(8,7,7,9,5,4,3,6,7,2,3,2)
scherrer = procmod.frame(y,x1,x2)
```
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